Leading up to the recent decline, market breadth (measuring how many stocks participate in a market move) had weakened. While a handful of dominant stocks masked the weakness, the underlying market was thinning out. Such dispersion [1] [2] [3], where some stocks surge while others lag, can create an illusion of stability in some market environments.
At the same time, liquidity—cash and credit availability—steadily drained from the system. Mechanisms like the reverse repo facility (where banks park excess cash with the Federal Reserve), the Treasury General Account (the government’s cash balance), and money market flows help offset [1] shortfalls. However, this time, they offered little cushion.
New policies—such as tariffs and trade restrictions—reinforce market trends and drive investors toward safer assets like bonds. There is a growing preference for lower bond yields over short-term stock market gains.
While the Federal Reserve controls short-term interest rates, long-term rates are more influenced by broader factors such as inflation expectations, economic growth, and investor sentiment.
Although lower long-term rates can support risk assets, their more immediate and significant impact is on the broader economy. Lowering them reduces borrowing costs for homeowners and businesses, encouraging investment and consumption. Additionally, lowering these yields helps with servicing government debt burdens and improving fiscal stability.
The shifts are intentional. Policymakers are transitioning the economy from dependence on government stimulus, but this adjustment comes with growing pains. Policy narratives and actions may weaken markets and slow economic activity in the short term. One reason receiving attention is the wealth effect—wealthier households, who drive a significant share of consumer spending, tend to spend more when stocks rise. Conversely, market drops can curb this effect and feed an economic slowdown.
Graphic: Retrieved from Bloomberg via @amitisinvesting.
Positioning Context: Setting Up For A Rip
History doesn’t repeat, but it often rhymes. Today’s setup echoes late summer 2024, albeit without the sharp volatility repricing. The difference? This time, investors were prepared, with hedges to act as insurance against market turmoil. The selling has been orderly, creating an illusion of stability and sustaining optimism.
Graphic: Retrieved from JPMorgan Chase via @Marlin_Capital.
This ongoing decline began in mid-February, coinciding with the unwinding of significant amounts of call options—contracts to buy stocks at a set price. This added indirect pressure on the market through hedging-related flows.
SpotGamma expresses this view, highlighting that the February expiration was “call-weighted” due to strong stock performance leading up to it. This increased the likelihood of a pullback, as call sellers unwound their long stock hedges—a simplified explanation, as other offsetting positions may also be in play.
Graphic: Retrieved from SpotGamma.
At the same time, after market shocks in August and December 2024, investors focused more on guarding against sudden volatility spikes rather than hedging against a broader market downturn. This pattern is familiar—the S&P 500 and the Cboe Volatility Index (VIX), which measures expected market volatility, sometimes rise together ahead of market peaks.
Meanwhile, within market supply dynamics, this activity has effectively set a floor under VIX pricing, as reflected in the VVIX trending higher since the volatility of late last summer.
Graphic: Retrieved from TradingView.
The result? Despite preparations for increased volatility, it hasn’t materialized, frustrating hedge holders and making it harder to identify a market bottom typically marked by extreme volatility spikes. Even with a backwardated implied volatility term structure (where short-term volatility is priced higher than longer-term volatility), anxiety and market movements remain out of sync.
Graphic: Retrieved from TradingView. 1-month VIX less 3-month VIX.
Over time, some traders might shift to longer-dated options, while others might drop their hedges altogether, which could amplify volatility-selling behavior. Ironically, this could create the conditions for shocks they were trying to hedge against.
Given this environment, 2022’s playbook becomes relevant. Back then, investors—rattled by the COVID crash—were prepared, monetizing hedges into declines and keeping a lid on volatility. We may see parallels now. After last week’s economic data, hedgers have been supplying volatility back to the market, offering brief relief as we potentially enter a seasonally stronger period.
Graphic: Retrieved from SpotGamma.
The main takeaway? Current positioning dynamics indicate that investors have effectively managed and responded to the downside. While markets will be volatile, significant shocks may be delayed or avoided.
Graphic: Retrieved from SpotGamma and for illustrative purposes only. SPX prices X-axis. Option delta Y-axis. When the factors of implied volatility (Vanna) and time change (Charm), hedging ratios change. If investors hedge by selling stock to offset long put options, falling implied volatility (as seen in the skew chart above) leads them to buy back the stock, which can support markets.
Context Applied: Trade Structuring
We adapted previously shared structuring guides. Given volatility’s failure to perform, we opted for downside ratios and flies. This worked, and we plan on developing some case studies.
A potential cyclical rebound within a broader period of weakness could be expressed via low-cost positive-delta (bullish) structures, including buying calls while proportionately hedging with stocks or futures, where potential gains from the calls can outweigh hedge-related losses. Additionally, as we prefer, one can deploy verticals and flies, buying options closer to the current market prices while selling more options further out (with an extra far-out option bought to reduce margin requirements if needed).
We and others agree that the Nasdaq 100 (NDX) and higher beta stocks are appealing. For one, relative strength pockets emerge in the NDX versus the SPX, potentially attributable to tariffs disproportionately impacting non-tech sectors. Checking options skews, and NDX options farther away in price may be underpriced for the eventually realized volatility.
Graphic: Retrieved from Bloomberg via Nicholas Smith.
For more on structuring across different products, be they gold or Bitcoin, see our Mar-a-LagoAccords letter published last month.
Disclaimer
By viewing our content, you agree to be bound by the terms and conditions outlined in this disclaimer. Consume our content only if you agree to the terms and conditions below.
Physik Invest is not registered with the US Securities and Exchange Commission or any other securities regulatory authority. Our content is for informational purposes only and should not be considered investment advice or a recommendation to buy or sell any security or other investment. The information provided is not tailored to your financial situation or investment objectives.
We do not guarantee the accuracy, completeness, or timeliness of any information. Please do not rely solely on our content to make investment decisions or undertake any investment strategy. Trading is risky, and investors can lose all or more than their initial investment. Hypothetical performance results have limitations and may not reflect actual trading results. Other factors related to the markets and specific trading programs can adversely affect actual trading results. We recommend seeking independent financial advice from a licensed professional before making investment decisions.
We don’t make any claims, representations, or warranties about the accuracy, completeness, timeliness, or reliability of any information we provide. We are not liable for any loss or damage caused by reliance on any information we provide. We are not liable for direct, indirect, incidental, consequential, or damages from the information provided. We do not have a professional relationship with you and are not your financial advisor. We do not provide personalized investment advice.
Our content is provided without warranties, is the property of our company, and is protected by copyright and other intellectual property laws. You may not be able to reproduce, distribute, or use any content provided through our services without our prior written consent. Please email renato@physikinvest for consent.
We reserve the right to modify these terms and conditions at any time. Following any such modification, your continued consumption of our content means you accept the modified terms. This disclaimer is governed by the laws of the jurisdiction in which our company is located.
This edition shouts out Public.com, a multi-asset investing platform built for those who take investing seriously. Public recently launched Alpha, an AI investment exploration tool, in the app store. We’re excited to host co-founder and co-CEO Jannick Malling on the next podcast to discuss the market and how AI levels the playing field. Stay tuned!
When market expectations drift too far from underlying fundamentals, they eventually become unsustainable. This sometimes leads to corrections that can trigger cascading effects across the broader market.
It is prevailing investment practices that partly fuel such a dynamic. While concepts like diversification and efficient markets appear sound, they often fail to account for the pressures investors face in practice. For instance, sophisticated retail investors have no mandate and typically have the space to make deliberate, calculated decisions. On the other hand, institutional-type investors, driven by the need to deliver consistent short-term profits, may feel compelled to chase returns. This pressure can lead to riskier behaviors, such as betting on low volatility by selling options. While this may produce steady returns in calm markets, it exposes investors to sudden shocks, volatility repricings, and forced unwinds when markets turn unexpectedly. Investors are often unprepared for such volatility, seldom owning options outright due to the rarity of shocks. This creates a market landscape skewed toward a “winner-takes-all” outcome, where only a few are positioned to benefit from such rare moments.
The following sections explore this realm of increasingly frequent, dramatic, and unpredictable outcomes. Let’s dive in.
In our excruciatingly detailed ‘Reality is Path-Dependent’ newsletter, we explored how markets are shaped by reflexivity (feedback loops) and path dependency (how past events influence the present), setting the stage for August 2024’s turbulence and recovery.
To recap, we noticed that while individual stocks experienced big price swings, the broader indexes, like the S&P 500—representing those stocks—showed restraint. Remarkably, the S&P 500 went over 350 sessions without a single 2% or more significant move lower, reflecting this calm. This happened because of a mix of factors, including many investors focusing on broader market calm, often expressed by selling options and, in some cases, using their profits to double down on directional bets in high-flying stocks. This helped create a gap between the calmer movements in the indexes and wilder swings in individual stock components, leading to falling correlations; beneath the surface, big tech, AI, and Mag-7 stocks gained ground, while smaller stocks in the index struggled, as shown by fewer stocks driving the market higher (weaker breadth).
By arbitrage constraints, declining correlation is the reconciliation. When investors sell options on an index, the firms on the other side of the trade—like dealers or market makers—dynamically hedge their risk. They may do this by buying the index as its price drops and selling it when it rises, which can help keep the index within a narrower range and reduce actual realized volatility. However, this doesn’t apply as much to individual stocks, where we observed more options buying. For these stocks, hedging works differently: dealers may buy when prices rise and sell when prices fall, reinforcing trends and extending price moves. This creates a situation where the index stays relatively calm, but its components can swing more wildly.
Anyway, we noticed that as the connection between the index and its stocks was weakening, traders who bet on these differences (called dispersion) profited. As more participated in this and other volatility-suppressing trades, it became more successful. This shows how feedback loops (reflexivity) and past events (path dependency) influence future market behavior. Overall, this trade helped sustain the market rally and added stability as lesser-weighted stocks stepped up to offset the slowdown in leaders in July.
However, we speculated about the risks of a broader “sell-everything” market. Waning enthusiasm for big tech stocks and broader market selling on the news could manifest demand for protection (such as buying longer-dated put options). During the quieter, less liquid summer months, this could trigger increased volatility and lead to a sharp sell-off (as dealers or market makers hedge in the same direction the market’s moving, amplifying moves). Although low and stable volatility gave an optimistic market outlook, we considered advanced structures to hedge against potential pullbacks at low cost, including ultra-wide, broken-wing NDX put butterflies, ratio spreads, and low-cost VIX calls and call spreads (which, by way of the VIX being an indirect measure of volatility or volatility squared, offer amplified protection in a crash). In the event of market weakness, these structures would be closed/monetized, with the proceeds/profits used to lower the cost of upside participating trades through year-end. Again, further details can be found in the ‘Reality is Path-Dependent’ newsletter.
Graphic: Retrieved from UBS. Hedge funds were cutting risk in July 2024.
Our warnings about the risks of extreme momentum crowding and positioning leading to violent unwinds were borne out in August 2024. Markets reeled as recession probabilities were repriced, quarterly earnings disappointed, and central bank policies diverged. The Federal Reserve’s dovish stance starkly contrasted with an unanticipated rate hike by the Bank of Japan. This fueled considerable volatility across assets, particularly higher-beta equities and cryptocurrencies, which are more heavily influenced by traditional risk and monetary policy factors. The episode highlighted the vulnerabilities of a market reliant on leveraged trading and concentrated investments; the situation was about more than just a fundamental shock.
The unraveling was marked by spikes in stock market volatility measures like the VIX, a liquidity vacuum, and forced deleveraging by trend-following and volatility-sensitive strategies. Despite this clearing some froth, key equity and volatility positioning and valuation vulnerabilities remained, leaving markets fragile and uncertain whether growth will stabilize or deteriorate.
Graphic: Retrieved from Bloomberg via PPGMacro. Yen versus Nasdaq.
Some accounts compared the selling to the 1987 stock market crash. Volatility broke its calm streak, with spot-vol beta—the relationship between market movements and expected/implied volatility changes—rising and correlations increasing.
Graphic: Retrieved from Morgan Stanley via @NoelConvex.
Early warning signs of precariousness emerged as prices for far out-of-the-money SPX and VIX options—key indicators and drivers of potential crashes when heavily traded—soared hundreds of percent the week before crash day, Monday, August 5. These tail-risk hedges, often viewed as insurance against steep market drops, carried well, becoming significantly more expensive as demand surged. Just as insurers raise premiums on homes in disaster-prone areas to account for higher risk, the soaring cost of these options reflected the market’s growing fear of extreme outcomes. This repricing fed into broader quantitative measures, triggering a wave of deleveraging and prompting investors to offload hundreds of billions in stock bets, amplifying the sell-off.
At one point, the VIX breached 65, its highest level since 2020. A lack of liquidity during pre-market hours and the shift from short-term to longer-term hedges contributed to this sharp rise. The VIX is calculated based on a selection of S&P 500 options about 30 days out, chosen by an algorithm that looks at the middle point between the prices people are willing to buy and sell those options. When there’s not a lot of trading activity and markets get volatile, the difference between the buying (bid) and selling (ask) prices widens, lending to the VIX being higher than it should be.
Graphic: Retrieved from JPMorgan via @jaredhstocks.
Comparatively, VIX futures—perhaps a better measure of hedging demands outside regular market hours—lagged. JPMorgan claims the fast narrowing in the VIX spot and futures indicates the VIX spot overstated fear and hedging demand.
Moreover, a technical issue at the Cboe options exchange delayed trading, and by the time the problem was resolved, the VIX had already dropped sharply. This coincided with traders doubling down on short-volatility positions and buying stocks, confident in the S&P 500’s historical tendency to rebound in the months following similar volatility spikes.
Graphic: Retrieved from Nomura via The Market Ear.
Rocky Fishman, founder of Asym 500, explains that the dislocations above were compounded by dispersion traders who likely experienced mark-to-market losses on their short index positions while single-stock markets remained closed. This forced some to cover their short index volatility positions, resulting in a pre-market surge in index volatility. Once trading resumed, many began selling single-stock options, triggering a broader decline in volatility levels—particularly in single-stock options.
So, the rapid decrease in the VIX was driven more by positioning dynamics and the calculation mechanics of the VIX itself rather than a complete unwinding of risky trades. Additionally, the S&P 500’s movement into lower-volatility segments of the SPX options curve, which the VIX relies on, further intensified this decline. Kris Sidial of The Ambrus Group adds, “It’s quite evident that many have doubled down on [short volatility]. But you don’t need to trust our data. Barring any additional volatility shocks in the next few weeks, I expect some of these firms to deliver stellar numbers by the end of Q3 due to their inclination to take on more risk.”
Graphic: Retrieved from Bloomberg via @iv_technicals.
The market’s recovery in the fall was mainly driven by the Mag-7 giants, whose robust performance overshadowed the struggles of smaller stocks. The August decline created an opportunity to acquire beaten-down stocks at discounts, with investors indeed seeing the panic as a buy signal; outside of significant crises unable to topple the economy (like the bank failures in 2023), back-tests suggest that when the VIX exceeds 35, the S&P 500 has historically risen upwards of 15% over the next six months.
The recovery was not without risks, with the divide between market leaders and laggards highlighting continued fragility. In any case, supportive flows into mega-caps and dealer hedging activities helped stabilize broader indexes through November.
The growing gap between the stable performance of the S&P 500 and the larger fluctuations in its components created profits for those dispersion traders we discussed. However, as valuations for mega-cap stocks climb, the market becomes more vulnerable to shifts in sentiment or capital flows. Events like the yen carry trade—where borrowing in Japan funded investments in U.S. Treasuries and equities—unwind exposed concentration risks and positioning imbalances, which could amplify future shocks.
Graphic: Retrieved from Bloomberg via @Alpha_Ex_LLC.
As for potential triggers and shocks going forward, rising inequality and populism are creating deep divisions within and among major powers, while protectionist policies strain potential global cooperation. According to Cem Karsan of Kai Volatility, these dynamics drive economic battles and indirect conflicts, with Eastern nations working to reduce Western influence. This shift coincides with a new era of high inflation, widening wealth gaps, and changing power dynamics. Millennials, now a dominant force in the workforce and politics, are challenging decades of policies that primarily benefited corporations and the wealthy, reversing globalization and redistributing wealth—though this comes at the cost of heightened inflation.
These structural changes disrupt traditional investment strategies like the 60/40 portfolio. A major geopolitical event, such as China moving on Taiwan, could severely impact supply chains, critical industries, and the global economy, with significant repercussions for stocks like Nvidia and broader indices like the S&P 500. If market bets against panic (like short volatility) unravel, it could trigger more swings like August’s. The same reflexivity that has stabilized markets since then could amplify volatility during future shocks, turning successive disruptions into severe crises if market positioning is misaligned.
Despite this challenging backdrop, short-term market behavior operates independently, dictated by supply and demand dynamics. Seasonal flows, particularly during year-end, created a bullish bias; reduced holiday trading volumes, combined with reinvestment effects and significant options expirations, contributed to structural upward pressure on markets. These flows and a historical tendency for election years to drive positive performance suggested a right-skewed distribution for near-term outcomes.
The prospect and fulfillment of a “red sweep,” characterized by follow-on deregulation, a business-friendly environment, and more animal spirits, boosted markets. However, caution was spotted in certain areas, like bonds, where expectations for inflation rose.
Ultimately, the market overextended, highlighting the risk of a peak as it caught down to weak breath on the Federal Reserve’s surprising hawkish shift in December. This change led to volatility in equities, interest rates, and currencies, reminiscent of the spike in August when the VIX jumped and surpassed the S&P 500’s decline. Such persistent divergences validate a clear shift into a new market regime characterized by volatility that consistently outpaces market sell-offs.
Graphic: Retrieved from Nomura.
In a report, Cboe said that equity spot/vol beta surged to -3.3, meaning for every 1% drop in the S&P 500, the VIX gained 3.3 points—exceeding even August’s extreme levels. SPX options priced greater downside risk, with skew steepening. Notwithstanding, correlations settled near historic lows, signaling investor focus on sector rotation and stock dispersion.
Graphic: Retrieved from Bloomberg via Alpha Exchange.
Early warning signals appeared when volatility and equities increased simultaneously, highlighting a “spot up, vol up” pattern that frequently foreshadows market peaks. For instance, at one moment, upside calls on major stocks like Nvidia and the S&P 500 were well-priced and poised to perform strongly in a rally. This occurs because, during rallies, implied volatility of call options generally decreases as investors tend to sell calls tied to their stock holdings rather than liquidating them entirely. When investors chase synthetic upside exposure through call options, indices like the VIX could stabilize or increase as the market rises. Since counterparties typically adjust their exposure by buying the underlying asset, it propels the rally and magnifies market fluctuations.
Beyond the chase, the post-election rally got an extra boost from unwinding protective puts. Significant events like elections typically boost demand for puts as hedges against adverse outcomes, with counterparties hedging these positions by selling underlying stocks or futures, among other things. As markets rise, time passes, or uncertainty fades, these puts lose value, leading counterparties to unwind hedges by buying stocks and futures. This is a structural support that pushes markets higher.
Corporate buybacks and stabilizing volatility levels encouraged funds to increase their exposure. Nomura estimated that assuming stable markets, up to $145 billion in additional volatility-sensitive buying could occur over three months. Although 30-day implied volatility traded a bit above realized volatility, this signaled uncertainty rather than distress. Seasonal factors mentioned in the previous section—like low holiday liquidity and limited selling pressure—added to the upward trend.
