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.
“Good investing doesn’t come from buying good things, but from buying things well.” – Howard Marks
There is a lot of noise—it’s exhausting. Today, we will sift through the noise and focus on how we can protect and potentially grow our portfolios this year. This is a follow-up to our Market Tremors letter. But first, let’s clarify the context for our approach. This is a long newsletter, so you may have to view it in another window.
Inflation is back in focus, gold is soaring, and investors are optimistic about stocks. Correlations remain low, dispersion is high, and the market’s volatility pricing/positioning obscures potential risks lurking beneath the surface. The macro landscape is shifting rapidly, yet when we zoom out, we’re confronted with something we’ve discussed before: inflation is here to stay!
For a long time, the expectation was that inflation would take a particular shape—a transitory spike and a manageable trend. Instead, structurally, we’re dealing with a world that is moving away from the low-inflation paradigms of the past. The pillars supporting cheap capital and abundant liquidity—globalization and dovish monetary policy—are shifting.
These shifts are neither sudden nor unexpected. In 2023, we wrote much about the narrative of the ideological struggle between the West and East, particularly with the Russia-Ukraine conflict sparking. Historically, whenever Eastern economies prosper, the West adjusts the rules. Now, it’s more about who controls what. Control over assets, inflation, and interest rates define economic power. Folks like Zoltan Pozsar have warned that the fundamental drivers of the low-inflation era—globalization and financialization—are unraveling, leaving policymakers with little choice.
The well-respected Kai Volatility’s Cem Karsan, a mentor to many, has pointed out in excruciating, albeit digestible detail that the trends favoring high-beta portfolios over the past four decades are reversing. Monetary authorities, particularly the Federal Reserve, have been constrained in their ability to address the widening wealth divide. Their response to inflation in the early 2020s—from creating demand to absorb surplus supplies of low-priced items to structurally restricting demand in response to shortages—was intended to guide the economy along a path of managed declines in activity while maneuvering interest rates to prevent another inflationary flare. Rising populism is a byproduct manifesting as shifts in public demand and political sentiment.
Thus, today’s Mar-a-LagoAccords and the broader economic overhaul signify a significant trade, monetary policy, and financial stability restructuring. Tariffs, a U.S. sovereign wealth fund, and global security restructuring are the key issues at this forefront. The implications of this shift are profound, and markets have yet to adjust. A portfolio for this new environment could creatively layer exposure to stocks, bonds, commodities, and volatility. Understanding the pieces herein will be critical for structuring trades and managing risk. Let’s dive in.
Macro Context: A New Economic Framework
#1 – Tariffs
One significant component of this broader economic overhaul is tariffs. Economist Stephen Miran, nominated by the U.S. President to be Chairman of the Council of Economic Advisers, has outlined how tariffs, historically used to influence trade flows, are being retooled as protectionist instruments and an alternative revenue source.
According to Miran’s AUser’sGuidetoRestructuringtheGlobalTradingSystem and fantastic explanations by Bianco Research founder Jim Bianco, a core issue is a persistently strong dollar distorting global trade balances. If paired with currency adjustments, tariffs could redistribute the costs away from U.S. consumers, “present[ing] minimal inflationary or otherwise adverse side effects, consistent with the [U.S.-China trade war] experience in 2018-2019.” However, this approach risks retaliation or distancing from key trading partners, further fracturing global supply chains.
To mitigate these risks, policymakers consider implementing tariffs in phases, gradually increasing rates to address inflationary pressures and market volatility. Even during the 2018-2019 trade war, tariff rate increases were implemented over time. Additionally, tariffs will be driven by national security concerns, targeting industries essential to defense and technological innovation. From this perspective, policymakers view access to the U.S. market as a privilege.
#2 – Sovereign Wealth Fund
A significant consideration is a U.S. sovereign wealth fund leaning on undervalued national assets to restore fiscal stability. Unlike traditional sovereign wealth funds built on surpluses, this fund would operate by revaluing and monetizing domestic reserves.
Key assets under consideration include undervalued gold reserves and billions in government-possessed bitcoin, which could be integrated into this fund. Bianco says these could total nearly $1 trillion.
This strategy introduces volatility concerns. Those concerned say government exposure and potential speculation on financial assets could lead to instability. Should we invest now for later?
#3 – Global Security Agreements
Beyond trade and monetary policy, a core element of the broader economic overhaul is linking military alliances to economic policy. The longstanding framework in which the U.S. provided security to allies without direct compensation is being rethought. The warnings are explicit; note the President’s Davos remarks and the Vice President’s Munich Security Conference speech.
Under a new paradigm, Bianco summarizes that NATO members may be required to contribute more to defense (say ~5% of GDP), foreign-held U.S. Treasury bonds may be converted into 100-year zero-coupon bonds, reducing short-term debt burdens, and tariff structures may be adjusted based on a country’s alignment with U.S. security interests.
“What Miran said in his paper is: you owe us so much for the last 80 years that what we want to do is a debt swap,” Bianco explains how the U.S. can be paid for being the world’s protector. “Those NATO countries have trillions of dollars of debt. [You’ll] swap it for 100-year or perpetual zero coupon non-marketable Treasury securit[ies]. So, you’re going to swap $10 billion worth of Treasuries for a $10 billion coupon century bond [that] won’t mature for 100 years, [and] won’t get any interest.”
In short, this is a fundamental shift that requires allies to bear a more significant share of security and costs. It’s the Mar-a-LagoAccords, a new financial order and policy framework akin to past agreements that reshaped the global economy, such as the Bretton Woods Agreement of 1944, which established the U.S. dollar as the international reserve currency, and the Plaza Accord of 1985, which coordinated currency adjustments to correct trade imbalances.
The proposed Mar-a-LagoAccords aim to reprice U.S. debt through asset monetization, weaken the dollar to improve U.S. export competitiveness and enforce tariff structures to rebalance global trade.
