Eric Torenberg interviews Byrne Hobart and Daloopa CEO Thomas Li on Hedge Funds, VC, and Finding Alpha
I pulled some excerpts below I’d like to hold for future reference. I used ChatGPT to clean up the transcribed excerpts — the result is a mix of quotes and paraphrases.
On Alfred Winslow Jones first hedge fund being similar to the modern pod shop:
But other things were just generally a sensible part of the model that you charge based on performance. You try to hedge your exposure. So you’re not just betting the market goes up. But you’re trying to differentiate among different companies and try to figure out what makes each company unique. Not just understanding the business, but also understanding what makes the stock move. A bunch of other hedge funds started appearing in the 60s. Then, in the early 70s, almost all of them went under. Many realized they could borrow as stocks were going up. They borrowed a lot to buy a lot, and it worked really well for a while. Then most of them got wiped out. However, there were a few survivors. You had this golden age in the 80s where you had people like Soros doing more macro stuff, and people like Tiger doing more company-specific fundamental stuff. As computers got faster and we started having more data, people came up with systematic strategies across different asset classes. A lot happened between the 80s and today, but the current evolution has been towards some funds that run classic strategies like value-based stock picking and being mostly long, with a couple of short positions. The strategy that’s gaining a lot of market share in terms of assets and public attention is the multi-strategy, multi-manager, platform, or pod shop model.
In this model, you give a portfolio manager some capital budget/risk budget. You tell them they are picking stocks in their sector, the kinds of companies they pick, and they must have no net exposure to the market, large stocks versus small stocks, or one industry versus another, or momentum stocks versus value stocks. Once you hedge out all those factors that cause different companies to correlate, you end up with a very pure view of which stock is going up relative to its peers. This model has worked really well as a way to create uncorrelated streams of alpha. So if you have 100 different people doing that in 100 different subsets of the market, and they all stay on top of these companies better than anyone ever before, they will generally figure out when orders are slowing down or picking up, when an airline will accelerate its growth, or when a price war between steel companies will abate. If you are continuously tracking and turning over a portfolio, you end up always identifying the idiosyncratic news that’s going to drive a given stock’s movements, beyond just the random noise that drives prices. That’s a general overview of what those funds do and how they think.
Examples of HF strategies
We’ve mentioned the multi-manager, multi-strategy funds, and they encompass a large number of different strategies within them. We’ve talked about the fundamentals and different strategies, but many of those funds will have systematic strategies. These range from broad-scale strategies, like looking at all the different asset prices and what correlates with what. For example, if there’s a view that deviations from these correlations will snap back. So, if oil stocks generally move together with the price of oil and then one stock is lagging, that’s the one you buy and you short a basket of other stocks against it. You can also have much more sophisticated systematic strategies.
One category that goes through booms and busts is index inclusion strategies. This involves predicting who will get added to or removed from the S&P 500 or other indices. The first order problem is predicting who gets added or removed based on explicit index inclusion criteria and your view of the index committee’s decision-making. You’re also trying to bet on the volume of trades that is already making this bet. For instance, if the index inclusion means that the index funds have to buy 10 million shares of Company X, but traders betting on that inclusion have already bought 15 million anticipating selling them to the index, then it’s actually a bad catalyst. When the inclusion happens, they are trying to sell more stock than the index funds want to buy.
Another style that goes in and out of fashion is global macro, which can be split into two things with opposite cycles. One is doing these global relative value trades, where you look at the world and basically look at which countries seem to be converging in terms of standard of living and government norms with the United States. You buy their currencies or revive their assets, expecting that convergence to continue. The other is, you look at the state of the world, decide something is totally unsustainable, figure out what’s going to break, and find the most cost-effective way to bet that it breaks. This kind of strategy can work extremely well during a crisis or sometimes when there is a crisis in one place or some outlier event.
Every time there’s an election surprise, you wait a couple of weeks, and you’ll find out that some macro hedge fund is up significantly, like 300%, because they had a massively levered bet on something like Argentinian stocks. They were the only ones who truly believed it would happen and that the rally would be as magnificent as it was. Regarding Brexit, there was a lot of activity where hedge funds were commissioning private polling and trying to track the developments over time. They tried to predict what would happen and, if so, what the magnitude of the price impact would be.
