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Tales of a Bridgewater Associate: The Fine Art of Building Portfolios

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 DalioGreg JensenBob 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.

Graphic: Retrieved from Andy Constan.

Why does this matter? 

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.

Graphic: Retrieved from Bloomberg.

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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Categories
Commentary

The Alchemy of Forecasting

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.

This academic work culminated in the book I published this year, Elite Networks: The Political Economy of Inequality. It is trending well at Amazon, Barnes & Noble, and other retailers.

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.

Graphic: Retrieved from CNBC.

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.

Are these options spreads that you are buying? 

A vertical. We always buy spreads.

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.

Graphic: Retrieved from Oraclum Capital.

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.

Graphic: Retrieved from Reuters.

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.

Graphic: Retrieved from the OCC.

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. 

I want to emulate someone like Paul Tudor Jones.

Do you have a favorite book recommendation?

Nassim Nicholas Taleb opened my eyes to options trading. After I read his third book, Antifragile: Things That Gain from Disorder, I thought, “Options are interesting; let’s see how this works.” I also think psychology books are great. So, Trading in the Zone: Master the Market with Confidence, Discipline, and a Winning Attitude and Schwager’s Market Wizards are fantastic because traders often make the same stupid mistakes; everyone goes through the same process.


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