Positioning Context: Market Positioning Obscures
Tariff-driven price pressures, a weaker dollar, and a floor under interest rates raise bond yields, corporate borrowing costs, and strain leveraged players. This backdrop favors debasement plays and perceived safe havens like bitcoin and gold, which have been climbing for reasons discussed in the past and present.
Graphic: Retrieved from Bloomberg via @convertbond.
Equities face a less promising outlook. Oaktree Capital highlights that decade-long returns have historically been lackluster when investors bought the S&P 500 at today’s multiples. As Howard Marks puts it, earning +/-2% annually isn’t disastrous—but the real risk lies in a sharp valuation reset, compressed into just a few years, much like the brutal selloffs of the 1970s and 2000s.
Graphic: Retrieved from Bloomberg via Bob Elliott.
While the current market environment may feel frothy, with stretched valuations and narrow leadership, we’re not in an imbalanced 1970s scenario. Also, the possibility of a dollar devaluation serves as a tailwind for S&P 500 earnings, potentially boosting stock prices, Fallacy Alarm explains. Markets are not irrational; instead, they could face modest returns of around 5-6% annually for stocks and bonds over the next decade. Such sanguine sentiment is evident in the options/volatility market, reflecting the distribution of future possible outcomes; the trading and hedging of options make them a robust gauge of future outcomes—offering a view of where markets stand and where they might be headed.
Graphic: Retrieved from Bank of America via Bloomberg.
We observe several key happenings:
#1 – Hedging Volatility Spikes, Not Market Crashes
Investors are hedging against potential volatility spikes like those seen on August 5, 2024, when the VIX exploded higher. While the S&P 500 grinds upward and the VIX drifts lower and appears cheap (<16), the VVIX—“VIX of the VIX”—remains elevated. This unusual divergence manifests from demand for VIX calls, suggesting the market worries sharp repricings of risk are more likely than broad equity selloffs. The dynamic boils down to supply and demand; SPX options remain underappreciated—why protect when the market seems stable—meanwhile, VIX options are in demand, bolstering VVIX.
SpotGamma highlights this massive VIX call buying, noting dealer short convexity positioning suggests that, should volatility “wake up,” there could be significant downside pressure on equities and upside pressure on volatility, reinforcing the view that the VVIX’s elevated levels could signal a potential volatility spike, rather than a broad market crash.
“The aforementioned vega supply is indeed large, but it is innocuous unless provoked,” SpotGamma’s founder Brent Kochuba explains. Still, “with correlation stretched and IVs at lows, there is the potential for an SPX index short vol cover/single stock spasm to push into this upside vega convexity – something that we think a sharp NVDA [earnings] miss could spark.”
#2 – Options Selling and the ‘Buy My Course’ Gurus
Investors are leaning toward short-dated options selling (sometimes packaged within an ETF structure, without regard for price and thoroughly assessing broader market positioning) and structured products.
Graphic: Retrieved from JPMorgan via @jaredhstocks.
As QVR Advisors’ Benn Eifert explains, dynamic creates opportunity: deep out-of-the-money, long-dated volatility in single stocks looks attractive for tail-risk hedging. But there’s a catch—the persistence of this activity reinforces spot-vol covariance (i.e., the relationship between the underlying movements or spot and its volatility or vol). If the market shifts and volatility rises as the underlying asset moves up/down (the usual pattern flips), long volatility positions could become highly profitable, as it is then they would benefit from this reversal in spot-vol dynamics (e.g., 2020).
Graphic: Retrieved from Bloomberg via Kris Sidial. Volatility is fair in indexes; “much better opportunities in singles right now.”
As SpotGamma elaborated, if strength through earnings persists, “it will supply a final equity vol and correlation drop (a ‘final vol squeeze’), ushering in a blow-off equity top. At the same time, these metrics are low enough to justify owning 3-6 month downside protection, as bad things usually happen from these vol levels.”
Graphic: Correlation via TradingView. Stocks are expected to move more independently. Peep the pre-2018 Volmageddon levels.
As an aside, implied correlation measures the degree to which the prices of the assets in the basket are expected to move together (positively correlated) or in opposite directions (negatively correlated). Low correlation, in this case, indicates that the stocks are expected to move independently or in opposite directions; hence, dispersion trades betting on this have performed well.
#4 – The Changing Narrative of Bitcoin and Its Maximalists
Similar patterns emerge in bitcoin. As countries face currency debasement and economic stresses, bitcoin stands out as a hedge to some. Like equities, bitcoin options are underappreciated.
For example, implied volatility has traded under 50% for one-month options, representing an attractive entry point for those looking to position themselves for a surge. This low volatility environment in Bitcoin mirrors the opportunities in equities. Here, bitcoin benefits from any volatility reversal, presenting a compelling case for those looking to participate in a big market move.
Graphic: Retrieved from SpotGamma. Higher skew and IV rank suggest calls are expensive and moves are stretched.
Context Applied: Trade Structuring
Trade structuring this year is all about creativity. We’ve added the following to our portfolios.
#1 – Rates
One efficient structure for safeguarding cash is the box spread, which offers several key benefits: a convenience yield, capital efficiency (especially for users of portfolio margin), easy execution via most retail brokers, and favorable tax treatment—60% long-term and 40% short-term if executed using cash-settled index options (e.g., SPX). This strategy combines a bull call spread and a bear put spread, matching lower and higher strikes and the same expiration date.
We frequently trade such structures. For instance, here’s one we purchased at the beginning of this year: BOT +1 IRON CONDOR SPX 100 (Quarterlys) 31 DEC 25 4000/7100/7100/4000 CALL/PUT @2964.25 CBOE
In this case, we invest $296,425 now to receive $310,000 in a year. This represents an implied interest rate of 5.32% or ((3100-2964.25)/2964.25)*(365/314)=0.053234. Note that there is a convenience yield, and that’s due to counterparty risk, as box spreads depend on the Options Clearing Corporation (OCC) to guarantee the transaction.
Tools like boxtrades.com help with tracking yields and finding attractive box structures.