Risk-parity and 60/40 being implicit macro bets on low inflation
If you look at a long chart of the equity and fixed income correlation, you see that the sign flips depending on the level and uncertainty of inflation. When I started working at a hedge fund in 2012, it was a given that when stocks went down, treasury bonds went up, and vice versa. This pattern essentially started in 1998, triggered by a market dip due to long-term capital management, the East Asian financial crisis, and the Russian crisis. The Fed significantly increased liquidity, boosting bond prices, and eventually, stocks snapped back while bonds came down, channeling liquidity into the stock market. This led to the highs of 1999 and part of 2000, which was enjoyable for everyone except the short sellers.
This situation was possible because inflation had been steadily declining since the early 80s. Around 1998, it could be argued that China’s labor supply was almost infinite compared to the world’s demand for physical goods. As long as people could move from the countryside to cities to produce goods, the cost of tradable physical items like TVs, toys, furniture, and apparel would either remain flat or decline. This price drop was largely due to production moving from more expensive countries to cheaper ones, with China offering a huge labor force and good ports, plus a government eager to grow its industrial base and invest in infrastructure.
For a long time, inflation wasn’t a concern. Whenever growth slowed, stocks would drop, and rates would decrease, causing bonds to rise. This made risk parity an excellent trade. However, it turns out that risk parity is essentially a macro bet that inflation will remain low, implying that the risk-adjusted return of stocks plus bonds is significantly better than that of either one alone.
Most strategies are implicitly a bet on the yield curve
If you’re a venture capitalist, you’re interested in the tail end of the yield curve being as low as possible.
In risk parity, the preference is for the yield curve to have a traditional curve shape, essentially what people envision when they think of a yield curve.
For a market neutral or factor neutral hedge fund, the ideal scenario is for the short end of the yield curve to be high, and for it to be flat or almost inverted, indicating high volatility.
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Don’t tell the VCs, but it’s true. A flat low yield curve implies a very low growth environment where real rates are extremely low. This means that if you can invest in a company that can produce secular growth at a time when rates are low, the valuation becomes completely nonlinear. For instance, look at what companies were trading at in 2021, it was because the present value of profits in 10 years was really close to the value of those profits today. As a result, many of them were valued on a multiple of 2027’s revenue or something similar. As long as they were growing really fast, that multiple made them look quite cheap. [Kris: See Negative Interest Rates and the Perpetuity Paradox]
These things work really well when rates are extremely low. Low rates also mean there’s a lot of capital floating around. This goes back to the earlier point on what Limited Partners (LPs) want; often they seek a single digit return. If you can buy 10-year treasuries at 5%, a single-digit return is not hard to achieve with very simple assets. But if your Treasuries are earning 70 basis points, then you absolutely have to take risks. This creates an interesting feedback loop where a lot of money flows into the growth parts of the economy. Many startups sell things to other startups. So, every time another large check goes into Snowflake before its IPO, suddenly there are more Zoom and DocuSign seats being sold, more Slack seats being sold, and there’s more usage on AWS. It all feeds into the same ecosystem. If everything’s trading at a high enough price-to-sales ratio, then every dollar that goes into the ecosystem increases the market value of that ecosystem by more than that dollar.
Additionally, if companies are increasingly paying people in equity, then you don’t need much cash to keep the flywheel going for a long time. Venture capital turned out, at least in modern venture where you have an ecosystem of startups selling to other startups, to be about understanding unit economics well enough to look at companies burning cash and ask, “What are they getting when they burn that cash? How much Lifetime Value (LTV) are they getting for the Customer Acquisition Cost (CAC) they have to spend?” If that number looks good, then you could put a really high valuation on these companies.
That’s one of the things that changed in the venture ecosystem, even over the five years up to 2021. People got really good at quickly identifying companies with a product-market fit, looking at what the unit economics look like, and discounting that by looking at the Total Addressable Market (TAM) and then basically saying, someone else can also figure out these numbers, so someone else can capture this TAM. Therefore, we absolutely need to give this company massive funding. The playbook for growing a company fast by dumping a lot of money into it got very refined by that time. You could find someone who had worked at a company that scaled at that speed and who knew where the bottlenecks were. Meanwhile, some of the scaling got easier because of all of these third-party services.