Then came the FOMC meeting, followed by December’s massive options expiration (OPEX), disrupting the supportive dynamics that had fueled the rally. While a rate cut was expected, uncertainty around forward guidance introduced volatility just as the market faced a substantial unwinding of stabilizing exposure. Those who hedged customer-owned call options by buying stock during rallies and hedged customer-owned puts by selling stock during declines were forced to re-hedge as markets turned lower following the FOMC meeting. This involved selling stocks and futures, adding downside pressure.
Macro factors triggered the initial downside, with positioning amplifying equity volatility.
Ultimately, volatility levels signaled oversold conditions ahead of a massive put-clearing OPEX, setting the stage for a year-end lift. The volatility spikes in August and December remained contained, as they were largely event-driven and mitigated by existing hedges and a market structure anchored by year-end flows. The subsequent unwinding of significant options positions in December eased the pressure, while reinvestment and re-leveraging effects into January supported against weak breadth; as the earlier-mentioned Cem Karsan explains best, the substantial gains over the year increased collateral for leveraged investors, enabling them to reinvest profits or take on more leverage, which has given markets a lease on life through today.
2025 might see increased volatility, not driven by typical inflation or recession fears but by the positioning dynamics herein that can magnify market swings during downturns. The so-called “red sweep” introduces optimism and the likelihood of greater risk-taking, which could result in one-sided positioning and heightened volatility. Factors like populism, protectionism, and rising interest rates are additional pressures on stocks and bonds. Gold and Bitcoin are identified as potential stores of value, but Bitcoin remains prone to speculation, liquidity challenges, and regulatory obstacles.
The following newsletters will identify structures to lean into fundamental catalysts and underlying volatility contexts. Notably, the structures discussed earlier (such as ultra-wide, broken-wing NDX put butterflies, ratio spreads, and low-cost VIX calls and call spreads) may continue to perform as effective hedges.
By viewing our content, you agree to be bound by the terms and conditions outlined in this disclaimer. Consume our content only if you agree to the terms and conditions below.
Physik Invest is not registered with the US Securities and Exchange Commission or any other securities regulatory authority. Our content is for informational purposes only and should not be considered investment advice or a recommendation to buy or sell any security or other investment. The information provided is not tailored to your financial situation or investment objectives.
We do not guarantee the accuracy, completeness, or timeliness of any information. Please do not rely solely on our content to make investment decisions or undertake any investment strategy. Trading is risky, and investors can lose all or more than their initial investment. Hypothetical performance results have limitations and may not reflect actual trading results. Other factors related to the markets and specific trading programs can adversely affect actual trading results. We recommend seeking independent financial advice from a licensed professional before making investment decisions.
We don’t make any claims, representations, or warranties about the accuracy, completeness, timeliness, or reliability of any information we provide. We are not liable for any loss or damage caused by reliance on any information we provide. We are not liable for direct, indirect, incidental, consequential, or damages from the information provided. We do not have a professional relationship with you and are not your financial advisor. We do not provide personalized investment advice.
Our content is provided without warranties, is the property of our company, and is protected by copyright and other intellectual property laws. You may not be able to reproduce, distribute, or use any content provided through our services without our prior written consent. Please email renato@physikinvest for consent.
We reserve the right to modify these terms and conditions at any time. Following any such modification, your continued consumption of our content means you accept the modified terms. This disclaimer is governed by the laws of the jurisdiction in which our company is located.
Last month, we had the privilege of attending the Milken Institute’s Asia Summit in Singapore, often seen as the West’s gateway to Asia. Prominent figures, including Bridgewater Associates Founder and CIO mentor Ray Dalio, shared insights on navigating a rapidly transforming, multipolar world. Dalio focused on the major forces shaping global conditions—such as debt cycles, political instability, great power conflicts, climate change, and technology—and highlighted where investment opportunities lie. While the U.S. market may be priced to perfection, Dalio pointed to regions like China and other parts of Asia as offering greater potential.
Fresh from Singapore, we sat down with Andy Constan, Founder, CEO, and CIO of Damped Spring Advisors, whom you may recognize from his appearances on CNBC or Twitter/X. Constan’s background is rooted in extracting value through “relative value” trades, but since the Global Financial Crisis and his time at Bridgewater Associates working alongside Ray Dalio, he’s shifted his focus to macroeconomic factors. In this discussion, we explore his experience building Bridgewater’s volatility pillar, the vulnerability of traditional alpha strategies during macro crises, the bull market for metals, stock market expectations, and more.
As you may have noticed, there’s a progression in our podcast episodes. In the first, Mat Cashman, a former market maker, broke down what options are and how they’re traded. In the second, Vuk Vukovic, founder of an upstart hedge fund, discussed idea generation and using options as tools to express those ideas. Now, in our third episode, Constan dives into how options fit into a balanced portfolio. The key takeaway? While options can enhance portfolios, most investors don’t need leveraged exposure to markets. A balanced portfolio in 2025 can remain straightforward, and here’s an expert telling you just that.
The video can be accessed at this link and below. An edited transcript follows.
I recently attended the Milken Institute event in Singapore, where Ray Dalio was a keynote speaker. Since you worked alongside Ray at Bridgewater, I thought it would be interesting to hear your perspective. Some key themes he discussed included multipolarity, deglobalization, internal disorder, elections, and the fact that a few companies drive much of the S&P 500 Index’s performance. Could you start by sharing a bit about your time at Bridgewater? What was your role, and how may those themes and what you learned there shape your portfolio today?
Before joining Bridgewater Associates as a senior research team member, I ran a hedge fund, focusing heavily on equity relative value, volatility, capital structure arbitrage, risk arbitrage, long-short strategies, and statistical arbitrage. Through my hedge fund experience, I looked at volatility across different asset classes—rates, equity, currency, and commodities. By the time I joined Bridgewater, I had accumulated 23 years of experience, including 18 years at Salomon Brothers, where I was involved in market-making and prop trading, and five years running my hedge fund.
When I joined in 2010, the idea was to see if I could contribute to Bridgewater’s investment process in areas they hadn’t previously explored. I created the volatility pillar within their idea generation team, working closely with Ray Dalio, Greg Jensen, Bob Prince, who were the three CIOs at the time, and several talented young individuals, including Karen Karniol-Tambour, now the Co-CIO, and Bob Elliott, now a well-known figure on Twitter/X who was always excellent at asking probing questions.
This role exposed me to macro factors I hadn’t previously focused on. I noticed that traditional alpha strategies often blew up during macroeconomic crises, convincing me that many of them—like long-short equity, leveraged derivatives, and convertible bond arbitrage—were vulnerable to the same risks. The Global Financial Crisis clearly illustrated how macro factors, along with central bank actions like quantitative easing and tightening or lowering and raising interest rates, influence monetary conditions and the availability of leverage; when financial conditions tighten, seemingly uncorrelated alpha strategies unravel.
Bridgewater’s focus is on directionally trading the most liquid assets globally. Before my time there, they primarily traded futures and cash securities, with little exposure to options or derivatives. So, my role was to explore whether the volatility market could offer insights to enhance their directional trading or even serve as a new asset class responding to their existing macro indicators.
Graphic: Retrieved from Renato Leonard Capelj, founder at Physik Invest.
Does Bridgewater still have this volatility pillar?
While my connections at Bridgewater remain strong, we don’t discuss business. Like most hedge funds, their work happens behind closed doors. In any case, I don’t believe they’re involved in those markets, as they’re typically too small for their size; instead, it is more likely they use some of the strategies I helped develop—focused on volatility, credit markets, and other convex assets—to refine their directional views on traditional, highly liquid macro assets.
Were there any trades—or even just ones you were eager to pursue—that Bridgewater decided not to go after?
Three days after I joined, the Flash Crash occurred. The market was already on edge, particularly with European turmoil. Earlier that spring, the Greek debt market had been rocked by significantly higher deficit expectations, sparking the European debt crisis just ahead of the Flash Crash. When the crash happened, it cemented for many investors that a more volatile post-GFC regime would persist for years.
A persistent demand for long-term equity volatility has run over many funds and investors throughout my career. This demand primarily comes from insurance companies, which can’t sell traditional investment management products but want to, as their clients are the same retail investors who may purchase money management services for their 401(k)s or pensions. Essentially, the clients have savings they want to invest, and the insurance companies have life insurance policies—like Term Life—that historically acted as fixed-income securities. You get a guaranteed death benefit, and your policy accrues value based on interest rates.
With interest rates incredibly low then, insurance companies in the mid-1990s began creating securities that offered guaranteed death benefits with upside exposure to equities. They bought equity portfolios, added interest rate swaps, and purchased puts on the S&P 500, creating a bond with a call option on equities. This enabled clients to receive a guaranteed death benefit with potential equity performance upside. Accordingly, the aggressive demand for these products pushed up long-term volatility, as these were 10- to 20-year death benefit products, and long-term call options became highly sought. This affected the dividend market—dealers who sold these calls became exposed to dividends.
Initially, Swiss banks like UBS O’Connor and First Boston and some French banks supplied the calls. However, by the mid-to-late ’90s, the demand overwhelmed them as markets grew more volatile, mainly due to the increasing tech concentration in the index. Long-Term Capital Management (LTCM) stepped in, selling global index volatility for five years. This did not end well, and after LTCM was unwound, long-term volatility remained well-bid as insurance companies continued buying these structures and selling them to clients. Warren Buffett eventually stepped in during the GFC, selling $9 billion notional in five- to ten-year S&P puts. He saw it as a good bet, figuring that buying stocks at $700 in ten years after collecting premiums was favorable. Uniquely, he wasn’t required to post any collateral—a situation unlikely ever to repeat. However, Buffett eventually unwound this position as the market rallied following the GFC lows around the Flash Crash.
With Buffett out of the game, no willing sellers of long-term volatility existed. The banks and LTCM had been burned, and even though Buffett avoided getting burned, his exposure to Vega (i.e., the impact of volatility on an option’s price) still cost him.
At one point, we saw 10-year implied volatility reach 38%. I spent weeks crafting a case for Bridgewater, supported by data, evaluating the size and forward demand of the insurance market and potential players who could self-insure. We analyzed whether selling 38 implied volatility was a good trade and gathered historical data from every stock market, from 1780s UK to post-Soviet Russia, to assess risk. As it turns out, selling a 38 implied volatility would have been profitable in most cases. The only exceptions were Germany, Italy, and Japan, where WWII drove realized volatility above 38. Never before in the US, UK, or elsewhere had there been sustained realized 38 volatility.
Confident in my findings, I presented this trade idea to Bridgewater, but we ultimately didn’t execute it. The following year, realized volatility dropped below 20, and implied volatility fell by 12-13 points. Had Bridgewater made the trade, it could have likely netted $1 billion in the first year and over $20 billion over the decade.
Did that, in terms of how they made decisions and portfolios guide how you think about making decisions today?
Yes. Bob Prince pulled me aside during the process and said, “We like what you’ve done, but we need you to think differently.”
At Bridgewater, the way they want you to think makes perfect sense. If you’re serious about having a long-term investment process, you need something you can use consistently, day in and day out. You’re not just looking to trade—you want an alpha stream that endures. That’s the real asset. Once a trade is done, if it can’t be repeated, all the effort is wasted. Bridgewater’s focus—and anyone involved in systematic trading should—was discovering long-term alpha streams.
The biggest constraint, both at Bridgewater and everywhere, is time. You have to be selective about where you invest it. For CIOs, learning to trade options proficiently would have been a massive time drain and likely hurt their performance in building a sustainable, long-term alpha-generating engine, which already demanded their full attention.
So that’s the key—what is your time worth? I believe they made the right decision. Investment researchers should focus on creating lasting alpha, not short-term trades.
What did your early work at Solomon Brothers—being on the Brady Commission following the 1987 stock market crash—teach you about the interplay between participants and how this affects liquidity and market outcomes?
At 23, I was fortunate to be assigned to the Brady Commission. What set me apart was a relatively ordinary skill for my generation: I was particularly good at working with spreadsheets. This put me at the table with five senior investment professionals from Morgan Stanley, Goldman Sachs, Lehman Brothers, JPMorgan, and the head of research at Tudor, who had made a fortune during the crash. I analyzed actual trades with the names of brokers and end clients—tracking who bought and sold during the crash across multiple markets, including S&P 500 futures, S&P 500 baskets, and rates.
This experience shaped my understanding of markets. Ever since, I’ve been focused on answering who owns what and why. Today, we call this flow and positioning, but knowing who held what and the pressures they faced was invaluable back then. Were they in a drawdown? Were they doing well? Did they see inflows or outflows? Were they levered or not? Understanding these dynamics—and who the players and their end investors were—has been the foundation of my life’s work.
Is that understanding of flow and positioning what guided your career following Solomon Brothers, even when you had the chance to work with firms like Long-Term Capital Management (LTCM)?
When many of my friends at Solomon’s prop desk went off to start LTCM, I had the worst year of my career in 1995. My convertible bond strategy and most hedge funds collapsed due to the Fed tightening. I asked those guys for a job multiple times. Thank God I didn’t get it, but they were the most brilliant people I knew back then. At the time, Solomon had just gotten past the treasury bond auction scandal, which John Meriwether, at least in part, oversaw, and that led to his departure to start LTCM. By then, Solomon was the worst-performing stock in the S&P 500 for the first ten years of my career—bar none. So, when LTCM launched, Solomon wasn’t a great place to be. I thought it through carefully—and even acted on it—but they didn’t want me.
Following LTCM, is that when things started clicking for you from a macro perspective regarding the relationship between macro crises and relative value trades failing? Moving into the future, what are some of the big macro themes you think may affect market outcomes significantly over the next few years?
Honestly, back in 1995, I had no idea what macroeconomics meant or how it worked, and I didn’t fully appreciate its significance. By 1998, it started becoming more apparent with the LTCM unwind. It wasn’t just LTCM; many firms, including Citibank, where I worked, were involved in government bond arbitrage. LTCM was simply the poster child, so attention gravitated there. By 2004, when I started my hedge fund, people were beginning to consider the possibility of hedge funds deleveraging as a cause of widespread contagion. Still, it wasn’t until 2007 and 2008 that I truly grasped the scale of that risk.
In any case, I prefer to operate on a one-year horizon. What’s clear now is that the Fed, more so than other central banks, has concluded that inflation is no longer a concern—it’s not going to re-accelerate. Because of that, they can lower interest rates relatively quickly, even if the job market doesn’t weaken enough to force their hand. You could call it a normalization. Since mid-December of last year, when the Fed started emphasizing the importance of real short-term interest rates, we’ve been on this path toward normalization. The idea is that real short-term rates dictate both inflation and economic strength, and the Fed is fully committed to returning to a normal interest rate—quickly.
The critical question is, are they right? That’s what markets are wrestling with now. Are they correct in saying that financial conditions are tight and that lowering short-term rates will ease those conditions, which flow through to stimulate the economy? Typically, the Fed doesn’t try to steer the economy directly; instead, it responds to and offsets economic pressures. When inflation rises, they hike—and do it aggressively, though often a bit late until they’re confident. They keep hiking until they’re optimistic inflation is rolling over. Conversely, when they cut rates, they should, in my view, be leaning against a trend and responding to a slowing economy that’s disinflationary and underperforming on growth and jobs.
We’re in a strange situation now. The Fed doesn’t need to combat inflation, and they certainly don’t believe they need to. Instead, they think that by acting too cautiously, they risk over-correcting. So they’re normalizing rates. But what does “normal” even mean now? Is the current path of normalization too aggressive? At the heart of it, this revolves around the pace and destination of rate cuts. That’s what we need to watch moving forward.
There’s also an election coming in early November, which could impact the economy. Politically, I believe it doesn’t matter much which party is in power—they both tend to increase the pie by accumulating more debt and engaging in deficit spending. The difference lies in who and how they distribute that pie. It matters for specific sectors and individual stocks. One might think that oil would do very well under Harris and very poorly under Trump, but one might think that oil companies are going to do very well under Trump and very poorly under Harris. It’s complicated but consequential.
Post-election, I’ll be watching to see if there’s any sign of austerity from either party, though I expect none. We’ll likely continue running budget deficits, though they won’t grow as fast. COVID drove a rapid spike in spending, but we’ve since returned to a more constant deficit. The change in expenditures, rather than the percentage of GDP, influences the economy. If spending remains steady, it acts as a drag. If it grows, it stimulates the economy. How that unfolds depends on the balance of power between the House, Senate, and the Oval Office.
Looking ahead, the Fed will cut rates to around 3%, leading to a soft landing—no significant increase in unemployment and inflation hitting their target. I find that scenario unlikely. It’s like a skipper on a battleship trying to dock perfectly by pulling an antiquated lever. The Fed doesn’t have that much control by tweaking the short-term interest rate; financial conditions matter most to me: the availability and cost of financing for consumers and companies, accumulated wealth, and the health of the dominant financial institutions. Right now, all indicators suggest consumption and investment conditions are favorable. At the corporate and individual levels, income is strong, and corporate profits are expected to remain robust. There’s no need to dissave or leverage up, but they can if they want to consume.
Given these conditions, I’ve remained bullish on the economy since April 2020 and still don’t foresee a recession. This leads me to question why the Fed is normalizing rates and why they believe this won’t stimulate consumption and investment. I think the 3% rate target is too low. If I’m right, inflation will stay sticky or rise slightly relative to their target—not dramatically, as there’s no supply shock, but the demand and monetary sides are still stimulative. Why would major corporations start cutting jobs when they’re reporting record earnings and the economy sees record GDP? I don’t expect a significant weakening in the job market, especially as the government continues deficit spending. In my view, the direction the central bank is taking—normalizing rates—is misaligned with the economy’s current strength.
Is this preemptive action by the Fed a mistake?
I don’t know. We’ll have to see what Jerome Powell does. He cut rates by 50 basis points, and now (September 25), the markets are pricing in about a 17% chance that the two 25 basis point cuts projected for the next two meetings will happen. There’s an 83% chance we’ll see two 50 basis point cuts or one 50 and one 25. The trough interest rate they’re targeting is now around 2.87%, the lowest we’ve seen, except for a brief moment on August 5 when people called for emergency cuts of 75 basis points. So, that’s a significant drop. Christopher Waller and other Fed officials have indicated that rates will likely come down over the next 6 to 12 months, and there’s plenty of room for further cuts. The Fed’s ‘dots’ representing the minimum projected path for interest rates validate this. Meanwhile, inflation expectations have risen daily since the Fed meeting, with gold at all-time highs, bitcoin rallying, stocks not so much, and long-term bonds selling off. Only very short-term bonds are rallying.
Gold is inversely correlated with rates, correct? So, you have other factors, like buying from central banks, that may help buoy it in recent years, correct?
Yes. Many central banks have been increasing their gold holdings — the obvious ones are China and Saudi Arabia. Switzerland is another, and some of the buying may involve private citizens in some cases. There’s been a broader trend among countries that don’t want to hold U.S. assets, particularly adversaries, turning to alternatives like gold. But this flow is unpredictable. Prices slow it down; people don’t buy gold at any price. It’s fairly inelastic — they’ll buy at most prices but not at every price.
In my framework, I’ve always been bullish on gold since leaving Bridgewater, where I was indoctrinated to understand the value of non-fiat currencies. I haven’t yet bought into Bitcoin because its price is still too correlated with the Nasdaq for me to consider it a true monetary equivalent, though it may become one someday.