Box trades unlock the power of yield stacking, enhancing returns by layering multiple exposures without increasing capital outlay. They preserve full buying power with portfolio margin for margin-intensive trades like synthetic longs.
For non-portfolio margin traders, yield stacking is less applicable. Instead, you can allocate ~95% of cash to box spreads, locking in your principal at maturity while risking only ~5% (the interest you stand to make), with limited downside.
Low correlation and subdued implied volatility signal stability, but any disruption could spark sharp moves.
As we explained better in Reality Is Path-Dependent, Cem Karsan notes that a slow grind higher cheapens options, fueled by continued volatility selling. Eventually, realized upside volatility will surpass implied, prompting smart money to buy options at these discounts. If the VIX holds steady or rises, it suggests fixed-strike volatility is creeping up, potentially forcing options counterparties to cut exposure or hedge, boosting markets higher; increased call demand could push counterparties to hedge by buying the underlying asset, reinforcing stability and giving a floor to options prices and the market by that token.
The play here? Replace stock exposure with options. You can buy calls outright and hedge them by selling stock—gains on the calls should outpace hedge losses. Karsan has talked about this a lot. One of our moves is to structure broken-wing butterflies or similar: buy an option near the money, sell a larger number of options further out, and cap risk with an even farther out option. In this environment, you can often put on these trades for little cost and exit at multiples higher if the market drifts sideways or up. Please see our website for case studies and example trades.
Don’t overlook crypto, either. Implied volatility remains underappreciated in bitcoin, making synthetic exposures compelling. Swapping spot for synthetic alternatives is a play on these opportunities. Though we haven’t touched them, check out Cboe’s cash-settled options on spot bitcoin: the Cboe Bitcoin US ETF Index (CBTX) and Cboe Mini Bitcoin US ETF Index (MBTX).
#3 – Hedging
Though less attractive now, VIX calls and call spreads remain a powerful tool for hedging tail risks. In our RealityIsPath-Dependent letter, we explore this topic further.
There are more compelling structures within the S&P 500 complex, particularly back spreads. For example, a put back spread involves selling a higher strike put option and buying a larger number of lower strike put options, positioning you to profit from substantial volatility shifts—similar to what we saw on August 5, 2024.
Although this structure takes advantage of the market’s unappealing volatility skew, drift presents challenges; if volatility fails to perform well during a downturn, you risk losing more money than you initially invested in the spread. Caution!
Graphic: Retrieved from Bloomberg via Goldman Sachs.
Bonus: From the White House to Wall Street
We had the opportunity to catch up with Steven Orr, founder of Quasar Markets. We discussed his career and the future of fintech and trading technology. Before Quasar Markets, Orr worked as an executive at Money.net and Benzinga. He also serves on the board of the American Blockchain and Cryptocurrency Association. His diverse background includes positions with the White House, the U.S. State Department, the PGA Tour, the NBA, and various professional sports leagues. Orr frequently shares his insights on TV and appears at events like the World Economic Forum. Check it out, and thank you, Steven!
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.
In this newsletter, we interview David Aferiat, co-founder of Trade Ideas, about his entrepreneurial journey and the future of fintech and trading technology.
From building Trade Ideas—a well-known name in self-directed investing—to advising startups, Aferiat has worked at the intersection of finance and technology for about two decades.
Our conversation explored the evolution of self-directed investing, including the recent introduction of commission-free trades and the implications of AI and automation. Also, Aferiat shared a few insights on building businesses and partnerships.
You can watch the video at this link and below. An edited transcript offering key context follows. We hope you enjoy this lighthearted conversation and some of our other recent newsletters, which are a nice break from the usual. Cheers!
Thanks for joining me, David. You’re based in Atlanta, Georgia, right?
I’ve been here for about 24–25 years. I raised a family, started several businesses, and moved here as a consultant after getting my MBA in Dallas. Atlanta has been great—it connects you to the world with direct flights and is a great place to raise a family.
I’ve known you since 2019 when I was at Benzinga. You were one of my first interviews, and we talked about how retail trading was evolving. You discussed AI before it became the hot narrative it is today. But before we dive into that, I’d love to hear more about your background—your heritage, upbringing, and early career. Can you take me back?
Absolutely. I love how our conversations have come full circle, especially with AI as a recurring theme.
I’m the son of an immigrant from North Africa, part of France before gaining independence. Half of my family went to France, and the other half went to the U.S. My grandfather was an entrepreneur, but my father took a different path, building a successful career in the hospitality industry.
My grandfather introduced me to the stock market. We used to pull out graph paper and manually track stocks using point-and-figure charts. That early exposure sparked my fascination with markets, which I later channeled into Trade Ideas—something much bigger than myself, helping people make better trading decisions.
Given your family’s history, you could have been risk-averse but took an entrepreneurial path instead. Why is that?
It is a generational ricochet effect.
My grandfather was entrepreneurial, but my father was more conservative—out of necessity. He had to adapt, assimilate, and establish financial security. I respect that, but I also felt a pull toward entrepreneurship, like my grandfather.
I relate to that. My grandfather was entrepreneurial overseas, but my dad was more conservative after immigrating to the U.S. It seems we take the best of both worlds. What did you study in school, and what was your early career like?
I graduated from the University of Texas and earned my MBA at Kraft Foods; Kraft paid for my MBA, and after that, I worked in consulting, which brought me to Atlanta. Consulting felt like an extension of my MBA—applying models, engaging teams, and managing projects, which proved valuable in building a startup like Trade Ideas.
Tell me about the early days of Trade Ideas. What problem were you solving?
It started as a web-based tool displaying market data. Over time, we refined it to help traders make better decisions.
We gained traction when major brokerages like E*Trade, Scottrade, Interactive Brokers, and TD Ameritrade integrated our tools. This helped us scale and reach more traders. Today, I apply the lessons I learned at Trade Ideas to assist fintech companies in growing, whether they are building indicators, execution tools, or enhancing market infrastructure.