You didn’t have to build out an entire internal communications infrastructure like Amazon did when they were getting started; they built their entire customer service system in Emacs Lisp. But now you would just use Front or something similar, so you don’t have to put any engineer hours into building that system, which means you can scale much faster. More of the money went more directly into the company’s core competency because everything that was non-core was somebody else’s SaaS product that you could just buy.
Why shorting overvalued or fraudulent companies is a weak hedge from a correlation point of view
I wrote a piece on shorting recently and how it’s become a worse hedge over time. The basic argument is that when people are shorting, whether it’s on an unconstrained generalist basis or within an industry, they tend to find the same companies. They tend to identify companies that are over-earning, have dishonest CEOs, or are overly promotional, and so they short them by default. Alternatively, they might do the funding short of just picking a company where nothing is going to change over the next decade. So if they have to have a short position, they could just short this and not think about it anymore.
One problem with this is that it means when there are extreme market disruptions and hedge funds are telling all their portfolio managers to cut their exposure in half as quickly as possible, they’re all selling the same stuff or, more likely, selling some of the same stuff and also buying some of the same stuff. Sometimes it’s gratifying when I’m on Twitter and I see a rumor that some pod somewhere blew up, and then I look at the stocks I’m short and see they’re all up five or 10%. It feels good to know that I’m shorting the same things the professionals are, even if I found out because that particular professional didn’t perform well and got fired.
An interesting example of this I stumbled on recently was a company called Zion Oil and Gas, which seems like a scam. They’re drilling for oil in Israel, which is one industry that Israel does not excel in. It’s one part of the Middle East where that’s not the main economic activity. But they’re raising money from American investors who think this is really cool or maybe it’s biblical somehow. The stock in Zion Oil and Gas was at $6 a share in December 2008 and then went up to $14 a share in February 2009, making it one of the better-performing US equities over that time period. This was during the depths of the crisis. I have to assume that a lot of it was that very smart people were shorting this, thinking it’s a retail promotion that’s going to run out of money and die. Then all of them were losing money on everything else they did and had to cut exposure and buy back. So the stock went up. Maybe they did a big promotion, or maybe they had some sort of financial crisis, the End of Times themed stock promotion, but a lot of the worst companies in the world all go up on bad days because everyone is covering. So it becomes harder; over longer periods, shorts do hedge a portfolio, but day-to-day, it’s more painful.
Framing the competition between retail and professional investors
Why different time horizons mean different arenas
In many ways, everyday investors will generally either have a really short timeframe and are more or less gambling, or making educated bets on minor market movements, or they’re making longer-term bets like, “I know this company, I like the company, I use the products all the time, I’m going to buy the stock and hold it for 20 years.” If you’re doing that, it doesn’t really matter if Citadel is better informed about how this quarter is shaping up. Sure, it’s unfortunate that you might have bought the stock for 10% less if you waited a week until they reported bad earnings. But if you truly believe in the company, then it’s a minor difference, especially over longer timescales. And if you’re investing continuously, saving money, and putting a little money into the market every so often, then it all averages out.
One of the nice effects that hedge funds have for you as an investor is that they price in all the incremental changes in the outlook all the time. So every time there’s a new round of data that tells you a bit about share shift within some industry, hedge funds immediately adjust to that, or they have predicted it and already adjusted. This makes you less likely to be blindsided by certain types of surprises, especially on the revenue side of consumer-facing companies. It’s broadly true that hedge funds do make the market more efficient, so you’re getting a better deal.
Hedge funds are not trying to figure out where the stock will trade in 10 years. To the extent that they are, it’s more like they’re trying to reverse engineer the process of large, long-only investors, like Fidelity and Capital Group, etc...and what incremental news flow over the next two weeks will adjust their 10-year price target in a predictable way that you can trade ahead of.