Moreover, there are a few ways inflation arises. Demand-side inflation happens when people decide to spend more, which can vary with societal changes and human behavior. Supply-side inflation can come from labor shortages and rising costs in services and manufacturing. However, the latter can’t be hedged with gold because its value doesn’t depend on these forces. The key to gold is its relationship to currency. The more currency that gets printed, the less valuable it becomes relative to gold. Gold is a hedge against monetary inflation. That said, I’m cautious about gold prices in the short term because we’ve diverged from the following three core factors I look at.
First, I see gold as a real currency with a zero coupon. Real rates have fallen but recently stabilized. Despite this, the drop in real rates has driven up gold prices considerably, making gold seem overvalued relative to real rates.
Second, I consider the credibility of central banks. Are they becoming more or less credible? You could debate that all day. You hold gold if you believe there’s less confidence in central banks. I think they’ve done a decent job tackling inflation, at least in perception, which should be bearish for gold since the Fed’s “mission accomplished” suggests stronger credibility.
Lastly, I look at monetary inflation. The U.S. has pretty much wrapped up its money-printing experiment. Sure, we still run a deficit, but that’s different from the aggressive balance sheet expansion we saw before. The balance sheet is still too large, but the impulse has subsided. Meanwhile, China has signaled a willingness to ease credit conditions, lower rates, and encourage banks to buy equities, though they haven’t engaged in fiscal stimulus yet. If they do, China could be where the U.S. was in 2021, which would be bullish for gold. I suspect part of the reason for increased Chinese gold buying is the expectation of significant monetary stimulus. We’ll have to wait and see if that happens, but it would be very bullish for gold if it does.
All things considered, I think gold is overpriced, so I’m trimming my gold positions in my beta portfolio. I’ve even placed a small speculative short position in my alpha portfolio. It’s still a bull market for gold, but bull markets do correct, and I’ll probably be buying the dip when it happens.
Graphic: Retrieved from Goldman Sachs Group Inc (NYSE: GS) via The Market Ear.
In the context of inflation staying sticky, could you foresee a period when, even if markets rise in nominal terms, in real terms, they don’t go anywhere or go down?
The ideal scenario for a broad portfolio to meaningfully outperform cash is if the central bank eases more than expected and inflation doesn’t respond. If that happens, every asset will outperform cash. Is it possible? Of course—it’s happened. Assets have done very well relative to cash this year despite a brief drop in August. But the question remains: can this continue indefinitely? There’s a natural limit to asset growth. Still, for now, the central bank seems more dovish each day despite no supporting data. It raises the question of whether they have an agenda. I don’t believe they know more than anyone else, but their actions suggest a strong confidence that inflation won’t rise. If they’re right, assets should hold up. Will they perform exceptionally next year? Probably not. But with cash yielding less than 4% on a one-year bill, that’s becoming less attractive too.
Leading to the volatility during August, we saw some rotation beneath the surface of the index, with movement into small caps and some softening in names like Nvidia. One could say that foreshadowed further weakness. Still, did you ever anticipate the unsettling volatility we saw and the subsequent quick recovery?
I wrote a fairly extensive piece on the dispersion trade and was bearish on the idea, expecting it to unwind. I was mindful of the yen’s strengthening and role in deleveraging, especially after seeing the wild moves in July following the CPI report. There was some instability, which I anticipated. But, in hindsight, the only real opportunity was to go all-in long at the bottom in August. I covered some positions and bought a bit more, but I didn’t cover enough, and I’m surprised by how strong the reversal was. Looking back, it’s clear the markets were already convinced the Fed would ease aggressively, and that’s where we stand now.
I saw a lot of commentary about how some of that risky positioning could have been doubling down following the August drop. Do you get concerned that this foreshadows something bigger happening in the future?
Everyone currently in the market is where they want to be. Their risk managers are comfortable, they’re comfortable, and they’re not over-leveraged. There’s no one delaying a margin call right now. These speculative unwinds happen fast unless they’re systemic and start feeding on each other. But we didn’t see that. More importantly, there was no sign of any banking institution struggling. The bigger story is consistent (i.e., passive) investment driven by strong incomes, robust job markets, steady 401(k) contributions, insurance plans, and government spending. In addition, reinvesting income from existing investments continues to fuel this trend. From what I see, it’s fairly leveraged, but only a significant drawdown would cause that to reverse.
And when you say meaningful drawdown, what does that look like?
10% corrections would probably mean a dip is less likely to be bought. You know, a 5% correction is just getting bought.
Could you ever foresee, though we have things in place to prevent such a thing from occurring again, a 1987-type crash unwinding some of this risky positioning in a big way? How would that look?
The odds of a stock market crash are low. A slower correction is more likely than a crash.
We had this rapid move down, and we’ve come back up. With markets now near all-time highs, how do you think about portfolio structuring? You talked a bit about positioning in gold, equities, etc. How do you think about structuring a portfolio, and do you look at things like volatility or skew levels as an input or guide?
When constructing a portfolio, the first step is to clarify your goals. For most people, the aim should be building a balanced portfolio that’s diversified across growth and inflation risks. It’s important not to focus on timing markets or picking specific asset classes. Instead, set it and forget it, with a long-term horizon of 10-20 years. Of course, some money will be needed sooner, so you must manage that more conservatively. Depending on your age and job prospects, you might adjust your risk tolerance—the better your prospects, the more risk you can afford.
My advice? Don’t spend time betting on markets. Focus on building a “set it and forget it” beta portfolio of long assets and keep adding to it. Spend your energy earning money outside the market instead. Speculating on markets is tough. It’s a zero-sum game—your gain is someone else’s loss, and that person is likely smart and motivated. It’s “Fight Night,” not passive investing. Thinking you’ll get lucky? These are sharks out there who will devour you. Competing against them far exceeds the costs of gambling in a casino. It’s like playing poker, not blackjack or craps. If you enter the game, you better be confident in your strategy because the competition is fierce.
If I’m not sleeping, I’m working to maintain whatever edge I might have, and I’m still unsure if I even have one. So, how do I build portfolios? Cautiously, with low confidence, sticking to what I know. I balance risk management, never going all in and grinding through it, just like Joey Knish, John Turturro’s character in Rounders. That’s the guy I want to be.
In terms of Damped Spring’s story, what do you want to do there? You’ve been running that for a few years, starting with a very small followership, and then you scaled that up. You’ve gotten to this point? What’s next?
I have a life I enjoy. I maintain relationships with a few hundred institutional clients, and over 15 of the largest firms value my insights. I provide them with my research, and I’ve also built deep connections with professionals—many of whom prefer to remain anonymous—who want to be members of Damped Spring. These members ask me questions like yours, and I give them data-driven answers. My goal is to meet them wherever they are on their learning curve and help them progress in a very hands-on way. Every day, I work with clients, answering their questions thoughtfully or being upfront if I don’t have the answer. I find that incredibly rewarding.
The financial side is a small part; it’s not about the money for me. Institutions pay because they value the service, and I charge individuals mainly to ensure they’re serious and to avoid wasting time with internet trolls. But people care—they want to be part of this community and learn from each other, which is wonderful. I’ll keep doing it for as long as I can add value and people want to hear what I say.
I’ve also started “2 Gray Beards” with Nick Givanovic. It’s a different approach—we offer low-touch, 20-minute videos once a week explaining what’s happening worldwide and what it means for long-only portfolios. People interested in 2 Gray Beards often don’t have much time to consider their investments. Many rely on their financial advisor or money manager, who might charge 80 basis points a year—say $40,000 for someone with decent wealth—and often, they don’t fully understand what the advisor says.
We aim to reach these end clients directly and say, “Here’s what’s happening. Watch these videos for 20 minutes a week for a few months, maybe half a year, and I guarantee you’ll be able to have a more meaningful conversation with your financial advisor. If we’re successful, you might understand your portfolio better than your advisor.” Nick and I see this as valuable and love doing it.
What’s the biggest lesson you’ve learned in the last four years? It could be good or bad.
Underestimating how far momentum could take the market, whether up or down. I was bullish from April 2020 to February 2022, and I thought a 5 or 10% correction in 2022 would be the extent of it—but I stayed long for too long. Likewise, as markets bounced, I held onto my short positions for too long. What’s interesting to me is the role of momentum. It seems to be a more dominant factor than my models have suggested, and while I’m addressing it, it’s still somewhat unclear whether this is driven by momentum strategies or just passive money flows. I’m still learning, but that’s what I’m focused on most right now.
Well, that ties it up. I appreciate your time. It is an honor. Is there something else you’d like to add?
Recognize that beta is the way to go—it’s not difficult, and anyone can guide you through it. However, be cautious not to get too caught up in short-term trading.
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This week’s letter is about 6,000 words and may be cut off. If so, try viewing it in a browser window!
Our recent focus reflexivity manifests in politics through reinforcing shared beliefs and narratives. When political group members share an ideology, their interactions often confirm and amplify their existing views, creating feedback loops. These loops can shape the group’s perception of political realities, such as the strength of their candidate, which in turn influences voter turnout and campaign contributions. This homogeneity also leads to a lack of exposure to opposing views, increasing the risk of misreading voter sentiment and making strategic errors in political campaigns.
This dynamic was evident in the 2016 U.S. presidential election. Many in liberal circles were convinced of Hillary Clinton’s victory, relying on polling data and a widespread belief in her inevitability. This perception reinforced within these groups created a reflexive cycle that contributed to complacency and lower turnout in critical swing states. Those in the Republican bubble who supported Donald Trump also experienced their form of reflexivity; early support and momentum generated enthusiasm that ultimately led to his victory. Both sides exhibited fallibility—Democrats overestimated Clinton’s support, while Republicans underestimated the opposition to Trump.
Vuk Vukovic, the CIO and co-founder of Oraclum Capital, is acutely aware of reflexivity and fallibility’s impact on politics and economics. Over the past decade, he has applied his academic research in political economics to accurately predict the outcomes of the past two U.S. elections and the Brexit referendum, as well as influence policy in his home country of Croatia. Following the pandemic, Vuković and his co-founders sought to monetize their predictive success, leading them to the financial markets. Today, they use the wisdom of crowds and their understanding of social networks to outperform markets with their hedge fund. Vuković graciously joined Physik Invest’s Market Intelligence podcast to discuss his career, research, starting and operating a hedge fund, trading psychology, and investment processes. The video can be accessed at this link and below. An edited transcript follows.
We spoke in April, and Oraclum Capital, your upstart hedge fund, sat at ~$8.6 million in assets under management. Has this number changed?
We’re going into September with $17 million under management, so it has been going well.
I want to go back in time before you studied economics. What were some of your big interests growing up, and how did they guide your pursuit of economics in school?
My interest in economics partly stemmed from my parents, who were both involved in that field. But even as a kid, I was fascinated by currencies and stock markets. Something about them attracted me—maybe it was the whole money aspect, but I think it was more profound than that. However, as I pursued my education, I diverted from finance and instead focused on political economics, which is more theoretical and combines public choice theory with macroeconomics. You can’t fully understand economics without understanding politics. Fast forward to today, I’ve returned to my first love, finance.
The idea of making money got me engaged in markets, but the details and the process kept me engaged. So, structuring trades, learning how markets work, and things like credit and positioning kept me involved. Does this resonate?
That’s the primary motivation, and you learn things that make it more or less attractive. In our case, it was more attractive.
So you went to the London School of Economics and the University of Oxford. Why those two?
Before that, I earned my Bachelor of Economics at the University of Zagreb in Croatia. During the summers of 2009 and 2010, I went to the United States—first to attend a summer school at Berkeley and then Harvard the following year.
I considered staying in Zagreb, but after those experiences, I realized I should go abroad. I chose the United Kingdom because it was closer and less expensive than the United States, especially at the master’s level. In Europe, you typically pursue a master’s before a PhD, allowing you to finance your education gradually.
The LSE is a prestigious institution with a political economy program aligned with my interests. If I wanted to go to the United States immediately, I would have had to choose an economics PhD and then branch out from there, which is not what I wanted.
Did you get a lot of value from those summer schools?
Absolutely. They showed me that I could compete in an environment where I wasn’t sure I would be able to.
I earned straight A’s at Berkeley and Harvard. I took an Intermediate Macroeconomics course and a Contemporary Theories of Political Economy course at Berkeley. At Harvard, I studied International Monetary Economics, taught by a former assistant to Milton Friedman. I also took a course on global financial crises there, which was particularly interesting to me because the Global Financial Crisis had just started in 2008. At that time, I was in my second or third year of university, and it shaped my research focus ever since. I found my niche by exploring the financial crisis from a political economy perspective, examining the political causes of the crisis, such as why banks were allowed to take on so much risk, and so on.
You wrote a couple of papers. How did you develop your theses, and how long did it take you to research and defend them?
Most of my political economics research explicitly focuses on corruption and lobbying. When I came to Oxford, my attention was primarily on the collusion between politics and economics—essentially, the relationship between the corporate and political worlds.
It all began with a paper on corruption in Croatia, where I examined the connection between firms and people in power and how this relationship affected reelection chances. I also attempted to measure corruption through public procurements awarded to specific firms. Unfortunately, my findings showed a significant impact of corruption on the reelection chances of Croatian mayors, cities, and municipalities.
The second paper I worked on centered around bank bailouts in the United States during the 2008 crisis, which has been a focal point of my research interests. I aimed to determine whether banks better connected to congresspeople received a more favorable bailout deal relative to their assets, and indeed, they did. With these two ideas and the supporting data, I developed a more unified theory on how corporate executives and politicians connect and how those connections impact economic outcomes. In my specific case, I was looking at income distribution and inequality.
This led to my third paper at Oxford. I analyzed a massive dataset of about a million corporate executives in the United States and the United Kingdom, linking them to politicians and observing that those better connected had much higher salaries. Specifically, the impact was about $150,000 more in annual salary in the United States. To clarify, these were corporate executives—CEOs, the C-suite, or board members—being compared within the same company, with the politically connected ones earning a premium of approximately $150,000. Political connections were measured by whether the executive had previously worked with someone at a senior government level or belonged to the same organization, such as a country club, charity, or other networking group. These affiliations might not necessarily make you friends, but they provide a way to connect with critical individuals when needed.
I remember this a couple of years ago: Amazon’s Jeff Bezos and Jerome Powell appeared at the same party or dinner. Jerome Powell was grilled over what was potentially discussed, and your response reminded me of that.
I was looking into that precisely during the Global Financial Crisis when Timothy Franz Geithner and Henry M. Paulson, Jr. held regularly scheduled meetings with the CEOs of the top eight banks. This was documented in The New Yorker and The New York Times. I was reading those transcripts, and it was clear that these people were friends. There’s also an excellent paper on social connections in a crisis, highlighting the importance of being connected—especially when you need to reach the right person to secure a bailout for your bank in times of crisis.
Did your findings in Croatia ever have an impact on policy?
Surprisingly, yes, though not as much as I had hoped.
My main finding was that there are very suspicious levels of public procurement where companies with, for example, zero employees can bid and secure huge deals from local governments. I focused solely on the local level. One potential solution to combat this issue is to introduce complete budget transparency so that the public can see every single expenditure made by the government. This would include everything from large procurement deals down to receipts for lunches, dinners, and similar expenses. You could even see who’s dining with whom and the salaries of public sector employees.
We started implementing this project in a few cities in Croatia, including Bjelovar—about five or six cities. These cities adopted the project with a message of having nothing to hide and being open and completely transparent. Incidentally, all the mayors who implemented our project significantly outperformed their opponents in subsequent elections. So, while corruption may help you get reelected, being fully transparent helps even more.
We wanted to extend this project to a broader audience of mayors, but unfortunately, the interest wasn’t there. What did happen, however, was that we were able to make this a formal part of the budget law. But now, the problem is that the bureaucracy watered it down. The law explicitly requires every local government to have full transparency, but as they say, the devil is in the details. Bureaucrats added a second layer of interpretation, defining what it means to be fully transparent, and the law’s impact has been diluted. So, I’m done fighting those battles. That’s behind me, and I’m doing something completely different now.
How did you develop the methodology used to predict elections, and how did you monetize it?
I didn’t initially think about starting a hedge fund, but I knew there was some applicability in markets.
So, my two colleagues, Dejan Vinković, a physicist, and Mile Šikić, a computer scientist, and I were in the academic sector. They were professors, and I was a lecturer at my university. We wanted to find a new way to create better, more predictive surveys. We were looking at what Nate Silver was doing in the United States, and since the three of us were all political junkies, elections were the first thing we wanted to apply these methods to. So, we started with the British elections in 2015, and it worked well. Our correct prediction of the Brexit referendum and Trump’s 2016 election further propelled us; we initially wanted to write a paper on our new prediction method, but we opted to try to build a company and monetize it instead.
Now, what’s the logic behind that? There are two components.
The first is the wisdom of crowds. You ask people what they think will happen and what everyone around them thinks will happen. Let’s say it’s an election. So, who is going to win, Trump or Harris? That’s the first question. Second, what do you think other people around you think will happen? When you get to that second question, you put people in other people’s shoes, forcing them to switch between System 1 and 2 thinking, as Daniel Kahneman and Amos Tversky describe.
The second part involves the networking aspect, the crux of our approach. We aimed to figure out who was friends with whom. For example, if you’re in a liberal or conservative bubble, you have a low ability to predict what’s going to happen outside of your bubble. So, we focused on people in more heterogeneous groups, where some friends are left-leaning, some are right-leaning, and some are centrist. This diversity increases the probability of making accurate predictions. The methodology involves playing with probabilities assigned to different individuals, and these probabilities have weights, which is how we determine the accuracy. So, not every person’s opinion matters in the same way. That’s the general idea.
Where would these surveys be accessible?
The crucial part is social media. Previously, during the elections, we did everything on Facebook. But this was before Cambridge Analytica when Facebook was very open to giving us the data we needed. We didn’t take any personal information besides what we asked for in the survey, like gender and age; we only gathered network data from Facebook. If your friends joined the survey with you, we could connect you. Now, we’re doing everything on Twitter and LinkedIn. We’re sourcing from those networks because Facebook no longer allows it following the Cambridge Analytica scandal. This is not a problem because people are typically on the same platforms. Again, we don’t need to know who these people are. All we know is who they’re connected with.
Would you have achieved the same results if you could go back and use Twitter and LinkedIn?
The data on Facebook was more versatile, and there was more of it. You could do more with a bigger pool. It wasn’t just the data itself but also the critical relationships between the data. Much of this was based on network theory in physics, akin to network science in general. My two partners, and later I, became remarkably proficient in this area. So, all we needed was good data to fit the theory and see if these things worked, and they did. With the Twitter data, I don’t think it would have been as helpful as the Facebook data, but once you learn what you need, you can apply it to any other platform that has a network.
How did you come up with the name Oraclum?
It’s a Latin word for prediction.
So, before starting the hedge fund, did you have any investing experience, and how did you learn about markets? What books did you read?
I’ve been investing on and off since 2011-2012. I began trading options in a retail capacity in 2018. Back then, trading options on Tesla was the name of the game, and I went through the whole trader experience. I love the Market Wizards book by Schwager because I went through the same processes as many of the people featured in it. You initially make a lot of money on something and think, “Oh my, this is easy, and I am so smart.” Then, you lose a lot of money on something else, and that’s when you start learning. So, I did have some experience with options. Since 2021, when I began testing Oraclum’s methodology, my options trading knowledge has improved significantly. We needed options because they provide convexity (i.e., non-linear payoffs), which is crucial when predicting with 60%-70% accuracy, which is what we achieved. So, while I did have some experience, it has grown exponentially over the past few years since I started the fund.
Graphic: Retrieved from Simplify Asset Management. “An investment strategy is convex if its payoff relative to its benchmark is curved upward.”