What did it take to secure those partnerships?
It’s about identifying champions within organizations—decision-makers with budget control and internal advocates who can push your product forward. We built a compelling case, showing how our technology improved engagement and retention.
Where do you see trading technology evolving?
Technology, especially AI, transforms how people interact with financial markets, driving a convergence where platforms like Robinhood and Fidelity adopt each other’s features. The emergence of 24-hour trading in equities appears inevitable, with crypto setting a standard for continuous market access. Traditionally, equities led in financial innovation, but in recent years, they have fallen behind. Also, the number of publicly traded companies has nearly halved since the 1990s, resulting in the dominance of larger firms and a growing need for fractional trading.
How did you stay committed to Trade Ideas for so long in this age of instant gratification?
Purpose and customer experience are key to me.
Joey Coleman’s book, NeverLoseaCustomerAgain:TurnAnySaleintoLifelongLoyaltyin100Days, emphasizes mapping customer touchpoints and delivering value from the first interaction. When engaging with customers, the first impression is crucial. It should demonstrate the long-term value, allowing them to instantly envision an ongoing relationship where they continue to benefit from the experience. That philosophy has been applied to Trade Ideas and continues influencing my work today.
How did you handle delegation as Trade Ideas grew?
Surrounding yourself with the right people is essential. For example, I am involved with organizations like the Entrepreneurs’ Organization (EO) and connect with other leaders who face similar challenges.
Generally, successful teams combine strategic thinkers, executors, relators, and communicators. If there are too many strategists, nothing gets done; too many executors, and there is no long-term vision.
You’re also involved in Avid Vines and Advintro. Can you share more about those ventures?
Avid Vines is an organic champagne import business rooted in my French heritage. It’s been a journey of learning and assembling the right team.
Advintro keeps me connected to fintech, where I advise startups and established companies on everything from scaling to customer experience.
What’s a key lesson you pass on to clients?
One big one is the power of experience and, within those experiences, intentionally moving people to create and connect themselves. A book called The Art of Gathering: How We Meet and Why It Matters helped me refine how I view and create impactful events, whether for fintech or entrepreneurs.
Last year, I co-led a conference on perseverance that brought together about 500 entrepreneurs. We spent over 18 months and had a $1 million budget to create this event, featuring speakers like Peter Diamandis and former Senator Martha McSally. The theme was perseverance and building routines that lead to a better future self.
Again, success isn’t just about KPIs; it’s about ensuring each team member is in the proper role, leveraging the collective strengths, and working toward a shared goal.
What daily habits keep you focused?
Self-care is essential. I follow GAMER: Gratitude, Affirmations, Meditation, Exercise, and Rest/Reading. It keeps me balanced and focused on the next version of myself.
Final question—what are the underrated characteristics of successful founders?
Empathy, humility, and integrity are essential for success. They come from relying on others, keeping promises, and staying true to one’s values. Those lacking these qualities may struggle with inner conflict, which can ultimately hold them back.
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.
Editor’sNote: In pursuing quality, we want to emphasize that we presented an inadequate definition in the haste to publish this letter. The Volume-Weighted Average Price (VWAP) reflects the average price at which an asset has traded over a specific period, weighted by volume. It is calculated by multiplying the price of each trade by the number of shares traded at that price, summing these products, and dividing by the total shares traded during that timeframe. We sincerely apologize for that!
Our technical analysis toolkit is streamlined to prevent analysis paralysis. The primary indicator we rely on is the Volume-Weighted Average Price (VWAP), which combines price and volume to provide a clearer view of an asset’s traded price over time.
Firms like Citadel use VWAP to execute trades, minimizing market impact and achieving better prices. VWAP can be applied to various strategies, including trend identification, mean reversion, and support/resistance trading.
Below, we explain VWAP and recommend following experts on the topic, like Brian Shannon of AlphaTrends. Additionally, platforms like TradingView offer this tool free.
What is Anchoring?
Anchoring is a psychological bias where people rely heavily on the first piece of information they encounter when making decisions. In financial markets, price levels often act as anchors that influence trading behavior. For example:
“If the price hits this level, I’ll take profit.”
“If it drops below this level, I’ll cut my losses.”
These anchor points serve as the basis for support and resistance levels in technical analysis. At support, there is buying interest (demand); at resistance, there is selling interest (supply). When these levels are breached, their roles can reverse: previous resistance can turn into new support, and vice versa.
Why Anchoring Matters?
Think of the average price people pay to enter a trade. Future decisions, like selling or holding, are often based on this price. If the price breaks a significant support level, traders who bought at higher prices may rush to sell to avoid further losses, creating resistance at those levels.
What are VWAPs?
Volume-Weighted Average Price (VWAP) is calculated by multiplying the price of each trade by the number of shares traded at that price, summing these values, and then dividing by the total shares traded during the period.
The Anchored VWAP (AVWAP) extends this concept by incorporating time, price, and volume to determine the average price participants have paid since a specific starting point. This starting point could be a pivotal event, such as:
A significant announcement (e.g., earnings, Federal Reserve decisions).
The start of a new trading period (e.g., month-to-date, year-to-date).
A considerable price movement (e.g., swing highs or lows).
AVWAP helps answer:
Have most traders been in a winning or losing position since the anchor date?
Where might support or resistance form based on trading activity?
AVWAP in Action
Above AVWAP: Most participants are profitable if the price exceeds the AVWAP line.
Below AVWAP: Most participants are likely at a loss if the price is below the AVWAP line.
Clusters of AVWAPs: Multiple AVWAP lines close together often create strong support or resistance zones.
Sample Uses
Intraday Support: In short-term trading (e.g., 2-minute charts), a rising AVWAP often acts as support. Prices may repeatedly bounce off the AVWAP line during the trading session.
Breaking Trends: In this example with Shopify Inc (NYSE: SHOP), the stock broke an uptrend, and the AVWAP line from earlier highs acted as resistance. Traders could use this to decide where to sell or avoid buying.