Retail advantages over pros
The single largest source of advantage in the markets, ironically, are not owned by hedge funds but by retail investors, and that’s the time horizon. Over a long enough time horizon, you can actually outperform most hedge funds if you do things with discipline. Hedge funds have some disadvantages which you can easily avoid as a retail investor. The first disadvantage is that hedge funds incur a lot of short-term capital gains tax when they make money because of trades that mostly don’t go above a year. For retail, holding a stock for over a year is not that difficult. The second key benefit is that hedge funds need to show short-term performance; monthly returns matter, quarterly returns absolutely matter. They are forced to take movements when the markets are not favorable. For instance, there’s a grossing down problem. If the markets are bad, and everybody’s losing money, that’s the time you want to be deploying capital. But what typically happens is they’re reducing their exposure to the market to figure out what is going on, and that’s when you see huge market dislocations. As a retail investor, you can sit there and say, “8% is nothing if I’m going to hold the stock for the next 10 years, I’ll just hang on to it.” And that time horizon difference is a huge source of alpha in a market that, for the most part, isn’t competed away, even with the biggest hedge funds, because they don’t have the ability to do that.
Hedge funds measure themselves on a risk-adjusted basis, and part of it is just how they’re structured and capitalized. They’re often levered, like six or eight to one is the usual ratio. So if you’re an individual portfolio manager at one of those funds, if you have a billion-dollar allocation, you think their target return is like 10% a year, but no, their target return is on the order of like two or 3% a year. Because they are hedging so many things out, they just aren’t taking enough risk to make massive returns. The risk comes from stacking a bunch of these portfolios together. And if you make a trade and it’s not working right away, you’re probably going to exit that trade because you don’t know why it’s not working. It means that hedge funds are in this constant effort to generate new ideas. There’s this idea of velocity, like if you have a portfolio and it has X amount of names, and you’re turning over all of the stocks in that portfolio every Y trading days, then you need at least one original long or short idea every workday to have a portfolio with the right structure. The median quality of the ideas is not necessarily good, but it is a volume game.
What is a hedge fund solving for fundamentally?
You’re in the risk removal game, trying to remove as much risk as possible, because you have access to cheap enough leverage that if you can consistently generate a 3% return, it’s world-class, it’s absolutely phenomenal. With that consistency, you can borrow 10 times the money and make a 30% return. So, to achieve a consistent 3%, the key being consistency, you are removing every type of risk possible. However, the challenge of doing that is you often end up in situations with many other funds trying to do the exact same thing. Hedge funds tend to get into crowded longs and crowded shorts, where everyone is following the same thesis. For example, everyone might be long Amazon and short a bunch of other e-commerce tech names, or long Booking and short out the rest of travel.
In these nuanced situations, if a company like Amazon reports earnings and beats them, but not by enough due to the high number of long positions, the stock may trade down. These funds that are long Amazon then have to sell because the earnings, though fundamentally good, didn’t meet the high expectations set by the market. In trying to remove risk, these funds actually take on a significant risk by not considering that everyone else is removing the same risk.
To avoid this problem, one strategy is to engage in areas others are not focusing on. This approach, however, can be challenging because it often means fewer resources, fewer people to talk to, fewer conferences to attend. You’re often on an island, which can be a more difficult psychological battle. When working for a large platform, especially those managing double-digit billions, you quickly realize you can’t deploy hundreds of billions of dollars in ideas that others aren’t looking at. The equity markets will tap out very quickly in those spaces. Thus, the risk many hedge funds end up ultimately taking, which they want to avoid, is the risk of everyone else doing the same thing.
[Kris: This section touches on a few ideas I’ve observed before:
- GPs have some misalignment with LPs (and non-partner PMs)
- The trading mindset is merging with investing as the focus on alpha marries and operationalizes what “trading as a business” understands with informational inputs that come from understanding what drives business fundamentals and market reaction]
The curse of hedge fund managers is that they start out because they enjoy picking stocks, building systematic models, or day trading, but as they grow, that becomes 0% of their job. Instead, 100% of their time is spent on risk management, investor relations, or recruiting. They end up building a system that automates a lot of what they’re good at and then have to find their own idiosyncratic source of returns. If a hedge fund has access to the best prime brokers, best exchange connectivity, and best algorithms for implementing trades with low slippage, they need to gain an idiosyncratic return by hiring unique people early and onboarding them effectively.
A significant part of the business becomes structuring the trade in a way that defines a person’s incentives and non-compete agreements to capture as much of the alpha as possible at an acceptable price. These funds often offer experienced portfolio managers guaranteed bonuses and agree to hire them at the beginning of a non-compete, allowing them to wait it out. The hedge fund entity’s trade is about defining the person’s incentives so that they capture as much alpha as possible.