What did the fund structuring process look like, and what guided your decision to create a hedge fund versus an ETF, which would allow more people access?
The hedge fund versus the ETF is a matter of cost. Launching an ETF requires about $250,000 upfront, which is beyond our reach at the time. However, we aim to establish an ETF within the next few years to offer it to a broader audience. Many people who participate in our surveys are eager to invest, but with our current $100,000 cap, they can’t. The ETF would allow them to be investors, providing an even stronger incentive to participate and perform well in the surveys.
To answer your question further, we need to go back to 2016, around the time of Brexit and Trump’s election. That’s when we decided to start a company. We set up shop in the United Kingdom, specifically in Cambridge—no connection to Cambridge Analytica; we’re the good guys and don’t misuse data. Initially, we focused on market research projects on elections, market trends, and public sentiment. However, after correctly predicting the 2020 election outcome between Biden and Trump, we started attracting clients from the finance industry who were buying our election predictions. I thought, “Why not test this on the markets?”
We had some funds and could hire people to help us, so we began the project with the mindset of trying it out for a year or two. If it didn’t work, we could always return to market research. But the project quickly gained momentum. I invested about $20,000 of my own money, and over a year and a half, I grew it to $54,000. I did this transparently, posting screenshots of my trades in my newsletter. People could see my profits and losses weekly. I would even send survey participants the trades I planned to make, and this transparency resonated with them—some became investors.
Like many others, our biggest investor initially followed us on Twitter and subscribed to the newsletter. After nearly a year of testing, the final decision to start the hedge fund came around the summer of 2022. People following us said they wanted to invest more seriously, so we started the process. I remember discussing it with my wife and telling her, “You need the confidence of someone who knows nothing about something but does it anyway.” We launched the hedge fund in 2023 and learned as we went.
Before we started, I spoke with a lawyer and met with potential investors. I also surveyed newsletter subscribers to gauge interest and ask if they’d like to invest. We received around $10 million in commitments. Of course, there’s a difference between pledging money and investing it, so we only started with about $2 million when we launched the fund in February 2023.
Our hedge fund story differs from most. While others often launch with $100 million, $200 million, or even $1 billion, we’re bootstrapping our way up, starting small but with solid performance and growing trust from our investors. It’s an unconventional story, but we don’t need the typical team of analysts or a Bloomberg terminal. We have our method and trade in a very straightforward way.
What does it cost to run your type of operation?
In the first year, last year, the budget was about $100,000. It is more significant this year because I’m expanding the entire marketing scope. It’s projected to be around $400,000. However, with our profit, we’re comfortably funding the entire operation.
Was creating the fund structure cost-intensive as well?
Surprisingly, no. It was about $30,000 altogether and set up in Delaware. I found good lawyers and used all the money I earned investing myself to fund it.
What does your investment process look like from pre- to post-trade?
It is straightforward. We get a signal every Wednesday before the market opens. Once we get the signal, we want to determine its strength. Then, we typically open positions about an hour after the opening, at about 10:30 Eastern on Wednesdays. We will keep the position until the end of trading on Fridays. This is the optimal timing for our prediction if we were right. We only allocate about 2% of our portfolio to each trade. If we’re wrong, the options expire worthless, and we lose 2% of the premium. If we’re right, then we make multiples of that. That is in a nutshell. Now, there are things that we can do. For example, we have this trailing stop strategy; if we make 1.5%, we will increase stops and keep raising them gradually. We have been testing and have considered using 0 DTE options in the other direction to hedge our profits.
You would never try any complex or ratio-type structures, right?
No, we keep it simple. We used to, and the following is a great story about that.
The fund is performing well currently. However, right out of the gate in March of last year, we were down 15% on our first $2 million. At the start, we told our investors they would be out if we lost 20%, so it was a tricky situation.
What went wrong? Several things contributed.
For background, I only risked 10% each week when trading alone. With about $20,000, this meant risking $2,000. A part of my strategy involved using iron condors, as our methodology works well in both direction and precision; our predictions are within 2% of the market’s actual ending about 80-85% of the time, which is quite significant. Thus, the iron condor structure worked well when trading on my own in 2021 and early 2022.
However, since the introduction of 0 DTE options, the price of the Friday options has changed dramatically, and the risk-reward ratio has shifted from 2:1 to 8:1; now, I would risk $800 to make the same $100. If I lost $800, I would need eight good weeks to compensate for one bad week. Consequently, iron condors are no longer viable. This structure, we know, significantly hurt us in the first quarter of 2023, which is why we abandoned it, along with others, focusing solely on directional options and spreads.
My first thought was how much of that was the volatility environment. So you dropped the condors, and then, did you change how you traded the verticals?
When we started the fund, we risked about 5%. When things quickly got out of hand, we lowered it; when we were down 15%, we reduced it to 1%, and it took us about five months to break even, gradually increasing our exposure. Now, we’ve found that 2% to 3%, depending on the strength of the signal, is our optimal point. So yes, it affected our position sizing. Regarding volatility in March of last year, the collapse of Silicon Valley Bank also impacted us.
Graphic: Retrieved from Federal Reserve. Due to the rapid pace of interest rate increases, Silicon Valley Bank’s unhedged bond portfolio significantly lost value, contributing to difficulties meeting withdrawal demands.
Would you consider trades like the iron condor again if the volatility environment changed?
It works for us over 80% of the time, but the risk-reward ratio is no longer suitable. That’s why we don’t want to engage again. The current data shows flat or slightly above-flat results, so there’s no point in doing it.
Do changes in volatility and positioning affect how you trade the underlying market? So, at the beginning of August, we had a bunch of volatility. You probably weren’t in positions at the start of the week because it was a Monday, and you avoided that. But do those significant changes in volatility impact how you structure trades?
Not the structure.
Let’s go back to that week. On Monday, markets were down. We were mostly in bonds and cash. We ended the week up 1%, with the compression of volatility benefitting us; as volatility went down and markets went up, it was an easy trade for us in retrospect.
It would have been fantastic if we had held puts on that Monday. If we had held calls, we would have only lost the premiums. That’s why volatility doesn’t impact us negatively, no matter how big. This is because we’re not sellers of options. If we were sellers, that would be a different problem. However, since we buy options, the most we can lose is the premium. We know our risk—if we’re wrong in a week like that, we lose 2% and move on to the following week.
Also, I noticed a mismatch between bid and ask prices on that particular day. That is something to consider as well. But if I had put options and there was a huge mismatch, we would have worked them at the mid-price.
How are you executing these orders? Are these just market orders, or are you setting a limit?
Always limit orders.
Are you using one of the ETFs, or do you use cash-settled indexes like the SPX?
ETF. Not the cash.
Would going into something like the SPX be more cost-efficient if you grow large enough?
Yes, absolutely. Right now, one of our institutional investors is coming in, and they want us to employ the same strategy using options on futures like the E-mini S&P 500 (FUTURE: /ES). Looking at the data, the approach also works there.
Are you testing trades in real time or backtesting?
Backtest.
If you were to go live with either the /ES or SPX, would you do that with a smaller size initially, test it out, and see how it works on that scale?
Yes. Initially, use a smaller size and then push it up as we go along.
Right now, we’re small—a $17 million fund—so I trade a couple hundred thousand dollars worth of premium every week, which is not a lot. Once bigger, we can look to the SPX and /ES, where the liquidity pools keep increasing.
As we grow in size, it’s straightforward for us to scale.
You said you risked 2%. Is the other 98% still in Treasury Bills?
90% in T-Bills, and 8% is a cash buffer.
Graphic: Retrieved from Exotic Options and Hybrids.
Because you’re always out of these spreads at the end of the week, I assume you’re pretty liquid and can quickly meet redemptions.
Yes, that’s not a problem for us.
If interest rates fell or you had a significant lull, would that change how you invest that capital?
It probably would. Right now, we’re taking advantage of the carry. There’s a straightforward carry trade—you leave cash in bonds for a year and get ~4%. It will probably be a different instrument if we return to the pre-COVID interest rate environment or even post-COVID 2021. However, I would still want to keep most of it in bonds because of the safety. Think of it like Taleb’s “Barbell Strategy.” You have 90% in something very safe and 10% in something very volatile—in our case, 2%.
You’re not using box spreads, right? You’re actually in T-Bills, right?
We have T-Bills but will switch to box spreads because of the tax implications.
How do you monitor the strength of the signals, and do you scale back if that signal weakens?
This is an ongoing process, and there are several things we’re looking at. Regarding the signal strength, we have KPIs. We’re monitoring whether the signal is improving or worsening over the past 4 or 5 weeks. If it falls below our crucial indicator, we say, “Okay, let’s see what the problem is, what’s happening, and how we can fix it?” Signal weakening can be due to several reasons, such as a drop in our survey response rates during slower periods of the year. If we can detect issues, we can prevent them from escalating. We allow ourselves a maximum of one lousy month.
Can you explain your fee structure?
We have a 1.5% management fee and a 25% performance fee subject to an 8% hurdle, accounted for quarterly. We must clear 2% each quarter before applying the 25% performance fee. There’s also a high-water mark in place. Performance fees can only be charged if the fund consistently makes money. So, if the fund makes money in one quarter but loses money in the next, it can only charge a performance fee once it has recovered the losses in the subsequent quarter and exceeded the previous high-water mark; the performance fee can only be applied to any additional profits after surpassing the previous peak value.
Despite being systematic, you’re still executing these by hand, inputting orders, setting limits, and so on, right? How do you manage any biases and emotions and just execute?
I have a psychology coach guiding me through this process, which is necessary. I’ve experienced losses before starting the fund, but managing other people’s money is different—it comes with much higher responsibility. Plus, you must report to these people regularly and inform them about any losses. This was particularly challenging for us in March of 2023 when we had just started the fund and were down 15%. We thought, “What do we do now, and how do we face these people again?” I did a lot of exercises to help myself cope with the situation, and I realized that the solution lies in sticking to the process. The less I meddle, the better our investment returns are; we achieve better outcomes by completely removing our biases and following the process, one of our key performance indicators. Ultimately, I aim to expand the team, hire traders, and stop trading myself. Although I could automate the entire process, it doesn’t always work as intended; sometimes, the machine won’t perform exactly as you want. That’s why I believe human traders still have value. We’re not high-frequency traders, so we don’t need machines to execute nanosecond trades. Instead, we rely on humans following a system to execute the orders.
Do you ever have a signal and you’re putting on a trade but think, “This isn’t going to work,” but you still go through with it because you are following a system?
Yes, but I’ve taught myself not to deviate. Sure, maybe this week I’m going to help it, but the next week I’m probably going to destroy it. Again, it is the whole psychological mindset thing. I still get the urge, but you’re pushing yourself to make this emotionless. It is a process, so it’s going to take a while.
So, the hedge fund feels like your second act to me. Do you have a third in mind, and may that involve you working in the government, especially given the research you’ve done?
I’m so removed from governments that it’s liberating.
The three of us at Oraclum—Vinković, Šikić, and myself—are political junkies. Since starting the fund, I’ve asked myself why I even cared. At this point, it’s tough for me to think about a third act, especially now that we’re in the middle of building this.
It depends on how much money I earn—maybe philanthropy or something else. We’ll see.
Have you done any work for the next set of U.S. elections? If so, can you share any results?
This is the big argument that my two co-founders and I have. One of them is against us doing this because of the focus of the fund, our investors, and everything else. And that makes sense. We won’t do it, even though I see it as a great marketing tool.
If you were to predict the next set of elections, what would you do differently?
I streamline much more toward the key swing states.
Pennsylvania was the key state in the last two U.S. elections, 2020 and 2016. As soon as we saw in our survey that Trump was winning Pennsylvania in 2016, that was it; Trump was taking the election. The same happened in 2020. At no point did Biden ever lose Pennsylvania in our surveys. So that was the turning point for us. Ohio and Florida were going for Trump. Before this election, whoever won Ohio and Florida would become the U.S. president. Not this time because you had Pennsylvania and Michigan going in the other direction. So, if I were doing it this year, I would focus on a handful of swing states. You can follow the surveys for the rest, focusing on Pennsylvania and Michigan. Ohio and Florida will most likely go to Trump. But then, I would also look at Arizona, North Carolina, Georgia, Pennsylvania, Michigan, and Wisconsin.
I recently watched a podcast featuring Citadel’s Ken Griffin. In it, he emphasized the importance of studying your winners rather than getting too hung up on the losers. Does your experience validate this thinking?
That’s a good point. I get more excited about the winners and learn that the losers don’t matter—move on.
There’s this great quote by Roger Federer: “In tennis, perfection is impossible. In the 1526 matches I played, I won almost 80% of them. But I only won 54% of the points in those matches.” For him, it’s not about the points. When they’re gone, they’re gone. You move on to the next one. It’s the same thing here. For every week we lose 2%, we move on. But when we get a big win, we’re delighted. It’s a psychological thing as well. You can get much more if you don’t cut the profits too soon and keep a trailing loss. That’s why we have weeks where we’ve made 5% or 6% in a week, which is good. So there is something to it.
We study the winners because it can all come down to 5 or 6 weeks a year when we make the bulk of the return on the fund. Everything else cancels out; the small winners and losers cancel each other out.
Graphic: YouTube interview with Citadel’s Kenneth Griffin.
Do you have any mentors or people you look up to?
I love that Market Wizards book by Schwager. Every interview in it is very revealing and comforting. When I was younger, I idolized George Soros. What we do has nothing to do with how Soros trades; he’s a big ideas guy, and I could never compete. It’s the same thing with people like Ray Dalio. It’s a different way of competing.
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This week’s letter is about 7,000 words and may be cut off. If so, try viewing it in a browser window!
Our long-winded “Reality Is Path-Dependent” letter, which you can review here, received great feedback. Thank you! We will spare you the excruciating details this week and opt for more frivolity. Additionally, following this brief update section of the letter below, we will reveal our first “Market Intelligence” podcast episode and transcript. Please read on!
From low levels, spot-vol beta (i.e., the relationship between the market or “spot” and changes in its volatility or the sensitivity of volatility to the market’s trading) performs better, as The Ambrus Group’s Kris Sidial characterizes.
Sidial sees “some institutional flow reach for tail-like protection,” which we can observe in elevating volatility skew (i.e., the variation in implied volatilities or the market’s forecast of likely movements for different strike price options).
BNP Paribas (OTCMKTS: BNPQY) identifies fragility, noting that selling flows from systemic strategies are offset by buying flows in ETFs. Their positioning indicators remain close to “maximum long.” A more correlated move may destabilize broader measures and have policy consequences; reality is path-dependent. To explain, inflation numbers haven’t worsened, and the wealth effect has supported the economy. However, with a ~10% market pullback, there’s a ~$10 trillion money supply and collateral reduction.
“The Federal Reserve will tell you all day long that they don’t manage the market, but they manage the money supply,” Kai Volatility’s Cem Karsan says. “The market has a wealth effect, which is money supply. So you better believe they’re watching this market if we continue to decline here. A July 31st [cut] is not only on the table; it becomes likely.”
Bill Dudley, former president of the Federal Reserve Bank of New York and chair of the Bretton Woods Committee, agrees that the Fed should cut this month as the delta between the haves and have-nots grows. Easing financial conditions and surging stock markets increased wealthier households’ consumption propensity. To contain inflation sustained monetary tightening from the Fed would be required, but many at the bottom are falling on hard times. Tightening labor markets can lead to reduced spending, economic weakening, and reduced business investment. That portends layoffs and even less spending; recessions soon follow.
Reminiscences of a Market Maker
Imagine you’re a large trader whose fund’s survival depends on quickly hedging against a severe market drop. You log on to your computer and place an order to buy to open 25,000 put spreads in the S&P 500 (INDEX: SPX). You’re shocked that only 175 of the 25,000 are executed at your desired price; you must break the order into smaller pieces.
That’s the horror story of screen trading. It’s also why pit trading, which financial journalism has long predicted the end of, is here to stay. Exchanges like the Chicago Board Options Exchange or Cboe fulfill such demands, offering traders a size market through a hybrid model combining electronic and pit trading. That’s according to Mat Cashman from the Options Clearing Corporation (OCC), whom we featured on our inaugural podcast episode last week.
Cashman’s been in markets for decades, starting in the pits of Chicago’s trading floors before moving to London and back to build big trading businesses. He explains pit trading is here to stay. Just look to the Miami Exchange preparing to pair its electronic trading venue with a physical one in Miami’s Wynwood neighborhood. Pit trading enables better negotiation of large orders, which can be challenging to achieve with electronic trading, especially during volatile markets.
“You can only write a headline about how pit trading is dying so many times before you’re like, maybe it will never die,” he shared. “You can find this sweet spot in that hybrid environment where one bolsters the other, and they both feed on each other regarding liquidity; they’re puzzle pieces that fit into each other to help keep up with the size that needs to be executed at a price.”
Cashman went on to share a lot more, including his start in markets, managing risk and making faster decisions, the benefits and costs of automation, trading in the S&P 500 pit, the entities taking the other side of your trade, incentives, 0 DTE options and the risks of high-variance and illiquid trades. The video can be accessed at this link and below. A lightly edited transcript follows.
How did you get into markets?
Serendipitous is a good word for that.
I studied music and philosophy in college and was a saxophone player. I still am a saxophone player. I play all the time. But I was at a gig and met the drummer’s brother. This was like 1998. He was an options trader. An O’Connor Swiss Bank guy. That was a famous nexus for options trading, and many of the Chicago genesis of options trading started at O’Connor School. We started talking after the gig, and the drummer’s brother and I bonded over math and things of that nature.
“You should come down and talk to my partners,” the brother said.
So, I showed up and had a great meeting with them. They offered me a job on the spot as a clerk to become a trader. This was 1998 on the floor of the Cboe. There were a lot of things going on during the run-up in the Nasdaq bubble. In some ways, you’re looking for warm bodies. The floor is an incredibly chaotic place, so whoever you get has to be able to function in that environment, be excited by it, or have some proclivity for functioning there. Secondarily, you need someone who will be fast, quick on their feet, and understand math. And I happened to fit all those bills, and I am relatively tall at about 6’6”. That helps when dealing with 100-plus guys in a pit. Being tall, big, and aggressive helps. The stars aligned in that way.
Back then, you were a clerk for six months before you went out and had to break into pits, which were a very insular environment. If you think about how options trade, a limited amount of edge or money comes into the pit from orders. You’re dealing with a limited amount of edge and have 15 or 20 guys. It gets split up 20 ways. If you have 100 guys, it gets split up 100 ways. So, they were incentivized, and eventually, I was also incentivized as a member of a pit to keep people out. You want to avoid more people involved, especially if you have great products where everyone’s making a lot of money.
You don’t want that broadcast to the world.
So you have to go through the process of breaking in, which means you stand in the back, and people yell at you and call you the worst things you could imagine. Eventually, by just showing up every day and getting beat down, you go through this hazing process, get accepted into the fold, and suddenly become a pit member.
That took about six months as well. Once you get in, it is an entirely different environment. So that’s the kind of story. That was 1999. That was a long time ago. So, my career has had many different iterations of training since then. But that’s where it started. That was the genesis of the whole thing.
What products did you trade back then, and how long did it take you to get comfortable managing that risk, making quicker decisions, and so on?
I stood at Post 1 Station 1, which was for Xilinx, a chipmaker.
It takes about six months to show up and get into a situation where you can start trading. I learned Put/Call Parity and all of those things. I had a limited idea of Vega, Gamma, Delta, etc. In 1999, you’re looking for warm bodies, right? People who can understand how these things work relatively.