MomentumTrading: Expanding on breaking trends, when trading stocks with strong prior momentum, the AVWAP from the latest spike high can serve as a dependable level to take profits. Here’s an example from Brian Shannon himself.
Why AVWAP Works
AVWAP is popular because Chief Investment Officers use it to evaluate trade quality, and liquidity algorithms are frequently designed to buy or sell near the VWAP, establishing it as a benchmark for institutional trading.
A Practical Application
When AVWAP lines from previous peaks and troughs come together, the stock often breaks out decisively above or below this “pinch” zone. For a depressed stock, pairing an AVWAP pinch with a rising short interest can prepare us for a squeeze! Below is an excellent example from Brian Shannon.
Combining AVWAP with Other Indicators
To enhance decision-making, we can pair AVWAP with:
Simple Moving Averages: A rising moving average signals buying interest.
Volume Data: To confirm whether trading activity supports the price levels.
Key Takeaways
The AVWAP provides a more customized view by anchoring the VWAP calculation to a specific point in time (e.g., a significant price level or event), which helps view price, volume, and time factors more dynamically. It is most effective alongside other tools like market profiles, volume profiles, and moving averages.
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.
Hedge fund week just wrapped up here in Miami, and I had the chance to catch up with some industry friends like fellow Croatian and former podcast guest Vuk Vukovic. A big shoutout to Vuk and the incredible success of Oraclum Capital, his NYC-based hedge fund! Beyond motivation, these conversations always get us thinking—about how far we’ve come, the lessons learned, and the market patterns emerging. So, today, we’re switching things up a bit.
Breaking Down Thinking vs. Acting in Real-Time
These newsletters often dive deeply into trade theory—how to form opinions, identify dislocations, and structure trades to take advantage of them. Thinking critically about market context is just as important, if not more so, than taking action, as outlined in the case study linked here. But let’s be honest: we don’t always have the luxury of testing every idea before committing capital. Sometimes, decisions must be made on the spot, and more often than not, they’re more straightforward than they appear—they have to be.
To illustrate this, I share a slightly refined diary entry from a year ago, offering reflection and motivation. Market highs, uncertainty, and the fear of missing out create a lot of noise—but they also spark new ways of thinking. I hope this record and perspective spark new ideas on when and how to engage in markets this year ahead.
Take your time, enjoy the read, and try to focus on the bigger picture rather than getting lost in the details. Stay tuned for upcoming podcasts, explainers, and part two of the Market Tremors newsletter. Cheers, and let’s make some money.
On February 16, 2024, my trading partner Justin pointed out a rich, elevated call skew in Super Micro Computer Inc. (NASDAQ: SMCI). This occurs when out-of-the-money call options carry higher implied volatility than at-the-money or in-the-money options, often signaling strong demand for upside exposure. At that point, SMCI had been climbing steadily for weeks, with the charts suggesting a parabolic advance and an imminent climax.
I spotted 200-wide 1×2 ratio spreads that could be opened for credit. Simply put, a ratio spread is an options structure in which you buy a contract at one strike and sell two (or more) at another, further away. I often use such a structure when volatility is more steeply skewed, meaning certain strikes—like deep out-of-the-money (OTM) calls—have much higher implied volatility (IV) because traders expect more risk or extreme moves in that direction.
IV is the amount of movement traders anticipate. A higher IV means the market expects more significant price swings, leading to more expensive options, while a lower IV suggests less expected movement, making options cheaper—factors like earnings reports, economic data, or overall market uncertainty influence IV.
In this case, using Schwab’s thinkorswim lookback feature, implied volatility on the call side ranged from the mid-100s near the current market price to over 200% at the furthest strike. On the put side, things got even wilder—volatility climbed from the mid-100s near the market price to several hundred percent at the farthest strike, as pictured below. When volatility is this high in far-out-of-the-money options, traders are piling in—either looking for protection or betting on a big, unexpected move.
As the stock was pulled back from its highs, we observed that the excitement in call-side volatility had begun to diminish—it wasn’t as intense as it had been a month earlier, according to SpotGamma data. This served as a key signal for us. A declining enthusiasm for calls indicates that traders are less inclined to chase the stock higher. We were prepared to act on this shift, betting that the stock had reached an interim peak; this was great news for us, as our options structures tend to perform best when volatility stabilizes and the stock drifts rather than making significant, protracted moves.
Right after the market opened, the 23 FEB 24 1300/1500 spread flipped from a 0.50 debit to over a 1.00 credit to open. I didn’t catch it right at the start, but about an hour later, I spotted the opportunity. The pricing looked solid—it offered a credit to close at the money—and everything checked out risk-wise according to my rules. So, I decided to dip my toes in with five units, keeping it on the smaller side for this trade. This all went down on February 16.
SOLD -1 1/2 BACKRATIO SMCI 100 (Weeklys) 23 FEB 24 1300/1500 CALL @1.10
All else equal, if the trade were entirely in the money (ITM), meaning the short strikes are right around the current market price, it would price for about 40.00 credit to close. At the money (ATM), right around the current market price, the structures traded for around 12.00 credit to close. This quick check suggests we’re good to move forward. Here are the orders for one account. You can find a summary screenshot of all orders at the end of this letter.
Given the risk involved in this trade, the abovementioned account could take on a maximum of 8 units. As we’ll see later in this entry, I pushed those limits, possibly going beyond what’s typical for me. However, I justified this by considering the distance between the stock price and the strikes used in the trades, which felt like a safe cushion to work with.
$320,000 (Net Liquidation Value) / $38,000 (Daily Loss at +1 EPR if the Spread’s Long Strike is ATM) = 8.4 units. EPR represents the brokerage firm’s estimate of the maximum expected one-day price range for an underlying security. Net Liquidation Value refers to the total value of a portfolio if all positions were liquidated at current market prices. Here are more details.