From the LP perspective, a hedge fund is like a marvelous treasury bond, producing a stable, non-correlated, and safe return. From the GP perspective, it’s more like a venture fund, looking for the handful of superstars who will consistently generate that 3% growth every year to make the business the best it can be.
Surprisingly, the big platform funds like Point 72, Millennium, Citadel, and Balyasny, which have backgrounds in day trading and systematic fixed income, do not come from a background of deeply assessing management integrity, which was a focus of Tiger Management. Tiger Management, once one of the biggest funds, wound down but seeded funding to its best analysts and network, creating an implicit multi-manager fund. However, they didn’t have the central risk management that current multi-strategy platform funds have. Julian Robertson’s funding led to a sort of implicit multi-manager fund, but they all used very similar strategies and often crowded into the same stocks.
This paradox shows that a background in assessing portfolio managers and analysts does not necessarily translate to success in managing a multi-strategy platform fund. The people who excelled at it were those who deeply loved creating the game.
“Peak-pod thesis” and efficiency
If you look back, there was a time when hedge fund returns significantly outperformed the market. However, starting around 2000, this gap began to shrink, and by 2010, it was minimal, closely aligning with the drag from fees and taxes. Hedge funds were once consistently generating a lot of alpha, but that started to decline. Now, the quality of reported alpha is higher, with more funds truthfully reporting no net market exposure or accurately disclosing their exposure and additional returns. However, as the skill level of investors increases and they understand the model better, the quantity of alpha available inevitably shrinks.
Hedge funds have become so proficient at generating ideas and maintaining a certain hit rate that they continue to produce risk-adjusted returns. But as more capital flows into these strategies and into competing funds, it becomes harder to execute large trades. The industry might reach a peak where the role becomes more routine and systematized, potentially leading to lower compensation per person but still remaining a significant job category.
Regarding total investment returns, imagine a stock market chart resembling a zigzag line deviating from a straight linear path. The area under this zigzag line represents the total market returns, predominantly beta. Alpha is the difference between this zigzag line and the linear path. In a market where volatility is high, hedge funds tend to perform better because the deviation from beta is greater, thus increasing the total alpha available. The current question is whether we have reached the peak number of portfolio manager “pods.” This depends on the total market volatility, which has been increasing due to higher interest rates, suggesting a potential for more pods and higher alpha generation.
However, if interest rates decrease and market volatility diminishes, hedge funds may face challenges in maintaining their current levels of alpha generation. They would need to diversify into other sectors to find new sources of volatility and alpha. Theoretically, if the market were to move in a perfectly linear trajectory, there would be little need for hedge fund pods, but such a scenario is unlikely to occur.
The concept that alpha sums to zero before taxes and transaction costs is crucial. If you’re making above-average returns, it’s typically because someone else made less optimal trading decisions, either buying high when you sold or selling low when you bought. Hedge funds rely on a supply of traders who are either valuation insensitive or simply poor at trading. However, this reliance draws other traders to exploit the same opportunities.
In The Laws of Trading you hear alpha doesn’t last forever, and this applies to both positive and negative alpha. For instance, negative alpha can occur in large pension funds that execute market orders for stocks every two weeks when employees contribute. Over time, traders might notice this pattern and begin buying these stocks a day earlier, selling them back to the pension fund at a higher price, thus reducing the fund’s impact and making it harder for them to systematically lose money. If it were possible to deliberately lose substantial money consistently, then inversely, one could make money by doing the opposite of their losing strategy. In public markets, it’s almost impossible to consistently lose money in absence of significant transaction costs.
How this can get quite meta
Concerning alpha capture, multi-manager funds analyze their portfolio managers’ decisions to determine their strengths and weaknesses. They can identify managers who consistently perform poorly with certain stocks or situations. This information helps build a meta portfolio that represents what the firm’s portfolio would look like if managers were perfectly self-aware of their abilities. Interestingly, someone who is consistently wrong about a particular stock, like consistently mispredicting Nvidia earnings, can be valuable. Their predictability, even in failure, can be leveraged by a quant model to generate profits by taking the opposite position.