When you start trading, you’re in the pit doing everything the other guys do. Through the osmosis process, you learn how risk management works. In some ways, everyone in the pit has the same position because of the order flow. So, once you’re accepted into the pit, you learn the risk management ropes by getting your teeth kicked in and losing a bunch of money. It’s hard to say it that way. But in many ways, that’s how it works.
For instance, say that I have a giant call spread. I’m long 500 call spreads and short stock against them.
If the stock rallies to the long strike of the call spread and expires there, that is the worst-case scenario, especially if you don’t have it creatively hedged; instead, you have it hedged on a delta-neutral perspective, and you get smoked on that move because you’re short stock up, and your call spread never kicks in.
If everyone in the pit has that on, the collective groans as the stock rallies every day into expiration and then pins at the strike on Friday. That is instructive if you need help understanding what’s happening. You just know, “Oh, my P&L is incredibly negative every single day. Why is that?” And then you start to put the pieces together, … and learn the risk management part of it in a piecemeal sort of way over time. The funny part is that at the beginning, we talked about how you started reading all of these books, like “Dynamic Hedging: Managing Vanilla and Exotic Options.” Those are incredibly complex books. Never in a million years would I have even thought about picking up a book like that in 1999. So many things were going on, and I was just trying to keep up. I didn’t have time to read Nassim Taleb. I didn’t even know who that was.I do now, and I love reading that stuff, but it’s a much more informed and nuanced understanding of the landscape built up over 20 years of doing this. At first, you’re just busy getting your teeth kicked in and trying to figure out what is going on, and that part is the steep part of the learning curve.
How much of a psychological effect was in play back then versus today with automation removing people from the mix?
There are two sides to that coin.
It helps remove the emotional aspect of trading decisions and the human aspect of risk management.
Taking the emotion out of trading, as long as you feel your model is reliable, and you are continuously checking and tweaking it, can be beneficial. The problem arises when people substitute automation for good old-fashioned risk management.
The combination of those two things is the ultimate. I want all the good parts: to remove all the emotion from the trading so I don’t have FOMO (e.g., I’m not buying call spreads as a stock is rallying because everyone else is buying call spreads, or I’m not selling my stock when I’m short gamma because everyone else is).
Even if you have a sophisticated model, it doesn’t necessarily mean you can let it manage all your risk.
It is interesting to consider how it has changed the industry. That’s an interesting question as to whether or not the industry going forward is better suited or not quite as well prepared for the events on the horizon. I don’t know the answer to that question. I know that trading in the pit in 2008 was incredibly educational, and being able to experience that firsthand in an environment like that is a visceral experience and imprints certain things in your mind, especially regarding risk management, that I will never forget. And if you’re in a situation where your computer is doing all that for you, I’m curious to know how much of that visceral experience gets translated to you. I don’t want to go out there and say automation is removing all of those good parts from the market-making or trading community because it isn’t. But it’s a delicate balance people need to navigate intentionally and thoughtfully.
You end up moving out of the pits upstairs. What did you do while you were in London?
I had two instances of pit trading at the beginning and very end. In between was London for me. London was a marketplace where pit trading only existed if you traded metals.
Graphic: Photo retrieved from HM Treasury via Flickr.
By the time I had moved to London, I went over there for a company called DRW. The pit trading of options had migrated to an upstairs model or a phone around marketplace that sat side by side with an incredibly robust and one-of-a-kind electronic presence. It was early on for the electronic markets, but they were developed in places like Amsterdam for an extended period.
I traded Schatz, Bobl, and Bund options: the German twos, fives, and tens. So, German bond options. It’s the long end of Europe’s interest rate curve, which is actively traded. Then, the other side of the desk traded a short-term trade: your IBOR, short Serling, and some other pieces of the shorter part of the duration curve in the interest rates in Europe. And then, we had what we would call a relative value book, which was finding opportunities where we felt like things were mispriced relative to something else, and so we would have mid-curve options in the IBOR versus Bobl options that were on the five-year. So, it’s a mixture of duration, and then you’re trading vol against each other doing all that complicated stuff. It’s fascinating, but it’s also tough to explain. And it’s also tough to model many times.
So my pit trading was at the beginning at the Cboe, and then I went to London and traded Bund, Bobl, and Schatz on the phone and, in the call around market and on the screen. Then, when I came back to the United States, I started and ran an index options market-making business on the floor of the Cboe with three other partners. We ran that for about a decade, up until 2016 or so.
The interesting part about London is that it requires you to be very dynamic and malleable in how you are if you’re coming from a pit environment and you’re walking into a phone around market. There’s a learning curve there. You have to be able to interact with people on the phone and then hedge things on the screen simultaneously. Parts of that were challenging to pick up initially, but once you get the hang of it, it’s a dynamic and exciting place to trade. I worked hard for 15- and 16-hour days. Your schedule then was extensive. The Bund, Bobl, and Schatz options were open at 6:30 or so in the morning and closed at 6 p.m. It was aggressive, and then we did a bunch of entertaining brokers and things of that nature. So, I worked 24 hours a day and burned out quickly. But it was an incredible learning experience, and much of my success after that was primarily due to all the things I learned while in that environment. Pit trading was at the bookends of my career screen trading.
What was the interest in getting into index options around 2006?
We had no grand vision other than having a decent amount of collective experience in that marketplace. I had never traded index options in the pit then. All of my pit trading had been equity options at first, but the other partners in that firm were all index traders. Some started in the equities but then quickly moved to index options. So, we were leveraging a lot of experience there. The other exciting thing about the index pits, especially on the Cboe at the time, was that they were massive. So you had 300 people in these pits. In a pit where you have that many people, the spot where you trade or stand – the actual physical spot – is precious. And your proximity to a broker – hopefully, a very good broker – is even more valuable. So, some parts of the pit are valuable.
Additionally, in a large pit, identical items can be traded simultaneously at different prices in different parts of the pit. So you need what is called pit coverage. You need to be able to be involved in all of the places because things trade at different prices in different parts of the pit. Part of the natural, physical arbitrage in a pit of that size is just the fact that a call spread traded for $3.50 in one place, and it just traded for $3.25 in another, right? That part is interesting. Another part is modeling the volatility surface, which is more interesting. But the physical part of it can’t be underestimated. That part is essential as well. And so the people I partnered with had experience and spots. Some people had spots in the pit, which is a big deal when starting because it gives you a head start. You don’t have to break into every one of these spots to create a viable business model.
Graphic: The Volatility Surface. Retrieved from Investopedia.
Are pits still relevant today?
Absolutely. I think the industry broadly has been sounding the death knell of the pit for 25 years, and it’s not dead yet. You can only write a headline about how pit trading is dying so many times before you’re like, maybe it will never die.
You’ll see that you can find this sweet spot in that hybrid environment where one bolsters the other. They’re puzzle pieces that fit into each other in environments where things are exceptionally volatile. Screens sometimes need help to keep up with the size that needs to be executed at a price. Sometimes, that’s hard in a volatile environment.
People with long-term experience in volatile environments tell stories like, “I went to execute 25,000 call spreads on the screen, and I got 175 done.” That’s the horror story of the screen trade.
In a volatile environment, you might be able to walk into a pit with 100 people in it and say, “Hey, I need a size market on this thing; where can I get size done?” If I’m a market maker and I have a trade worth $5.00 and you tell me you need to sell 200 of it, I might buy it for $4.90 or $4.95. If you tell me you will sell 25,000 of it, I will just say, “All right, listen, you’re doing a massive amount of size. This incurs a significant amount of, like, carry risk for me. And just like strike risk and all kinds of other risks, liquidity risks. You want 25,000 of them done. You have to sell it for $4.50.”
Everyone in that pit trading environment may add, “Yeah, I’ll do 5,000 for $4.50” or “I’ll do 5,000 for five, right?”
Suddenly, you can get 50,000 contracts executed at $4.50, whereas, in another liquidity environment, like a screen, it’s tough to have that conversation with an algorithm. So, I’m not pooh-poohing one side or the other. There are benefits in a pit trading environment that you don’t have in a screen trading environment.
The other side is that the screen trading environment often does things better than the pit. It’s a puzzle-piece environment, and it can be exceptionally robust when you find the proper connection between the two. They can feed liquidity onto each other, which you see in environments like the Cboe, where weekly or daily index options are almost exclusively traded electronically. The reason is that they move so fast that a pit broker cannot keep up with quoting them how they need to be quoted. That is something that can only happen with an algorithm and a computer. And so that’s another side of this. That’s one thing the screen does well. These algorithms do exceptionally well. There are benefits for each one.
Graphic: Retrieved from Cboe Global Markets.
Who is on the other side, and does size change that? Additionally, are they the same persons warehousing the risk?
Let’s say you, and I are trading in the pit, and you are a broker, and you come in, and you say, “I have 500 of these to sell,” and I give you a price, and you decide to sell them all with me and say, “I’ll sell you 500, and then you walk out.”
In an old-school environment, I would say, “What’s your house?” or “What’s your give-up?” I would signal [a tent over my head].
What that means is what clearing firm are you giving up to me so that I can tell my clearing firm we need to meet that clearing firm and tell them, “Hey, we bought 500 of these for $2.50, and they sold us 500 at $2.50.”
Let’s say you give up 005, which was Goldman back in the day, and it might still be. I would write down your acronyms, 005, and that I paid $2.50 for 500 of them. You would do the same on your side, except you write down MKC 690.
That way, you know who’s on the other side. You can see the house. You can see where the clearing firm is coming from. That kind of vocabulary, or the same way those are designated, also exists electronically. Digging into the electronic record lets you see the house you’re trading with. However, the electronic trade has a much more anonymous presence. We’re not sitting face to face, and I’m not trading with you. I’m trading on the screen, and it just so happens that I traded $2.50 and I bought 500 of them, but 15 other people bought 500 of them also, and so you don’t have the same face-to-face interaction, but you still have the same amount of information about it, which is I paid $2.50 for it, and someone from 690 sold or someone from 005 sold.
As things become more and more electronic, they will become more anonymous because, in many ways, it doesn’t matter. You can’t keep track. If you’re trading 100,000, 200,000, or 400,000 contracts a day, keeping a mental note of who you’re trading with and what their house is is tough. However, generally speaking, the people on the opposite side of your trades as retail investors, if you’re selling five, ten, or 15 of them, will be the people just making markets in the regular scope of market making. That will be the case for most large market-making firms, constantly putting out tight prices and creating liquidity. If you are a much larger player and you’re doing something like selling 50,000 call spreads, it creates an event that people take notice of. You’ll see big prints hit the tape and then be disseminated by people like prime brokers and brokerage firms.
Part of what they’re doing is saying, “Hey, this hedge fund sold 50,000 call spreads through Goldman. Look at this trade. Do you want to sell it, too? You’ve got a bunch of money in your account.”
They’re utilizing the prints to create more volume for themselves. They get paid on volume. When the size of those prints increases, it doesn’t necessarily change the players involved. It changes the size in which they participate. It’s generally the same people involved in it, but it creates a situation in which people will take a little more notice of what happened.
Five 0 DTE call spreads expiring at the end of the day don’t hit the tape. 50,000 call spreads in DEC that trade in the SPX or the Bund, Bobl, Schatz, or whatever create an event where people will be like, “Oh, what happened there, and at what price did it trade? Where were futures when they traded? What is the vol level that this creates?”
You have a situation wherein someone is short a bunch of vol from a point, and people start to do all kinds of things with large prints because they like to keep track of big players who have positions that might unwind.
Why would you keep track of that?
If someone came in and sold 50,000 call spreads, they might need to go in and repurchase them at some point. And if you can be the person who bought the last 500 lot of the 50,000 lot and then be the person who is the last person on the print on the sell side when they come back to repurchase it, you’re going to be the person who probably makes the most amount of money for the commensurate amount of risk that you took. So that’s the puzzle that everyone’s trying to put together.
You get a little information about who’s trading, but you’ll never know the exact person you’re trading with. As things become more electronic, they will become more anonymous.
What do you get in exchange for taking on the other side of trades?
If you’re a market maker, you have theoretical values for every option on the board. Your model is telling you this option’s worth $4.00, or this option’s worth $1.50, or whatever. You have created an edge if you can buy that option with a theoretical value of $4.00 for $3.90. You have $0.10 of edge to buy however many you purchase.
Generally speaking, if you do the math, $0.10 of edge maybe $1,000 in an SPX product. That’s the edge relative to your theoretical value. Here’s the hard part: when you pay $3.90 for something worth $4.00, especially now, you immediately move your theoretical value to $3.90 because you just paid $3.90, hoping the next person who comes in sells it at $3.80. You get to buy more for a lower price, or in the perfect world, they pay $4.00; you bought it for $3.90 and sold it for $4.00. That’s the ultimate. But that essentially never happens anymore. It used to be in 1999 all the time.
So, you’re paying $3.90, moving your theoretical value to $3.90, and then constantly moving things around to capture the bid-ask spread. Suppose you’re continually buying on the bid and selling on the offer. If the spread is a dime, and you manage your risk correctly, you will make a dime as many times as you trade.
What you’re doing is providing liquidity, a.k.a. prices, into the marketplace in exchange for theoretically harvesting the bid-ask spread on any number of millions of options that trade. It’s more complicated than it sounds because it’s an incredibly complex ecosystem. The landscape is much more complicated than ever, and people are constantly hedging things in different and innovative ways.
Whereas when we used to trade Xilinx, that semiconductor company I traded in 1999, we wouldn’t even trade monthly options against each other. It was just because people were so simplistically putting on bets. “I want to buy the DEC 100 calls,” and we would sell you the DEC 100 calls. People weren’t coming in and saying, “How’s the DEC 100/120/140 call fly versus the JAN 90/110/130 call fly?” That never happened. Right now, not only does that happen because people are rolling positions and doing all this stuff, but it also occurs electronically via a broker. That didn’t happen then. The landscape has changed, but the actual market-making process hasn’t changed. It is the margins that have changed.
Remember, if you’re buying, you’re paying the offer. If you’re selling, you’re selling the bid. I buy the bid and sell the offer if I’m the market maker. That’s where my edge comes in as a market maker. But think about this if you’re a market maker: if the bid-ask spread continues to narrow, which it has over the last 20 years, which is good for the retail investor, that means that there is margin compression happening on the market-making side is getting more and more and more aggressive. You must be more aggressive and competitive to participate in the marketplace. No one is writing sad songs for market makers. They’re doing just fine. They always have been, and they always will be. But it’s important to understand that when you see margin compression like that, it’s a force that has knock-on effects on profitability for people trading the options as market makers.
Graphic: Retrieved from thinkorswim.
What’s your role at the OCC, and how did you join them?
I joined OCC almost three years ago now. I never really thought I would have a career in education, but when I look back on it, I was always positioned that way without knowing it. And so this is a very natural outgrowth of my career thus far.
My title says that I’m the Principal of Investor Education at OCC, meaning I work for the Options Industry Council (OIC), a non-profit educational arm inside the OCC. All we do is provide educational resources about options to the broader public. We do this as a free service because OCC fully funds us. The OCC is the Options Clearing Corporation. Anytime you’re trading an option in the United States in an index or equity, it’s going through the hands of OCC in some sort of centrally cleared and settled option marketplace. For that, the OCC charges a variable rate. I think it’s $0.02. And that pays for all the operational expenses in an organization of that size where we’re now clearing over 12 billion contracts a year. You can do the math there and know that the operational expenses and the operational budget are significant, but so are the responsibility and the amount of risk management that has to occur to maintain a smooth and functioning marketplace that doesn’t have a bunch of hiccups in it.
The OCC is the foundation for secure markets in the United States.
My job is to educate people about the risks and benefits of exchange-traded options. I do that through many different methods, including things like this. I go out, and I do interviews and YouTube videos. I appear in places and speak publicly about who I am, where I’ve come from, and how I’m leveraging that expertise to get the message out about how options work more broadly. My job is to teach people how options work. It helps to lean on my experience as an options market maker to tell people stories about how things work and what can go wrong.
The idea behind what we do is that we teach about the risks and the benefits of exchange-traded options. I’ve experienced both of those many times, right? And I can tell you many stories about how those things work. Sometimes those stories effectively get the point across to say to someone, listen, “This is how puts generally work, but sometimes you have to be on the lookout for a situation in which this happens,” right? “This should be on your risk radar as a potential outcome for a strategy like this. I can’t guarantee how it will play out, but let me tell you a story,” right? “Let me tell you one example of how I was in a situation where this happened, and this is how I dealt with it, or this is one of those situations where I got my teeth kicked in. I’m not saying this is exactly how it will play out, but it’s a potential way it plays out. Or, the other way is that things might go exactly as you expected.”
Eventually, my job is to figure out the best way to give people that moment where the pieces click and they understand how optionality works. You know, at the beginning of this conversation, you and I talked about how you were reading these incredibly complex books, and that’s amazing. Still, I would also say when you’re first learning how options work, sometimes the best way to learn how they work is to trade them in a paper account and get smoked a couple of times on paper without actually using capital because you’re never going to learn.
The lessons I’ve learned best are where I’ve lost the most money. And so, everyone has a different aha moment where they’re looking at all this option stuff and saying, “Okay. Calls are supposed to go up when stocks go up, and puts are supposed to go down. And I kind of get that. But why is this one not going up?” And often, people have difficulty understanding because options are so variable. There are so many strikes and different strategies. My job is to try to distill all that information into digestible pieces of transmittable information and say, “This is how these things generally work. Take this knowledge and then build on it.” So that’s what we do at the Options Industry Council. We do that from our website, optionseducation.org, and host an entire educational resource suite. They are broad and exceptionally robust. It’s a fantastic resource and free.
I spent all that time reading, but it only clicked once I started doing it. One thing that helped was taking a small amount of capital and testing trades in real time. It was incredibly informative, and I would like to know if that resonates.
That resonates. It’s something that I think is a vital part of the learning curve, and when people ask me whether or not they should start by paper trading, my response is always, “Well, it can’t hurt. Why not try it without risking actual capital first?” And if you can find the ability to do that at your brokerage or your clearing firm or whatever, it’s a fantastic way to dip your toe in the water and figure out if optionality is for you. Maybe it is not. It’s incredibly variable.
As an options educator, I can help people understand that some aspects of optionality are for everyone in some way, shape, or form. But maybe it’s not how your dentist told you they bought call spreads. Perhaps that’s not for you.
When he started, I had a friend who took 80% of his capital and traded it the way he thought it should be, and then 20% of his capital was put on the same trades in the opposite direction. He said the amazing part was to look at the P&L of those two accounts next to each other and that the combination of those two numbers was instructive as to why things were happening the way they were. Often, when you have something on and it loses money, it’s hard to figure out why it lost money. Like, “The stock did exactly what I thought it would do. Why did the option part of this lose money?” And his thought was, I’ll be able to figure it out a lot easier if I have it on the opposite way over here, and I can just go look at it and be like, “Oh, this part made money, and this part lost.” The combination of both helped him. I’m not advocating that strategy, but thinking about it that way is interesting, especially when you’re starting and learning how options work.
The options markets have grown tremendously, with shorter-dated options receiving much of that interest. Is that risky?