A few hours later, implied volatility dropped across the board, with the further out-of-the-money (OTM) options seeing the most significant decline. The implied volatility of the options closest to the stock price fell moderately, while the farther OTM strikes experienced a more substantial drop. This shift worked in our favor and helped make the trade profitable.
The long strike I owned (1300) had an implied volatility of ~190% before, which dropped to ~165% after.
The short strike I sold (1500) had an implied volatility of ~215% before, which dropped to ~180% after.
The trades were closed on the consolidation following the sharp morning liquidation. Here are the trade tickets.
From the panicked price movement, it looked like people late to the party were just selling off existing positions, not necessarily big new sellers entering the market; the stock might eventually retest those higher levels again. Even with the drop, implied volatility stayed high, which is crucial because it suggests continued uncertainty and anticipation of significant movement.
Given how sharp the sell-off was and how many traders were probably surprised by it, I decided to jump back into the trade on February 20—this time with a bigger position, especially after the long weekend when the market had some time to settle. Strikes and trade tickets follow.
SOLD -1 1/2 BACKRATIO SMCI 100 (Weeklys) 1 MAR 24 1300/1500 CALL @1.10
SOLD -1 1/2 BACKRATIO SMCI 100 (Weeklys) 1 MAR 24 1400/1600 CALL @1.10
SOLD -1 1/2 BACKRATIO SMCI 100 (Weeklys) 1 MAR 24 1350/1550 CALL @1.05
With the liquidation, the trade above was farther away from current prices than the last. Additionally, we moved it to next week’s expiry after the long weekend since it was no longer present for the 23 FEB 24 expiry. The lookback feature on Schwab’s thinkorswim shows implied volatility at the 1300 strike was ~180%, while at the short strikes, it was ~200%.
A quick check of SpotGamma’s implied volatility skew tool reveals a still-elevated call skew. Awesome!
Soon after, despite minor volatility shifts, we added similar trades with strikes that were further from the current price.
Gauging implied volatility accurately using the lookback feature can be tricky, but we observed that the difference in implied volatility between the strikes was narrowing. This indicated that the volatility skew was “flattening.” In simpler terms, the implied volatility between different strikes was becoming more similar, unlike a steeper skew where the farther strikes have much higher implied volatility. This can be good for the trade.
Here’s a chart that illustrates this “flattening” volatility skew. While this example shows the S&P 500, the concept is the same. Pay attention to the blue versus green line!
Anyways, back to the charts. So, here’s the price action. Straight down!
On February 22, we rotated more into similar structures we started working on February 20.
SOLD -1 1/2 BACKRATIO SMCI 100 (Weeklys) 8 MAR 24 1400/1600 CALL @1.10
At the time of entry, lookback showed the implied volatility of the 8 MAR 24 spreads was around 145% for the long and 155% for the short strikes. At the second entry, the volatility spread between the strikes started narrowing. Overall volatility came down, but the difference between the strikes was about the same.
Here’s what the volatility skew looked like at this point. This is a 30-day look back (the shadow).
I ended up closing the 1 MAR 24 spreads on February 22 for up to a 1.00 cr.
Here’s the implied volatility for the 1 MAR 24 options chain. Again, while a bit lower than when we started, the difference between the two is roughly the same. The passage of time is definitely working in our favor, here!
I’ll note that I closed prematurely because underlying price action suggested we could trend higher, with the upper VWAP band as an upside target. The spreads ended up pricing for $1.00 more in credit. Take what you can get, Renato!
The challenge we faced was deciding whether closing and rotating the trade early would lead to additional profits. Ultimately, we rolled the position and made money either way, but this was the thought at the time. In other words, are we doing too much?
After closing the 1 MAR structure, we added 8 MAR structures on February 22. Trade tickets for one account below. These additions made the position larger than I wanted, so I bought cheaper crash options to manage the margin (the amount of money required to maintain the position) first and foremost. It was a tense moment! Thankfully, with these additions, we stayed within our limits and didn’t breach any safety thresholds.
SOLD -12 1/2 BACKRATIO SMCI 100 (Weeklys) 8 MAR 24 1400/1600 CALL @1.05
At this point, the lookback showed implied volatility for the short strikes was around 160%, while the long strikes were at 150%. The difference between the two was around 10%.
Again, IV refers to the market’s expectations of future price movement expressed as a percentage. A higher IV suggests more movement, while a lower IV suggests less movement.
This is what the chart looked like at that time.
Around 2 PM, the market struggled but recovered, finishing higher by the close. The trades moved against me slightly, but the ATM and ITM entry and holding criteria (i.e., credit to close) mentioned above were still met, so I stuck with it.
Regarding having to hedge, I just focused on the spread’s sensitivity to price movements. Despite intense price action, the Greeks were okay. I remained in the position for about a week and a half. After the first week, the spread moved in my favor, but not to the extent I had hoped.
To explain, implied volatility remained higher on the short strike but dropped more on the long strike. Had the volatility on the short strikes dropped significantly more, the spreads would have likely come off sooner. Pricing the 15 MAR 24 spreads, those were trading for a debit to close, and it did not make sense to do anything other than sit on my hands and wait. If the stock continued to rise, which eventually occurred, the spread had more potential. Here’s the lookback at the time.
This is the price chart at month-end. It felt like there was more room to go up.
After the weekend, there was a big overnight move. Traders caught the news that SMCI would be included in the S&P 500.
I used the gap as a gift and sold into it, monetizing spreads from 3.00 to 5.05 cr to close. Trade tickets for one account follow.
5.05 marked the top in the structure’s pricing despite the stock moving higher after 10:00 AM. It took me years of watching these structures to spot softening sensitivity in the spread, prompting such closure. Had this gap not happened, the spreads likely would have been closed for small credits (0.05 cr). Again, the gap was a gift. Take it, Renato!
At this point, I am already considering rinsing and repeating this trade. The 15 MAR 24 200-point spread fully ITM traded for a small credit to close, which was unsafe. I widened accordingly to a 250-wide spread, priced for a very thick credit to close—the lookback shows about 44.00 cr. Here’s the lookback.