This leads to a somewhat disconcerting situation where a financial professional might realize their value came from consistently incorrect predictions about a specific stock, contributing to their firm’s success by serving as a reliable contrary indicator. It’s this weird Marxist alienation from your labor, where if you find out that you had a really lucrative financial career, and it was entirely because you were really, really bad at Netflix earnings or something, but you were so bad that the quant model realized it would just fade you in much larger size every single time and make money like that’s gonna be a depressing realization. But someone, someone someday will probably come to that realization that they were just so reliably bad in certain situations that they actually made their employer money.
Understanding the good and bad of the job can help you determine if pro investing is for you
It’s exhilarating to feel like you’re always in the flow, that when something happens, you either anticipated it or are among the first to grasp its implications and strategically position yourself. That’s a thrilling feeling, although it’s not the norm. Usually, you feel clueless, underperforming, and stressed by random bad news. It’s like walking into the office and getting hit in the face. But occasionally, it’s extremely fun. The most gratifying things often come through ongoing stress and suffering. If you learn to enjoy that, you’re set.
Working at a hedge fund is unique because of the day-to-day variability. You’re dealing with extreme uncertainty and making decisions where being wrong 45% of the time means you’re top-notch. If you value intellectual honesty and variety, it’s a fantastic career.
However, when things go bad, they can be drastically different. The high level of trust and unpredictability can significantly impact your personal life.
There’s a trend of hedge funds starting venture practices and vice versa. It’s interesting to see if there will be more crossover, as both sectors tolerate a high rate of being wrong. One key difference in venture capital is the longer feedback loop. You won’t know if you’re a good venture investor for many years, unlike the quicker feedback in hedge fund investing.
The hedge fund industry is known for high burnout rates. Many enter in their early to mid-20s and leave by their 30s. Often, these employees haven’t experienced a full market cycle; they’re hired in good times and shocked by downturns. For instance, the downturn in Q4 2018 was mistaken by some as an apocalypse, but it was followed by a great year, giving a misleading impression of real downturns. In 2022, with an actual downturn, the industry faced a harsh reality check.
Updating is something people do a lot within a cycle on kind of minor stuff, like on Netflix, for example, it was more of a net subscriber additions story for a long time, and then became more of a revenue story. And it was also partly a margin story. However, when there’s a quarter where you correctly predict the net adds but get the revenue wrong, and the stock reacts more to revenue, you must quickly adjust your focus.
You have to very quickly tell yourself “the thing I was really good at predicting actually does not matter as much as this other thing. And so now I have to get good at predicting that.” And it’s when the really big shifts happen — like when the focus shifts from growth to profitability, or when we can’t assume infinite capital or money having zero cost doing lazy discounts. Now you actually have to think about what is the value of 50 cents in five years versus $1 in 10 years, instead of treating $1 in 10 years is worth roughly $1 today.
The ability to quickly adjust perspectives and decide what matters is crucial. Adapting your mental model rapidly during major shifts, such as a shift in focus from growth to profitability, is challenging.
Those who can adapt and last through multiple market cycles do extremely well due to their growing experience and opportunities.
[Kris: See 5 Takeaways From Todd Simkin on The AlphaMind Podcast to understand how a trading firm trains cognitive flexibility. This is especially important when you hire smart people who aren’t used to be wrong. This is echoed below.]
There’s a saying: “a smart person knows what to do, and an experienced person knows the exceptions to what to do.”
The average age in a hedge fund is relatively low compared to many other industries, including their mutual fund counterparts. You often see people working in hedge funds who have had a series of successes throughout their lives to reach their current positions. The typical profile of an analyst, for example, is someone who excelled in high school, attended a prestigious university, graduated at the top of their class in finance or economics, then went on to work at a major investment bank. After one or two years, they’re recruited from that investment bank to a top private equity shop or hedge fund. It’s a chain of success where they haven’t experienced significant career failure.
However, once in a hedge fund, the measure of success is not about the ability to study well or work hard. The skills required for success in a hedge fund are different from those correlating with educational success or early career achievements at places like Goldman Sachs or Morgan Stanley, where hard work is more directly linked to success. In a hedge fund, working harder does not necessarily equate to generating more alpha. If it did, everyone would be working 20-hour days.
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