When discussing options, I tell almost everyone this, especially when they ask whether 0 DTE options are for them: the optionality itself is the most essential part to understand. Once you know the optionality and have that kind of aha moment, it becomes easier to say the optionality reacts to time this way and extrapolate it out in time, then extrapolate it in time toward expiration and away from expiration. An option should take on the qualities of a Vega-rich option as you add duration to it. Another should take on the qualities of a more Gamma-rich option as you take time out of it and move it closer to expiration. If you understand the actual inherent optionality that exists there, you’re going to be so much better prepared to be able to make decisions that are based on an exact understanding of the inherent optionality, which is really what you need if you’re going to be using options like that.
The one thing that I will say about 0 DTE options, and the move towards shorter-dated options in general, is that there’s a lot of financial journalism focused on what we would call systemic risk that might be part of that rotation into shorter-duration options. My response is that every option that has ever existed has, at one point in its lifetime, been a 0 DTE option on the day it expires. This is not a new concept. This is not something that someone cooked up in a lab. This is a change in the actual cadence of expirations. And, if you look at a lot of the stuff that I put out on LinkedIn, especially about 0 DTE options, it’s taken 15 years to get to the point where we have an expiration that happens every day. And the Cboe did it in chunks.
It’s not a new risk. It’s a different cadence of risk. That’s an important distinction.
The more you understand the basics of optionality, the better you’ll understand the shorter-dated and longer-dated options. Sometimes, shorter-dated options can’t do what you want them to. You can’t get real Vega in a shorter-dated option. An option with two days left has less Vega; it will not respond to implied volatility changes as well as an option with 200 or 100 days.
If you’re looking for exposure to implied volatility moves, you can’t get that in a two-day option.
Duration is a spectrum; like anything else, it involves a mixture of risk and benefit. But to understand both sides, you must understand the basics of optionality.
The Cboe adds that much of this short-dated exposure is balanced. It’s just a one-day exposure, and it can’t be anything more because it just rolls off at the end of the day.
Since we’re running out of time, I’ll end with your best or worst trade. What was it, and what did it teach you?
The worst was an exceptionally high variance trade that risked about 80 to 90% of the firm’s capital and was put on at an unusually illiquid time during the cycle. We carried it over Christmas when everyone is wherever they are; when you remove players from the marketplace, things can be weird because fewer people are on cash arb desks to keep things in line, like indexes with cash components and actual stock components. If you take all the people out of that trade that usually would trade it and keep it in line, that thing can do really weird things and print in very strange directions.
From that, I learned risk management has many different elements.
You need to keep many other things on your radar, and liquidity is one of them, right? Volatility is another one. Cash management is a massive part of it, too. How much of my account have I invested in this one trade? Am I too concentrated on something that might be exceptionally volatile? That’s an essential part of risk management as well.
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This week’s letter begins with an overview of reflexivity. Many works exist on this topic, with “The Alchemy of Finance” summarizing it well. Written by investor George Soros, it concludes that markets are often wrong, and biases validate themselves by influencing prices and the fundamentals they should reflect.
Namely, reflexivity is this feedback loop between participants’ understanding and the situations they’re participating in. Sometimes, these feedbacks manifest far-from-equilibrium prices. Think of the connection between lending and collateral value, selling stock to finance growth in the dot-com boom, leaning on cheap money to make longer-duration bets on promising ideas, or the success of volatility trades increasing the crowd in volatility investments, be this dispersion or options selling ETFs.
Graphic: Retrieved from Nomura Holdings Inc (NYSE: NMR)
Perception begets reality, with these far-from-equilibrium conditions reinforced until expectations are so far-fetched they become unsustainable. Sometimes, the corrections become something more, with self-reinforcing trends initiating the opposite way.
Enron creatively hid debt from its balance sheets, guaranteeing it with its stock. When the stock fell, it revealed financial misdeeds, contributing to a broader market downtrend, bankruptcies, and corporate scandals.
FTX brought itself and some peers down when withdrawals revealed a billions-large gap between liabilities and assets.
Volmageddon climaxed with the demise of products like the VelocityShares Daily Inverse VIX Short Term Exchange-Traded Note (ETN: XIV) after a sharp jump in volatility sparked a doom loop; to remain neutral, issuers rebalanced, buying large amounts of VIX futures, which propelled volatility even higher and sent products like XIV even lower.
Graphic: VelocityShares Daily Inverse VIX Short Term Note (ETN: XIV) retrieved from investing.com.
The expansion of such trades increases liquidity, sometimes making assets appear more liquid and money-like stores of wealth. This may also stimulate economic growth. Likewise, the contraction or closing of these trades can lead to a sudden reduction in liquidity, negatively impacting the economy and market stability.
“The Alchemy of Finance” identifies a recurring asymmetric market pattern of slow rises and abrupt falls. Additionally, if market prices accurately reflected fundamentals, there would be no opportunity to make additional money; just invest in index funds.
Further, we continue to see interventions to stabilize markets, and they encourage further distortion and misdirection of capital. Often, such interventions are blamed for benefitting wealthy investors most and increasing inequality. As explained in works like “The Rise of Carry: The Dangerous Consequences of Volatility Suppression and the New Financial Order of Decaying Growth and Recurring Crisis,” monetary authorities and regulators’ interventions reinforce scenarios of deteriorating economic growth, more frequent crises and less equality and social cohesion.
We’re getting off track, but the point is that the conclusions and approaches outlined in “The Alchemy of Finance” are captivating. Soros sought to understand markets from within without formal training, access to unique information, or his being math savvy; instead, he attempted to connect deeply with markets, assuming they felt like he did and he could sense their mood changes.
“We must recognize that thinking forms part of reality instead of being separate from it,” he explains. “I assumed that the market felt the same way as I did, and by keeping myself detached from other personal feelings, I could sense changes in its mood, … mak[ing] a conscious effort to find investment theses that were at odds with the prevailing opinion.”
We apply this understanding of the market’s mood in our best way here. Our long-winded analyses of everything from technicals to positioning and, increasingly, fundamentals and macroeconomic themes give us a holistic understanding of what’s at stake, whether self-reinforcing trends exist, and whether to adjust how we express ourselves.
Let’s get into it.
The Great Rotation
Last Thursday, an update on consumer prices showed US inflation cooling to its slowest pace since 2021. Accordingly, traders began pricing the news and buying bonds in anticipation the Federal Reserve may cut its benchmark rate by ~0.75% this year.
Graphic: Retrieved from CME Group Inc’s (NASDAQ: CME) FedWatch Tool. SOFR is a check on market conditions and expectations regarding short-term interest rates.
Optimism about lower interest rates prompted investors to shift from the previously favored large-cap tech, AI, and Mag-7 stocks into riskier market areas and safe-haven assets like gold, reflecting concerns about a potential dovish mistake. The Russell 2000 (INDEX: RUT), an index of smaller companies, outperformed the Nasdaq 100 (INDEX: NDX) by one of the most significant margins in the last decade. Despite the S&P 500 (INDEX: SPX) declining by nearly 1%, almost 400 components recorded gains.
Graphic: Retrieved from BNP Paribas (OTC: BNPQY) Markets 360.
With these underlying divergences, committing capital to bearish positions is challenging. Breadth strengthened with more volume flowing into rising stocks than falling ones. This wouldn’t happen in a sell-everything scenario, explaining the hesitation to sell.
Graphic: Market internals as taught by Peter Reznicek.
The outsized movement observed isn’t surprising as it aligns with the narrative we shared earlier this year.
While individual stocks are experiencing significant volatility, indexes like the S&P 500, which represent these stocks, show more restrained movement. For example, after Thursday’s sell-off, despite its large constituents like Nvidia Corporation (NASDAQ: NVDA) weakening, the S&P 500 firmed.
Here’s a chart to illustrate.
Graphic: Retrieved from TradingView. Nvidia versus the S&P 500, with the latter in orange.
Among the culprits, investors have concentrated on selling options or volatility (the all-encompassing term) on indexes, and some of this is used to fund volatility in components, a trade (considered an investment by some) known as dispersion.
The trade is doing well in this environment, with Cboe’s S&P 500 Dispersion Index (INDEX: DSPX) jumping to a one-year high. Dropping realized volatility (i.e., volatility calculated using historical price data) and a widening spread between stock and index implied volatility (i.e., expectations of future volatility derived from options prices) validate this trade’s success, reports Mandy Xu, the Vice President and Head of Derivatives Market Intelligence at Cboe Global Markets (BATS: CBOE).
Graphic: Retrieved from Cboe Global Markets’ (BATS: CBOE) Mandy Xu.
“The market has been broken up into two groups: 1. Nvidia and Magnificent 7; and 2. The other 493. The correlation between those two groups has been low, which has pressured S&P 500 correlation,” explained Chris Murphy, a derivatives strategy co-head at Susquehanna. “When looking at S&P stocks on an equal-weighted basis, the outsized impact of the MAG7 as a group and NVDA specifically is neutralized.”
Understanding correlation is critical to grasping the pricing dynamics between index options and their components and trading volatility dispersion. When counterparties (our all-encompassing term for the dealers, banks, or market makers who may be on the other side) fill their customers’ options sales in the index, they may hedge by buying the index as its price falls and selling when it rises, with all other conditions remaining the same. Consequently, trading ranges may narrow, with realized volatility also falling.
To explain visually, see immediately below. Movement benefits the counterparty’s position. Hedging may result in trading against the market, selling strength, and buying weakness.
This effect may be less pronounced or absent in single stocks, which do not experience the same level of this supposed volatility selling; instead, there is more buying, and the opposite occurs. Movement is a detriment to the counterparty’s position, with all else equal. Hedging may result in trading with the market, buying strength, and selling weakness. This can reinforce momentum and give trends a lease on their life; hedging can help sustain and extend market movements rather than neutralize them.
Together, as counterparties align the index with its underlying basket through arbitrage constraints, its volatility is suppressed, and the components can continue to exhibit their unique volatility—the only possible outcome is a decline in correlation. If the index is pinned and one of the larger constituents moves considerably, the dispersion trader may make good money in such a scenario.
Graphic: Retrieved from Bloomberg.
We now see large stocks starting to turn and lesser-weighted constituents in the S&P 500 firming up, picking up the slack. For instance, Nvidia traded markedly higher immediately after its last earnings report, and the S&P 500 was unfazed. Something is giving, and those constraints we talked about keep things intact.
The rotation, in and of itself, is healthy, giving legs to and broadening the equity market rally. It’s just that it’s happening with the most-loved stocks being severely overbought.
Graphic: Retrieved from BNP Paribas.
Should interruptions continue across large-cap equities, souring speculation on further upside, a broader turn and outflows may manifest. The market’s gradual shift into a higher implied volatility environment, notwithstanding direction, may aid in any such unsettling, feeding into a higher realized volatility.
Building on this point, we observe a shift in S&P 500 call options before last Thursday’s steep decline. Implied volatility rose with the S&P 500. SpotGamma indicates this is partly the result of demand for SPX call options as traders seek synthetic exposure to the upside in the place of stock. This “SPX up, SPX vol up” pattern is unusual and typically happens near the short-term tops.
Graphic: Retrieved from Bloomberg via Danny Kirsch, head of options at Piper Sandler Companies (NYSE: PIPR).
SpotGamma adds that the pressure on individual stocks that followed last Thursday stemmed from significant selling of longer-dated calls in the tech sector, a last-in, first-out (LIFO) phenomenon. In other words, those late to the party are the first out!
The counterparts on the other side of this trading potentially (re)hedge this by selling stock.
Graphic: Retrieved from SpotGamma.
However, with call selling, the chances of sustained follow-through are significantly lower. Put buying, which was less prevalent, changes this dynamic.
In the case of a prolonged downturn, equity put buying is the key indicator we would watch for, along with deteriorating market internals such as breadth, as analyzed earlier. We want to see traders committing more money to the downside at lower prices, and increasingly so, as prices drop and the range expands downward. That’s what market and volume profiles can help with!
The fundamentals don’t necessarily support the case for some disastrous downside, though.
A dovish Fed can be good for risk as it’s seen as preemptive, BNP Paribas (OTC: BNPQY) shares. Or, a dovish Fed could suggest a coming deceleration. In any case, long-term interest rates will be least sensitive to any change, a negative implication for capital formation, growth, and equity returns.
The Summer Of George
Kai Volatility founder Cem Karsan uses this Summer of George Seinfeld reference to describe the current market. During the summer months, there is insufficient liquidity to overwhelm the market’s current position.
Graphic: Retrieved from Bloomberg via Michael J. Kramer.
We know the SPX volatility risk premium is near its highs this year. The Cboe, itself, shows the implied-realized volatility spread widening to 4.5% (96th percentile high).
Implied volatility is low, but not cheap. Consequently, short-leaning volatility trades mentioned in this document remain attractive.
At the same time, however, there’s still a ton of volatility protecting investors against downsides owned below the market.
To quote QVR Advisors, there’s “too much supply of front month call selling and too much buying demand for longer-dated puts.”
“This trade flow is contributing to a large and growing structural dislocation which is not compensating ‘insurance sellers’ (i.e., near-dated call and put writers) and is overcharging in implied volatility terms, buyers of insurance (i.e., long-dated puts).”
Taken together, the implications are staggering. With calm and falling realized volatility, there may be some counterparty re-hedging. This may consist of buying stocks and futures and supporting markets where they are.
Let’s break down some of the trades to understand better.
Consider yourself a customer who owns 100 shares of the SPRD S&P 500 ETF Trust (NYSE: SPY). You’re traveling to Europe and want to hedge your position against the downside. So, you wake up one morning, go online, and tell your broker you want to buy one at-the-money 50 delta SPY put option.
The delta is terminology for how that option’s price will change based on a $1 change in the underlying. In this case, for every $1 move up/down, the option will change in value by $0.50. Delta is also used to estimate the likelihood of an option expiring in the money. For example, a delta of 0.5 suggests there is approximately a 50% chance the option will expire in the money. There’s also gamma, the second derivative of how the option’s price changes with underlying changes, but we won’t discuss that further.
With your 100 shares hedged, if the market goes down, you don’t mind. You’re hedged, after all!
Naively, we’ll say this trade wasn’t paired up against another investor’s; instead, some mysterious counterparty will warehouse this risk. These mysterious persons want nothing to do with the directional risk of your trade. They’ll hedge by selling 50 SPY shares (i.e., 100 × 0.50). Again, we’re naive here and don’t consider their potential to offset this risk with other positions they may have.
You check your phone after a while and find that SPY hasn’t moved much. Your 50 delta put is now 20 delta. Bummer! You shrug, turn off your phone, and hit the beach.
What happened to that mysterious counterparty on the other side of this trade, though? They bought back 30 SPY shares, supporting the market and reinforcing the trend!
Though this is a naive take, it may help.
Reality Is Path-Dependent
Your and the counterparty’s actions partly shaped the SPY’s price movement. You bought puts, setting off a chain of events. The counterparty hedged, the market didn’t move, and the hedge was unwound. This only serves to support the SPY further.
“There’s skew in the market, which ultimately forces a buyback of stock by dealers, market makers, banks, etc., every day, and it accelerates into expirations,” Karsan elaborates.
“When the market’s up, there’s a buyback and a momentum re-leveraging, … forcing more buying.”
As we approach the end of summer, things change. Among other things, elections are coming, and there will be some hedging of that. With months to go, broad market hedges against a sudden downturn have appeared generally inexpensive, with three-month puts protecting against a drop in the S&P 500 near their lows. See the dark blue line in the graphic below as an example!
“The high dispersion of stocks has contributed to weighing on VIX,” shares Tanvir Sandhu, chief global derivatives strategist at Bloomberg Intelligence. “If the equity market breath improves then that may weigh on volatility, while a pullback in mega-cap tech stocks could see both correlation and index volatility rise.”
In fact, excluding NVDA, the VIX hit traded into the 9s, on par with 2017 lows.
Graphic: Retrieved from Bloomberg via Michael Green.
SpotGamma adds that we are in the second longest stretch without an SPX 1-day 2% move up/down; traders aren’t committing capital to bets on big moves, either.
We see this in spot-vol beta, which refers to the relationship between the market (which we refer to as the “spot” here) and changes in its volatility over time or volatility’s sensitivity to market trading.
This spot-vol beta has been depressed.
In observance, Nomura Cross-Asset Macro Strategist Charlie McEligott states there’s limited potential for volatility to decrease further, particularly with the SPX 1-month implied correlation at historically low levels.
To that point, “the historically low spot-vol beta we are seeing now will eventually be followed by historically high spot-vol beta,” the Ambrus Group’s co-CIO anticipates.
Graphic: Retrieved from Nomura. A weak spot-vol beta historically leaves stocks going nowhere.
The case is less so valid with more actively traded shorter-dated options. According to Simplify Asset Management’s Michael Green, the sensitivity remains. You just have to look elsewhere.
Shorter-dated options are less exposed to changes in implied volatility; instead, they expose one more directly to movement or realized volatility. They can be more attractive to hedge with but can cause problems and amplify wild swings in rare cases.
Graphic: Retrieved from JPMorgan Chase & Co (NYSE: JPM).
If news shocks the market one way, movements may exaggerate when traders scramble to adjust their risk, as discussed below.
Though that’s usually not a worry, as Cboe puts, according to Karsan, a dwindling supply of margin puts, especially those with high convexity and far out-of-the-money, would be the indicator to watch for impending exaggerated movement. These options, particularly if shorter-dated, are crucial during market stress, serving as indicators and drivers of potential crashes when traded in large sizes (e.g., 5,000-10,000 0-DTE options bought on the offer to hedge).
As a counterparty, you may also use similarly dated options to hedge yourself, bolstering a reflexive loop!
Again, the reality is path-dependent! The path leading to this point—low correlations and reduced availability of those protective options—sets the stage for increased volatility.
Here, we wish to emphasize the convexity component—gamma or the rate at which the delta changes with the underlying asset’s price—rather than the likelihood of the underlying asset reaching the options’ strike prices. Just because an option turns expensive doesn’t mean it is likely to pay at expiry; instead, it may have value because that’s precisely what traders need to trim their margin requirements during volatile markets.
“Implied vol is about liquidity. It isn’t about fear or greed,” writes Capital Flows Research.
“Implied vol is about liquidity on specific parts of the distribution of returns on an asset. Remember, even the outright price of an asset is pricing a distribution of outcomes, not a single destination. Options make this even more explicit by having various strikes and expirations with differing premiums and discounts.”
History shows a minor catalyst can lead to a big unwind. Take what happened with index options a day before XIV crash day.
“Going into the close the last hour, we saw nickel, ten, and five-cent options trade up to about $0.50 and $0.70,” Karsan elaborates. “They really started to pop in the last hour.”
“And then, the next day, we opened up, and they were worth $10.00. You often don’t see them go from a nickel to $0.50. If you do, don’t sell them. Buy them, which is the next trade.”
New rules surrounding the collateral traders must post to trade can only amplify a bad situation, “potentially leading to premature and forced hedging as volatility increases,” The Ambrus Group writes.
“Because everyone has to put down more capital, you have to disallow people from trading down there in a way that you don’t have to now,” JJ Kinahan, president of Tastytrade, says.
The opposite can happen when markets move quickly higher. Take the options activity and price action in the Russell 2000 over the last week. Volatility skew, or the difference in implied volatility across different strike options, steepened accordingly.
Graphic: Retrieved from Bespoke Investment Group via Bloomberg.
Typically, options with farther-away strike prices have higher implied volatility than options with closer strike prices. When the skew steepens, the disparity in implied volatility between these various strike prices widens.
Depending on the steepening, we may have insight into the type of impending velocity and trade accordingly.
For instance, the implied volatility of out-of-the-money (OTM) calls, which offer protection against market upturns, rises significantly compared to at-the-money (ATM) calls and downside protection (puts). This steepening volatility skew indicates heightened enthusiasm among investors regarding potentially large upward market movements.