So, we went out to 1300/1550. There, I saw 1x2s pricing for thick credits to open.
SOLD -1 1/2 BACKRATIO SMCI 100 15 MAR 24 1350/1600 CALL @3.05
The implied volatility at the 1300 strike was ~150%, and at the 1550 strike, it was ~175%. We entered an hour early without regard for the stock chart (above), which was a costly mistake. The stock ripped higher, resulting in a ~$2.00 loss per spread.
Notably, the implied volatility skew steepened on the day of entry. Here’s a visual.
However, later that day, the spreads settled down. At 1:40 PM, the stock peaked, and the pricing of the 1300/1550 we put on declined slightly. To manage risk, I closed some units there. In any case, the spread narrowed, owing to a flattening and stickiness of the skew; 1300/1550 = 150/175% (~25% spread) → 140/160% (~20% spread). If I waited longer, the additional units would have gone massively in my favor. Oh, well!
Over the next few days, the stock moved down and then up; overall, the stock stayed flat. During this time, the spread increased in value, working in my favor. With these spreads, you want drift, not protracted movement!
My targets were at 5.00 and 10.00 cr to close, as I got over the next day or so. Implied volatility didn’t budge much. Based on thinkorswim’s lookback feature, it rose in the long strike more than the short strikes, which is what you want to see. Decay helped!
I wanted to hold longer because the spread 50 points closer to the money was pricing at 10.00 cr to close, about 3-5.00 cr more than I had my pricing for. Based on the stock price chart, we were peaking, but these moves tend to go sideways or higher for a bit longer. Candle shadows tend to get tested!
If we fast forward, the session was quiet, with SMCI trading sideways to lower from the open. The pricing of the spread went as high as 10.00 cr (at which point I started to monetize).
Here’s what implied volatility looked like (i.e., a rise in the long strike, whereas the short strike stayed about the same).
After the close, I started thinking, “Man, I should have closed that last spread.” It went to my target, but I kept holding (correctly), as you want to maintain some runners. However, the price action was weak into the evening, after the market closed, and I was now concerned I would lose all or most of the profit in my remaining spread. Mental games, here. Patience, Renato!
The market opened sideways the next day. I monetized my last spread for 11.00 cr, close to its peak.
Here are the implied volatilities at the exit (i.e., noticing the more significant drops in short strikes relative to the long ones).
There’s a critical factor that helped the trade keep its value. The stock went sideways, and a day or so passed, allowing some of the decay to kick in. The decay disproportionately affected the further OTM strikes (i.e., there’s more to decay than usual), with the lookback showing implied volatility dropped to 135% long / 150% short a few minutes after I closed my position. The market was attempting to go higher, volume was low on the 1300 strike, and the spread ended up pricing higher than what I closed it for. Bummer! Here’s what it looked like.
On March 8, the market peaked, hitting the 1200 figure I had envisioned. 1200 was a target for me due to the amount of interest (open interest and volume) at that strike, as well as the trend of the market. The March 4 shadow would also be taken based on the March 5-7 price action. Essentially, we traded up to and held short of the March 4 high, and the spread increased in value by $10.00 cr. I could have doubled my profits for the trade, but at the risk of losing it if things had gone the other way. Remember February 22, when the extreme volume was at the 1000 strike and above? We failed there. This was a blow to options buyers!
On March 8, 10-15 minutes after the market opened, implied volatility for 1300/1550 per thinkorswim’s lookback showed a much more significant drop in further OTM strike as the stock went up by $33 in that one day. Vol down, stock up, weekend decay, and that’s how a spread that priced 5.00 cr to close two days before ended up trading to 25.00 cr to close. Here’s the lookback.
On March 11, the stock traded much weaker. Implied volatility on the 1300/1550 went to 150/170%. Despite this, the trade lost a lot of delta, which the implied volatility bump couldn’t make up. The lookback shows the trade went to a 7.00 cr, a ~70% loss. This is what I say you’re up against. There’s not a lot of give to work with at times.
At this point, I realized I was doing what I was supposed to: take what I could get. Sometimes, the risk of losing what you made is not worth the potential reward. The trade was done after the stock hit 1200 (a location where I struck a bunch of Fibonacci extensions, too).
Finally, this is what the implied volatility skew looked like on March 8, the peak day.
In conclusion, this trade demonstrated one of my better executions. Over the month, SMCI traded sideways, and I captured about $24,000 in premiums across a couple of accounts. As I told my trading partner, the execution felt “divine”; there were plenty of moments where we could have made big mistakes—being too greedy, sizing too large and having to delta hedge in response, entering or exiting at the wrong times, or letting fears take over. However, the SMCI trades show improved thinking and acting quickly. Continuous improvement is all this is about—and that’s all I can ask for!
Some thoughts from this experience include waiting for market capitulation when the excitement fades, which helps spot better opportunities to trade the above structures. I identify these trades by scanning for high implied volatility, tracking a watchlist daily, sizing trades appropriately, and monitoring them closely; I size appropriately when I spot potential trades and save those trades (i.e., keep them in my monitor tab). I also suggest tracking implied volatility at specific strikes and keeping detailed notes; if you see a pattern, note it and decide whether adding or reducing the position is worth it. Additionally, holding onto “runners” (remaining positions) can significantly boost profits, as seen with the trades expiring on 15 MAR. Also, consider what may happen if the underlying moves toward the spreads and how you’ll react, adding, hedging, or reducing size.
What’s your favorite engagement trade when the fear of missing out is so great? Let’s discuss.
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.
Financial markets tend to oscillate like a pendulum, but recently, the fluctuations have intensified. For example, interest rates rapidly climbed from zero to 5%, and markets often alternate dramatically between despair and euphoria.