The steepening call volatility skew below results from distant call options pricing higher implied volatility than usual due to investor demand. Beyond helping understand the market’s thinking and mood, it can serve as a catalyst, with call options buying into a price rise further accelerating movement indirectly by how the other side hedges this risk (i.e., they buy stock to hedge).
Graphic: Retrieved from SpotGamma.
This action is apparent elsewhere, too, in the S&P 500 (as can be seen via the SPX cross-sectional skew graphic from Cboe above), where it’s proving quite sensitive, as well as single stocks like NVDA and Super Micro Computer Inc (NASDAQ: SMCI). We provided examples this year where steepening call skew helped reduce the cost of trades we used to capture the upside. In one case, we removed SMCI butterfly and ratio spreads for tens of thousands of percent in profit (e.g., $0.00 → $10.00)!
Graphic: SMCI volatility skew in February, relative to where it was (shaded) in recent history before that.
Market Tremors
This week’s market tremors are affecting some of the most loved areas of the market, and a flattening skew (e.g., green line versus grey line below) alludes to further potential for pressure.
Graphic: Retrieved from SpotGamma.
In the long term, a few things stick out, including high interest rates and a stronger dollar, which create macroeconomic problems.
A few explain it better than we do. Higher US interest rates relative to other economies can result in outflows and stress. Just look to places like Japan, where there’s been a lot of currency volatility. If the dollar’s strength continues, it could lead to crises elsewhere, creating a ripple effect and priming potential volatility at home.
“A US Dollar devaluation will then be a tailwind to S&P 500 earnings, which would be positive for stock prices,” Fallacy Alarm summarizes. “However, an unwinding carry trade also causes deleveraging, which is typically not good for asset prices.”
May this upset popular trading activities and catapult something minor into something more?
Sure, and the current low correlation and implied volatility mean that any considerable market disruption could have a substantial impact. Still, markets are intact and likely to stay so.
“If we continue to grind higher, options will get cheaper and cheaper on their own accord. Not to mention all the vol selling that’s getting them to a point which is even cheaper, at some point,” Karsan adds. “And the acceleration generally in those things becomes on the upside, the realized volatility on the upside gets to be just too big relative to the implied, which means it becomes profitable for entities to come in and start buying vol at these lower levels. Add to that, the vol supply is likely to dissipate a bit as we get into September, October, and November. Why? We have an election sitting there.”
So, as the market moves higher, it transitions into this lower implied volatility, reflected in broad measures like the VIX. If the VIX remains steady or higher, “that indicates that fixed-strike volatility is increasing, and if this persists, … it can unsettle volatility and create a situation where dealers themselves … begin to reduce their volatility exposure,” naturally buoying markets as previously outlined. If there is greater demand for calls, counterparties may hedge through purchases of the underlying asset, a positive.
If The Music’s Playing, Get Up And Dance
With volatility at its lower bound, at which it can stay given its bimodality, it makes sense to look at markets through a more optimistic lens. A lot is working in its favor, and if near-term declines are marginal and not upsetting to the status quo, it may set the stage for a rally through elections.
Accordingly, how do we make positive returns in rising markets and minimize losses or gains in flat-to-down markets as we have now? That’s the goal, right?
For the anxious and must-trade types, short-dated (e.g., 50- or 100-point-wide and 0-1 DTE) butterflies in the NDX worked well on sideways days. Here, we’ve tried to double and triple our initial risk but can easily hit more in benign markets. For the passive types, calendars may do just as well should the realized volatility keep where it is or fall relative to what is implied.
In anticipation of this week’s controlled retracement, we initiated wide (e.g., up to 2,000-point-wide) broken-wing butterflies and ratio spreads on the put side in the NDX, reducing their cost basis, if any, with the credits from the short-dated fly trades, among others. Into weakness, those spreads now price a few thousand percent higher, and we’re monetizing them, intending to use the credit to finance trades that capture upside potentially or to reduce our stock cost basis.
“And I think this is one of the arguments for going with VIX calls, not that we’ve seen anything explosive yet this year, but if we do see some of these things unwind, you’re going to get a kicker there where you might see the VIX cruise very quickly up to 45, and it probably won’t stay there unless there’s a real good fundamental reason for that to happen,” explains Michael Purves, the CEO and founder of Tallbacken Capital Advisors. Josh Silva, managing partner and CIO at Passaic Partners, adds, that “when there is a liquidation, it’ll be hard, it’ll be fast and it’ll be dramatic.”
“Typically, the market after that is pretty awesome.”
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Bursts of volatility punctuate calm and resilience, resulting in demand for safety and protection in everything from stocks and commodities to bonds and currencies. The general agreement is that macroeconomic policy and geopolitics are to blame, and investors are repositioning to stem risk and potential bleeding in their portfolios. This sometimes disturbs historical trends and relationships.
Thank you for tuning in. We will unpack much of it herein. Let’s get into it.
Hedging Against Monetary Inflation, Weaponized Dollars, And Debt Monetization
Gold serves as a prime example. Instead of being guided by conventional catalysts, including real interest rates (i.e., nominal interest rate minus inflation), growth prospects, and currencies like the dollar, recent movements are more likely driven by factors like central bank accumulation on macroeconomic and geopolitical shifts.
For instance, China may increase its gold reserves to hedge potential disruptions and sanctions, as Russia saw after it invaded Ukraine in February 2022, or establish a collateral reserve for an autonomous financial system. Likewise, Poland, the Czech Republic, and Singapore have also increased their gold reserves.
As liquidity in the gold market is thinner, this buying activity amplifies volatility and disrupts established trends. Therefore, fast moves up!
The typical trajectory is guided by monetary inflation, characterized by increasing liquidity within the financial system. According to CrossBorder Capital, gold moves 1.5 times the liquidity growth, a solid sensitivity to so-called monetary expansion. Bitcoin, often considered a digital gold, moves sooner and exhibits higher sensitivity.
Recent expressions of interest in Treasury securities by central banking authorities, such as Federal Reserve Governor Christopher Waller, further fuel ascents. New demand would lead to higher bond prices and lower yields.
Therefore, gold continues surging due to geopolitical shifts, liquidity in the financial system, and the potential for debt monetization. The latter occurs when excessive debts prompt central bank authorities to intervene, using printed money to purchase bonds to manage interest rate levels more effectively.
“Investors are looking beyond the ‘here and now,’ realizing that there is no way markets or the economy can sustain 5% nominal and 2% real rates,” Bank of America elaborates. Investors are “hedging two things: i) the risk that the Fed cuts as CPI accelerates, and ii) and more ominously, the ‘endgame of Fed Interest Cost Control (‘ICC’), Yield Curve Control (YCC) and QE to backstop US government spending.’”
There is bi-modality. Typically, high rates are bad for gold. But, with debts and rates as they are, the probability of debt monetization increases. For now, we have a cycle wherein stocks and commodities may rise with a firming economy, and bonds may offer limited salvation, nodding to higher-for-longer rates.
Hedging Loss Of Momentum And Left Tails Following Big Move-Up And De-levering
Interest rate increases are likely only on the horizon if something unexpected occurs. Given that stocks are priced well, the question arises: how can we protect ourselves while many anticipate, based on market pricing, either minimal changes to the status quo or a substantial event triggering a broad downturn?
For one, commodities don’t do much good in a broad downturn.
Consider the years 2001 (during the tech bubble), 2008 (amidst the global financial crisis), 2015 (the flash crash), 2018 (during Volmageddon), and 2020 (amidst the pandemic). According to Kris Sidial of The Ambrus Group, gold was an ineffective hedge against equities during these periods.
So, how do we hedge the middle reality between “minimal” and “substantial.”
The first idea involves hedging downside thrusts in equities via call options in the Cboe Volatility Index (INDEX: VIX), Goldman Sachs, and UBS note. This isn’t necessarily optimal. Volatility is high over the short term and may revert quickly, indirectly boosting stocks. The alternative strategy entails selling options and utilizing the funds to purchase similar options with later expiration dates. Such digestion trades enable traders to capitalize on increases in volatility in the near term, reducing their costs on longer-term trades.
Graphic: Retrieved from SpotGamma’s April 15, 2024 Founder Note.
To explain, in a recent letter to subscribers, SpotGamma shared that numerous expiring VIX call options were in the money. In other words, this exposure, which makes money if the VIX and S&P 500 implied volatility (or the options market’s anticipation of future movement in the underlying), was soon to disappear. Accordingly, the hedges to this exposure would do the same, and the rebalancing after that would be enough to buoy markets.
We’ll try to break it down further in the simplest way possible.
The S&P 500 (INDEX: SPX) and VIX are inversely correlated. When the S&P 500 falls, the VIX tends to rise. Naive of us to say, we know, but bear with us.
One can buy an SPX put or a VIX call to hedge a portfolio’s volatility. Let’s say one buys an SPX put, and the other side of this trade sells an SPX put. The other side may hedge this short put by selling stock and futures correlated to the S&P 500. Let us say the S&P 500 falls and volatility rises (pictured below). That counterparty may have to sell more stock and futures, pressuring markets. If this now valuable put expires, the counterparty will buy back the stock and futures it sold. This can support markets or do less to exacerbate movement and underlying volatility.
SpotGamma’s data suggests the markets are not facing an impending crash; instead, per their April 17, 2024 note, “if stocks rally and IV drops, it may add more stock for dealers to buy.” I plug SpotGamma because I worked there. Check them out!
Graphic: Retrieved from SpotGamma.
So, calendar and unbalanced butterfly or ratio spread trades (pictured naively below), a play on the recent richness (pictured much further above) of options, may help capture the low case of downside and stem potential portfolio volatility.
Graphic: Retrieved from Physik Invest.
Flipping these trades (i.e., using call options in the SPX instead of put options) allows one to play the market rising. For instance, let us say the upside of gold and silver will continue, but only after stopping and digesting recent movements. You can sell a call expiring soon and buy one later at the same strike price. Your loss is, technically, limited to the amount paid for the trade.
Graphic: Retrieved from Schwab’s thinkorswim platform.
In general, ratio spreads, and butterfly trades are designed to capitalize on movement toward specific price levels, while outright calls and puts are better suited for hedging sharp movements.
The former two strategies serve as practical tools for safeguarding the value of your positions during periods of heightened volatility. In such environments, the options you own are positioned closer to the market, usually retaining their value well, while the options you sell are priced higher than usual and located farther from the market, with more value to decay into expiry.
Consequently, while the options you own tend to keep their value, the options you sell struggle to retain theirs. As a result, the spread can appreciate even without significant movement, particularly if implied volatility declines significantly at the furthest strikes. Earlier this year, such was true in Super Micro Computer Inc (NASDAQ: SMCI) and Nvidia Corp (NASDAQ: NVDA).
SMCI was trending up, and traders were feverishly betting/hedging this reality. In a 20-page case study we may release, we detail how Physik Invest navigated this environment successfully. In short, we bought options closer to where the market was trading and sold more of them in places where we thought the market wouldn’t end up going. With implied volatility jacked, for lack of better phrasing, it was often difficult for those far-away and short-dated options to keep their value. Hence, we managed to put trades on for low or no cost and flip them for significant credits!!!
Graphic: Retrieved from SpotGamma. SMCI volatility skew.
In any case, there’s been a weakening under the surface of the indexes (see below).
Graphic: Retrieved from TradingView via Physik Invest. Black = Breadth Measure.
Later, when breadth improves, we can use the portfolio volatility-reducing trades discussed to cut costs or buy more stocks, anticipating upside continuation. According to Carson Group’s Ryan Detrick, the S&P 500 experienced its first close below the 50-day moving average in 110 trading days, marking the longest streak since 2011. Following similar streaks, stocks were higher three months later, 88% of the time, and six months later, 81% of the time. “A warning? Maybe, but maybe not.”
Graphic: Retrieved from Ryan Detrick of Carson Group.
If you enjoyed this week’s letter, comment below and share. Thanks and take care!
As we step into Spring, we’re riding the wave of one of the strongest stock market rallies in over fifty years. It’s been a period of smooth sailing, with record highs beckoning transition from concern over potential downturns to the fear of being left out of further gains.
The BIS has commented on some of these trading behaviors, which can drive upward momentum and foster a sense of calm or low volatility. They point to the increased use of yield-enhancing structured products as a critical reason for reducing volatility. These products have stolen the show, boosting investor returns by selling options or betting against market fluctuations. In calm markets, those on the opposite side of these bets hedge in a way that reduces volatility: they buy when underlying asset prices dip and sell when they rise. As the supply of options increases, the liquidity injected to hedge stifles movement, resulting in a stubbornly low Cboe Volatility Index or VIX.
The BIS example illustrates a product that sells call options against an index position to lower the cost basis by collecting premiums. The counterparty buys call options and hedges by selling the same index. If the call options lose value or the market declines, the counterparty buys back the index they sold initially. This strategy is constructive and potentially bullish, especially in a rising market, as one could infer counterparties may postpone rebalancing to optimize profits (i.e., swiftly cut losses and allow profits to accumulate).
Graphic: Retrieved from Bank for International Settlements.
However, these trading behaviors come with risks.
While individual stocks may experience volatility, the indexes representing them move begrudgingly. Investors have concentrated on selling options or volatility (the all-encompassing term) on indexes to fund volatility in individual components, a strategy known as dispersion. Although typically stabilizing, experts caution that it can end dramatically. One can look at the destructive selling in China as a cautionary example.
Kai Volatility’s Cem Karsan compares the trade to two sumo wrestlers or colossal plates on the Earth’s core exerting immense pressure against each other. While the trade may appear balanced and continue far longer, the accumulated pressures pose significant risks. Major crashes (up or down) happen when entities are compelled to trade volatility and options. Often, the trigger is the inability to cover the margin and meet regulatory requirements, causing a cascading effect (e.g., GameStop and 2020 crash).
The current scenario mirrors the conditions before Volmageddon, where short-volatility tactics failed.
With implied correlations low, a market shock could see investors exiting their positions abruptly, amplifying volatility. Karsan notes a precursor to such a crash is a weakening supply of margin puts, particularly the highly convex and far out-of-the-money ones. These options play a significant role during stressful market periods, acting as indicators and drivers of impending crashes. The focus is on convexity (i.e., the rate of change of delta for changes in the underlying asset’s price or the nonlinear relationship between the option’s price and the underlying asset) rather than whether there are good odds the underlying asset will trade down to the options in question.
“Implied vol is about liquidity. It isn’t about fear or greed,” writes Capital Flows Research. “Implied vol is about liquidity on specific parts of the distribution of returns on an asset. Remember, even the outright price of an asset is pricing a distribution of outcomes, not a single destination. Options make this even more explicit by having various strikes and expirations with differing premiums and discounts.”
History shows a minor catalyst can lead to a dramatic unwind. Take what happened with S&P 500 options a day before XIV crash day.
“Going into the close the last hour, we saw nickel, ten, and five-cent options trade up to about $0.50 and $0.70,” Karsan elaborates. “They really started to pop in the last hour.”
“And then, the next day, we opened up and they were worth $10.00. You often don’t see them go from a nickel to $0.50. If you do, don’t sell them. Buy them, which is the next trade.”
Similar to downward crashes, there are occasional but now more common upward crashes.
Recent market movements, particularly the surge in stocks such as Nvidia, Super Micro Computer, and MicroStrategy, echo the frenzy seen with high-flying stocks like GameStop in 2021. This caused losses for some liquidity providers and funds that mistakenly equated the price or level of volatility with value, selling it at a discount to where it would eventually trade.
Graphic: Retrieved from Bloomberg via Simplify Asset Management’s Michael Green.
“I remember several traders I knew trying to short-vol on GME when it was at 300 because it was ‘cheap’ due to its level,” Capital Flows Research adds. “They were blown out of those positions.”
Graphic: Retrieved from Bloomberg via Capital Flows Research.
So, we have played along, nodding to George Soros’s famous statement: “When I see a bubble forming, I rush in to buy, adding fuel to the fire. That is not irrational.”
To explain, we go deeper into something known as implied volatility skew.
Skew refers to the difference in implied volatility across different strike options on the same underlying asset. Typically, options with farther away strike prices (out-of-the-money puts) have higher implied volatility than options with higher strike prices (at-the-money calls).
Implied volatility skew, as shown below, is often nonsymmetrical due to higher demand for downside protection.
When volatility skews become steeper, the disparity in implied volatility between various strike prices widens. For instance, the implied volatility of out-of-the-money (OTM) puts, which offer protection against market downturns, rises compared to at-the-money (ATM) puts and upside protection (calls). This steepening volatility skew indicates heightened apprehension among investors regarding potentially large downward market movements. Similarly, when the implied volatility of upside protection (calls) surpasses that of downside protection (puts), it signals growing concern (i.e., FOMO) about potential upward market movements. A steepening call volatility skew results from distant call options pricing higher implied volatility than usual due to investor demand/fear.
Graphic: Retrieved from Exotic Options and Hybrids: A Guide to Structuring, Pricing and Trading.
As savvy traders, we can construct creative structures and sell options against the closer ones we own to lower our costs on bullish trades. We detailed such bullish trades in our last two commentaries titled “BOXXing For Beginners” and “Foreshocks.” The outcomes for one of Physik Invest’s accounts are detailed below.
Graphic: Retrieved from TD Ameritrade’s thinkorswim platform.
Regrettably, enthusiasm is waning. Using Nvidia as an illustration, the stock surged 2.6% on Friday but plummeted 8% on the same day. The call skew was elevated over the weekend before leveling off earlier this week, which poses difficulties for traders betting on further upward movement.
Graphic: Retrieved from SpotGamma.
We discussed how such a flattening could foreshadow waning risk appetite and potentially herald market softness. SpotGamma indicates that call skews are flattening across the board, as illustrated in the chart below.
The red bars on the left represent approximately 90th percentile skews during a significant stock rally. However, a week later, on the right side, the skew rankings decline. “This appears like the uniformly bullish action in top tech stocks is breaking apart,” SpotGamma explains. This “is a reduction in bullish exuberance.”
This activity will not likely disrupt the broader market; markets will stay intact as traders double down, selling shorter-dated volatility and buying farther-dated ones. We observe this using SpotGamma’s Fixed Strike Matrix below. In a simplistic sense, red indicates selling, while green suggests buying.
“By default, cells are color-coded red-to-green based on the Implied Volatility Z-Score,” SpotGamma explains. “If the cell is red, Implied Volatility is lower than the average implied volatility over the past two months. If the cell is green, Implied Volatility is higher than the implied volatility over the past two months.”
Graphic: Retrieved from SpotGamma on Monday, March 11, 2024.
The recent compression in short-term volatility aids stabilization, leading to restrained ranges in the indexes relative to components. Among these components, which drove the S&P 500 upwards, some big ones face downward pressure, partly due to the expiration of previously demanded/bought call options. This expiration prompts those initially selling these (e.g., call) options to re-hedge by selling the corresponding stocks.
As the indexes remain fixed, the only resolution is a decline in correlation. As larger stocks decline, smaller constituents rise, contributing to the strength observed in the S&P 500 Equal Weight Index.
Breadth can be evaluated naively by comparing the S&P 500 stocks trading above their 50-day moving average and examining the proportion of index constituents achieving new highs and lows. We see improvement, per the below.
Graphic: Retrieved from Physik Invest via TradingView. Breadth black. Correlation purple.
Based on the above explanation and graphics, after the triple witching expiration of futures, stock, and index options, traders may rebalance their portfolios and sell some of the remaining volatility they’ve bid.
As explained earlier, this will further compress volatility, reducing the potential downside and providing critical support for stocks. Considering it’s an election year and policymakers prioritize growth over instability, Karsan suggests the market may remain stable with these forces above offering an added boost. Therefore, focus on creatively structuring longer-dated call structures and financing them with other trades to amplify return potential.
If the market consolidates without breaking, we may have the groundwork for a much bigger FOMO-driven call-buying rally culminating in a blow-off. Karsan adds that the signs of this “more combustible situation” would appear when “volatility remains persistent during a rally.” To assess combustibility, observe the options market.
We remember that calls trade at lower implied volatility than puts, particularly from all the supply. As the market moves higher, it transitions to lower implied volatility, reflected in broad measures like the VIX. If the VIX measures remain steady or higher, “that indicates that fixed-strike volatility is increasing, and if this persists, … it can unsettle volatility and create a situation where dealers themselves … begin to reduce their volatility exposure, leading to a more combustible scenario.”
To elaborate on the reducing exposure note in the previous paragraph, if there is greater demand for calls, counterparties will take on more exposure and hedge through purchases of the underlying asset. The support dealers provide will diminish once this exposure expires. If the assumption is that equity markets are currently expensive, then after another rally, there may be more room for a decline, all else being equal (a simplified perspective), thus increasing risk and combustibility.
Graphic: Outdated. Retrieved from Nomura. To help explain.
This week, we discussed a lot of information. Some of it may need to be explained better. Therefore, we look forward to your feedback. Separately, I wish my friend Giovanni Berardi congratulations on starting his newsletter. I worked with Berardi, giving him input on some of his positioning-related research. He shares his insights here. Please consider supporting him with a subscription. Cheers, Giovanni!
Nvidia Corporation (NASDAQ: NVDA) beat on earnings last week, lifting the entire stock market.
Graphic: Retrieved from Bloomberg via Christian Fromhertz.
The chipmaker confirms it can meet lofty expectations fueled by the artificial intelligence boom, with demand for Nvidia’s newest products likely to outpace supply throughout the year. Despite mounting competition and regulatory challenges in markets like China, Nvidia pursues strategic partnerships to expand its distribution channels.
Graphic: Retrieved from Bloomberg via @Marlin_Capital. NVDA eclipses $2T market capitalization, with its 12-month forward PE now at 33.
Before the earnings announcement, heightened implied volatility derived from options prices on the chipmaker’s stock indicated anticipation of significant fluctuations. The at-the-money straddles, composed of call and put options, suggested movement expectations of as much as +/-10% after earnings.
Various methods exist to estimate the expected move. One approach involves taking the value of the at-the-money straddle for the front month and multiplying it by 85%. Another entails using a narrow range of options.
The volatility skew, which will be defined later, implied that the perceived risk of movement was tilted toward the upside. In any case, staying within the anticipated movement would not favor options buyers, as we show later.
Since late 2023, traders have increasingly been hedging against or speculating on market upswings. This is evident in the higher call option implied volatility. Expectations for significant upward movement are particularly notable in the growing number of stocks where the 25 delta call implied volatility exceeds the 25 delta put implied volatility, shares Henry Schwartz of Cboe Global Markets.
To elaborate, options delta (∆) measures the change in an option’s price relative to changes in the underlying asset’s price. It indicates the option’s sensitivity to the underlying asset’s price movements. A delta of 0.50 means that for every $1 change in the underlying asset’s price, the option’s price would change by $0.50 in the same direction. The skew reflects the difference in implied volatility between out-of-the-money call and put options with the same delta.
When the 25 delta call implied volatility surpasses that of the 25 delta put implied volatility, a more pronounced positive skew suggests traders are willing to pay a premium for calls. Conversely, if the 25 delta put implied volatility exceeds that of the 25 delta call implied volatility, often observed in products like the S&P 500 (due to concerns about protecting equity downside), there is a negative skew or stronger inclination to pay a higher price for put options.
This persistent fear of missing out on sudden upward movements manifests a cascading effect when markets move higher, says Nomura Americas Cross-Asset Macro Strategist Charlie McElligott.
“The key to equities seemingly being able to keep shaking off nascent pullbacks? Well outside of the ongoing ‘AI euphoria’ theme and de-grossing of shorts, … it’s been all about the Pavlovian ‘options selling’ flows, which continue to suppress [implied volatility].”
Graphic: Retrieved from Nomura.
As explained by McElligott, these “options selling flows” have the potential to amplify momentum. For instance, when traders or customers purchase call spreads, as they are large, the counterparties or dealers are left with a short skew, negative delta position that loses money if implied volatility rises or markets rise. In response to a rising market, dealers may manage their delta by selling put options or buying call options, stocks, or futures. Adding these positive delta hedges helps propel the market into uncharted territory during swift movements.
Graphic: Retrieved from Nomura.
As validation, after Nvidia Corporation’s stock surged about 10% post-earnings, Bloomberg reported that “to fully re-hedge all open option positions coming into the day, 51 million shares, or 91% of the daily average,” would need to be traded. Bloomberg added that the March 15 $680 call, February 23 $700, and $750 calls experienced the most significant changes in the delta before the market opening.
Graphic: Retrieved from Bloomberg via Global_Macro or @Marcomadness2.
Observing SpotGamma’s real-time options hedging impact measure HIRO, the chipmaker was boosted partly on positive flows from the hedging of call options, as shown by the orange line below, while put options trading had a limited effect, as indicated by the blue line. The re-hedging activity positively affected the stock on Thursday post-earnings and had a pressuring effect on Friday, owing to the short-datedness of some of the options exposure traders initiated.
Graphic: Retrieved from SpotGamma.
While mentioning pressures, see below the volatility skew before (green) and after (grey) earnings.
Graphic: Retrieved from SpotGamma.
Short-dated options with very high strikes (e.g., 900+) and close expiration dates (e.g., ten days) struggled to hold their value. SpotGamma shared that the pricing of near-the-money $785 calls expiring on March 15 returned to their previous levels just a week before earnings. Since the actual movement closely matched the expected movement, there was little justification for options well above the market (i.e., +30%) to retain their value.
At Physik Invest, we foresaw such a situation and executed 100-point wide 1×2 call ratio spreads between the 900s and 1000s for a credit of approximately 0.90. We closed these positions the next day for an additional credit of 0.50 when the 1000 strike options failed to keep their value as good as the closer 900 strike options. The resulting profit was a 1.40 credit per spread.
Graphic: Via Banco Santander SA (NYSE: SAN) research. The return profile, at expiry, of a 1×2 (buy 1 and sell 2 further away) ratio spread.
Please be aware that similar trades are present in other high-flying products, albeit less widespread than in 2021 during the meme-stock trend. A simple way to determine whether such trades are safe is to check the pricing of fully in-the-money spreads. If the spreads trade at substantial credits to close, they are worth considering. However, if the spreads require a debit to close, it’s best to avoid them. In the case of Nvidia, the 100-point spread was priced at 25.00 in credit to close the day of earnings.
Graphic: Retrieved from TD Ameritrade’s thinkorswim platform.
Generally speaking, this trend in implied volatility is something that may continue. Kris Sidial from The Ambrus Group says the trend, which masks the risks of short volatility under the hood, such as those tied to risk-management practices, is driven by several factors not limited to the following:
(1) Increased demand for call options.
(2) Larger institutions seeking volatility as a hedge against rising risk exposure as the S&P 500 climbs.
(3) Significant market movements make it difficult for implied volatility to decrease significantly.
As such, Sidial suggests that “there is significant value in embracing volatility in both directions,” hedging against geopolitical and economic uncertainties while also capitalizing on the market upside. As discussed last week, we focus on leveraging elevated skew to reduce the cost of bullish trades (e.g., metals). Additionally, we plan to replenish our long put skew by acquiring put spreads in equities as a precaution against potential risks ahead, mainly local market peaks this time of year.
With recent data dissuading anticipated cuts, there’s room to safeguard cash at higher rates for longer.
One trade structure to help us do so is the box spread, which includes benefits such as a convenience yield, capital efficiencies achieved through portfolio margining, easy entry/exit on an exchange through most retail brokers, and potential 60% long-term and 40% short-term tax treatment.
Like a Treasury bill, the loan structure combines a bull call spread and a bear put spread. In a bull call spread, an investor purchases a call option and sells another at a higher strike price. A bear put spread involves buying a put option and selling another at a lower strike price. The lower (X1) and higher strikes (X2) match for a box spread, with all legs sharing the same expiration date.
Graphic: Retrieved from OCC.
In calculating the loan rate, we take, for example, a recent box spread trade of Physik Invest’s: BOT +1 IRON CONDOR SPX 100 (Quarterlys) 31 DEC 24 3000/6000/6000/3000 CALL/PUT @2867.90 CBOE.
We lend $286,790.00, at a risk-free rate of 5.27%, in exchange for $13,210.00 of interest at maturity. You can track box spread yields more quickly using tools like boxtrades.com. Such insights open up several strategic avenues for traders.
One approach is investing about 95% of your cash into box spreads to return the principal at maturity, risking the 5% interest you make on trades with a limited downside (e.g., SPX bull call spread).
A more preferable option exists for portfolio margin traders. Portfolio margining is a risk-based approach to determining margin requirements in a customer’s account, aligning collateral with the overall portfolio risk. Portfolio margining considers offsets between correlated products, calculating margin requirements based on projected losses. This approach may lower margin requirements, allowing for more efficient capital utilization.
As portfolio margin traders, we retain our buying power due to the minimal directional risk associated with box spreads, allocating it to other margin-intensive trades. To illustrate, if such a trader initially invests $100,000 in box spreads, they are left with $0 in cash and $100,000 in buying power available for margin-intensive trades (e.g., synthetic long stock or the purchase of an at-the-money call and simultaneous sale of an at-the-money put). You get your inflation protection while participating 100% in up-and-down market movements. Why not, right?
The point of the above passage is that much of what you see online can be done yourself in a tax, margin, and cost-efficient way. Alternatively, you can be hands-off, investing in money markets and CDs or complicated yet cool products like the popularized Alpha Architect 1-3 Month Box ETF (BATS: BOXX), which has grabbed attention for its tax arbitrage through complex strategies and loopholes.
Graphic: Retrieved from Bloomberg via Eric Balchunas.
With BOXX, you’re investing in something as safe as short-term Treasury bills, but you can get your money back anytime and enjoy better tax treatment than Treasury bills. Bloomberg’s Matt Levine has an excellent write-up on the mechanics of BOXX, which you can read here.
We digress. You can do more with your unused cash and buying power when following the methods outlined earlier and as we put well in our “Investing In A High Rate World” report published in April 2023. There, we discussed return stacking utilizing Nasdaq call ratio spreads and S&P 500 box spreads, two trades that continue to kill it this year.
We choose these structures, which have limited losses in case of market downside, for the following reasons: There is considerable support for the market, but this support appears fragile. For one, we refer to record-level dispersion trading, which involves the sale of index options and buying options in individual stocks.
It’s the same short volatility exposure Sidial has warned us about. With some stocks realizing substantial differences in movement from the index, this booming trade may have gone too far, setting the stage for a potential market reversal.
The situation resembles the period leading up to Volmageddon when short-volatility strategies backfired. Implied correlations are low, and if a market shock occurs, investors may be forced to close out their trades, which could feed volatility. As was in the case leading up to Volmageddon, however, volatility can cluster and mean-revert for longer.
Graphic: Retrieved from Bloomberg via Tallbacken Capital Advisors.
While scrolling through online news, some may relate to the idea that, sometimes, a lot can happen quickly. In other words, “There are decades where nothing happens, and there are weeks where decades happen.” This feeling was especially noticeable during last week’s “Volmageddon” anniversary, when the VIX skyrocketed, causing significant market disruptions. Skeptics and worriers were vocal about everything, from problems in how markets work to possible economic and political troubles.
Graphic: Retrieved from Bloomberg via Interactive Brokers’ Steve Sosnick. Pictured is “Volmageddon.”
A highlight was Tucker Carlson’s interview with Russian President Vladimir Putin. Throughout the conversation, besides uncovering insights into the Ukraine conflict’s ties to Poland, it became evident that not only the BRICS nations (Brazil, Russia, India, China, and South Africa) but also other countries like Saudi Arabia, Egypt, Ethiopia, Iran, and the United Arab Emirates, collectively representing over 30% of global GDP and 45% of the world’s population, are diminishing their dependence on the US dollar.
Putin suggested that the US effectively undermines the dollar, misusing its position as the issuer of the world’s primary reserve currency. This shift, previously discussed in our newsletters on January 4 and 5 of 2023, reflects broader changes in the global economy, carrying significant implications for the future. Let’s break down how.
Countries that share ideological alignment with BRICS are actively working to decrease their dependence on the US dollar and mitigate risks associated with (potential) sanctions. One practice involves trading resources for development without relying on US dollars for funding. For example, China securing oil at discounts by utilizing its renminbi currency allows Gulf Cooperation Council (GCC) nations to convert it into investments, development projects, and gold. Further implementing central bank digital currencies (CBDCs) streamlines interstate payments, an alternative to the Western-dominated financial system.
This gradually diminishing dependence on the West complicates challenges like inflation. Nations can boost their weights in currency baskets by encumbering and re-exporting commodities in strict supply. Accordingly, as Zoltan Pozsar shares, “the USdollar and Treasury securities will likely be dealing with issues they never had to deal with before: less demand, not more; more competition, not less.” Monetary policymakers can’t fight this trend alone; instead, for one, Western governments can boost energy production (not just productivity), states Rana Foroohar, global business columnist and associate editor at the Financial Times.
“Petrodollars also accelerated the creation of a more speculative, debt-fuelled economy in the US, as banks flush with cash created all sorts of new financial ‘innovations,’ and an influx of foreign capital allowed the US to maintain a larger deficit,” shared Foroohar. “That trend may now start to go into reverse. Already, there are fewer foreign buyers for US Treasuries. If the petroyuan takes off, it would feed the fire of de-dollarisation. China’s control of more energy reserves and the products that spring from them could be an important new contributor to inflation in the West. It’s a slow-burn problem.”
Regarding the market functioning narratives, David Einhorn, founder of Greenlight Capital, believes markets are fundamentally flawed, blaming the rise of passive investing and algorithmic trading. According to Einhorn, these methods prioritize short-term profits over long-term value creation.
To explain, we consider Nvidia’s case. Over the past five years, its weighting in the S&P 500 increased by 3.7%. This growth was driven by active managers who recognized the company’s value and bought shares, consequently boosting its market capitalization. This increase in market capitalization, in turn, elevated the stock’s weighting in the index.
Passive funds create a problem because they purchase stocks regardless of price when they receive new investments, as Bloomberg’s John Authers explains. Ultimately, “Passive decreases the inelasticity of a stock as it grows in market cap,” Simplify’s Michael Green shares. “Lower inelasticity, more extreme price response to the same volume of flow.”
As a company’s value increases, passive funds buy more of its stock, increasing prices. This trend is particularly concerning in the technology sector, where the flow of funds into passive investments pushes those stocks even further from value, stoking bubble fears.
Moreover, weakness beneath the surface is hidden, as seen in the comparison between the stocks above their 50-day moving average and the S&P 500.
The US stock market is approximately 70% of the world’s total market value, despite the US economy contributing less than 20% to global economic output, Authers adds.
“These valuations cannot make sense,” he elaborates. Markets imply that “over the next 20 years, less than 20% of the world economy will earn three times more profits than the remaining 70%,” Charles Gave of Gavekal Research says. It is a significant multi-decade bet on a small portion of the global economy generating most profits, primarily through the sustained dominance of technology giants.
Despite the strength and profitability of these companies persisting, with firms beating earnings estimates by about a margin of 7%, says Nasdaq economist Phil Mackintosh, whether their fundamentals alone justify such continued dominance is questioned.
Still, many experienced fund managers, who would typically bet against tech stocks, are refraining from doing so. Einhorn highlighted the costliness of taking such positions due to passive investing. As a result, his fund has shifted focus towards companies with lower market capitalizations relative to earnings and strong cash flows to support share buybacks.
According to Damped Spring Advisors’ Andy Constan, the trend towards indexation will continue as all investors have not fully embraced passive investing. If everyone were to adopt passive investing fully and no one bought stocks outside the S&P 500, companies not in the index would lose access to the public market, impacting funding for PE/VC markets and capital formation.
Though index investing may eventually face challenges as money moves from expensive stocks to cheaper, non-indexed ones, we can stick with it. Even if active managers do better than the index and counteract the distortions caused by passive investing, many of their stocks are still in those indexes. Again, more of a reason to invest in index funds.
A similar reasoning can be applied to the growing short volatility trade, which the likes of The Ambrus Group’s Kris Sidial have generated much buzz around.
Even though volatility was very low in 2017, the smart move was to sell it. As Sidial explains, volatility can have two modes. If you sold volatility in late 2017 to early 2018 when the VIX was in the 9-11 range, you made money because it tends to cluster. There’s a time when it’s wise for traders to take risks and go against the flow to make profits. However, there’s also a time when the flow is too big, dangerous, and not sensitive to price, and it doesn’t make sense to take that risk by buying low volatility and hoping for a big win, he shared in a recent update.
At this point in the newsletter, it’s apparent that timing matters. Manufacturing and employment appear strong, and overall, the economy is in a good place in the short- to medium-term, with above-zero rates contributing to the solid economic growth.
Graphic: Retrieved from Fidelity via Jurrien Timmer, Director of Global Macro at Fidelity. “This chart shows that during most cycles, the baton gets passed from P/E-expansion to earnings growth a few quarters into a new bull market cycle. We appear to be there.”
The context states rates and stocks can stay higherfor longer. On the flip side, we know volatility can stay lower longer, though its falling from lower and lower levels has less of a positive impact on stocks. Positioning is stretched, and the focus is shifting from worries about missed opportunities to safeguarding against potential downturns.
“We tend to see this type of movement before a reversal,” Kai Volatility’s Cem Karsan says, noting that volatility may rise, with the S&P 500 peaking as high as $5,100. “The speed of the move starts getting more accelerated towards the top because people start betting against, saying, ‘this is crazy, these values are too high, and the market needs to come down.’”
What Karsan describes is a more combustible situation arising from the market and volatility syncing.
To measure potential volatility, check the options market. Calls usually have lower implied volatility (IVOL) than puts. As the market rises, IVOL typically drops, reflected in broader IVOL measures like the VIX. If these broad IVOL measures rise, it suggests fixed-strike volatility is also rising. If this persists, it could unsettle dealers, leading them to reduce their exposure to volatility, boosting the momentum and whipsaw.
More demand for calls means counterparties take on more risk, hedged with underlying asset purchases. If this hedging support is withdrawn, it may increase vulnerability to a downturn. Still, we must remember that it’s an election year, and there could be more monetary and fiscal support for any weakness.
Graphic: Retrieved from Morgan Stanley via Tier1 Alpha.
As George Soros said, “It’s not whether you’re right or wrong that’s important, but how much money you make when you’re right and how much you lose when you’re wrong.” Given the low volatility environment and the performance of skew with such aggressive equity positioning and divergences beneath the surface of the indexes, consider the lower-cost structures we’ve discussed in newsletters, minimizing equity losses by employing the appropriate unbalanced spread.
Graphic: Retrieved from SpotGamma on February 11, 2024. Volatility skew for options expiring on March 15, 2024, on February 5 (grey) and February 9 (blue).