Decades-long policies concentrating wealth and incentivizing risk-taking are at the heart of these fluctuations. Central bank interventions, passive investing, and regulatory quirks have created fertile ground for dislocations and headline-grabbing events like the GameStop saga in 2021. For younger generations, these dynamics have been particularly tough. Millennials and Gen Z face delayed milestones, diminishing wealth, and growing skepticism about traditional, centralized financial structures.
Jannick Malling, co-founder and co-CEO of Public.com, aims to help investors adapt and thrive in this evolving landscape. In our latest podcast, Malling explains how Public engages a newer, active generation of investors.
You can watch the linked full video and/or read about some key points below.
Who is Jannick Malling?
Malling, of Danish descent, began his entrepreneurial journey in Copenhagen. His childhood curiosity about technology led him to build computers and explore the digital world. What started as a simple interest soon became a passion, as he began creating websites to organize video game meetups and help small businesses increase their visibility.
“You spend a lot of time in front of the computer, and you can’t be gaming 24/7,” he reflects. “So, when you’re not gaming, you’re just hanging out online, and I started to get excited about building websites. I wanted to design to solve problems, and this led to finding the design process so incredibly rewarding, interesting, and fulfilling that I just stayed with it my whole life.”
Unconventional for a 17-year-old, Malling later entered the professional world by joining Saxo Bank, a fintech that radically transformed investing in Europe. The experience was incredibly formative, teaching Malling about work ethic and the importance of speed, adaptability, and user-centric design—principles that continue to guide him at Public today.
Malling parallels his experience after Saxo Bank with that of the PayPal Mafia, a group of former PayPal employees who went on to shape the tech and venture capital worlds. Like PayPal’s Peter Thiel, Reid Hoffman, and Elon Musk, Malling and his Saxo Bank colleagues stayed connected, collaborating on new ventures after leaving the company.
Why build Public.com?
After co-founding and building several companies, Malling took a break and moved to New York City. Frustrated with managing his portfolio as a retail investor, he realized fractional shares could ease rebalancing and dollar-cost averaging for everyday investors. Inspired by this insight, he designed mockups for an app with a Venmo-like user experience. This ultimately became Public.com.
“I think there’s always been this overarching trend of leveling the playing field between institutions and individual investors,” Malling explains, highlighting Public’s focus on providing better information, community, and access at a lower cost. “Now, the question is what they should buy and why, which is where the research comes in.”
Retail brokerage platforms usually offer research, which can be challenging for investors without financial expertise. However, thanks to recent AI advancements, Public has enhanced its offerings, introducing products like Alpha.
“When ChatGPT came out, our imaginations were captured, and we started tinkering with this intersection of research and LLM interfaces,” he says. “Now, you can go to a stock on Public and ask any question. It’ll answer immediately, scanning the earnings files, financials, and analyst ratings. The process of researching a company is so much higher quality, and we’ve been able to drive AI hallucinations to basically zero, improving trust.”
Alpha has proven so effective in answering investor questions that Public launched it as a standalone platform. Those with a Public account can access Alpha for free, while those without can subscribe for just $1 weekly.
“Just screenshot your Apple Stocks, and Alpha auto-follows all those stocks, constantly scanning and telling you not just if they’re moving, but why they’re moving. That’s a great example of how AI can enhance the research experience without requiring people to dig too deep, creating more informed investors.”
Why did Public ditch PFOF?
In 2021, Public discontinued payments for order flow (PFOF). This decision underscores a more significant concern: the shortcomings of the National Best Bid and Offer (NBBO) system, which represents the best available bid and offer prices for equities across U.S. exchanges, serving as a benchmark for order execution.
The NBBO was created long before retail investing and zero-commission trading rocketed. Malling notes that it is based on market volumes of 100 shares or more, leaving out much of the current retail trading activity, like fractional and smaller trades. Although retail trading now accounts for a substantial share of market volume—sometimes over 40%—the NBBO does not adjust to this; firms complying with NBBO standards may provide prices that are not the best available.
“Retail makes up a much larger part of the market, but the NBBO doesn’t consider that in its reference price,” Malling explains. “So, sort of the hurdle that you have to clear is a little broken, which means that some firms can say, ‘Hey, we gave you the NBBO,’ but there may be a price that nobody else sees that’s lower than that.”
However, Public continues using the PFOF model in options trading, which works differently.
“In the options world, orders must be posted to an exchange, which creates more competition and drives better customer outcomes,” Malling explains. Public also shares up to 50% of its order flow rebates with customers. “You make what we make on your flow, so for those customers, it’s just incredibly transparent.
How does Public launch quicker?
Last year, Public launched a significant new feature roughly every ten days. Malling attributes this speed and adaptability—such as launching options on cash-settled indexes and Bitcoin in just over two weeks—to the company’s horizontal tech stack, lean team, and commitment to minimizing technical debt.
“Most big players in the space rack up technical debt, which happens when code is written in a way that makes it hard to maintain or further develop,” Malling elaborates. “To add new products, you’re forced to rewrite entire features, which slows down product velocity. Our engineers are allergic to that. By building a horizontal stack, we can move much more quickly.”
Partnerships that mitigate risks in everything from clearing to custody bolster this adaptability, enabling Public to concentrate on providing valuable features like seamless, real-time money transfers across asset classes, unified performance reporting, and tax optimization tools—all accessible via a single login for multiple account types, including cash, margin, IRA, and trust accounts.
What’s Public positioning for?
Looking ahead, Malling sees enormous opportunity in the ~$100 trillion wealth transfer from Baby Boomers to Millennials and Gen Z. These generations grew up with information at their fingertips and distrust in traditional financial structures. Accordingly, they seek alternatives like DIY investing, which, as was discussed, Public is making easier.
“This shift will redefine how wealth is managed. Younger generations are fundamentally different in how they approach investing, prioritizing transparency, technology, and self-direction,” he says. “Now, I have all the tools in the world to do it myself, which is the simple one-two punch that puts this generation on a fundamentally different path.”
For more, please consider watching the YouTube interview. Jannick Malling can be followed on LinkedIn and Twitter/X. Thank you!
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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