The first investors to take advantage of computers to process data and make investment decisions were ‘quant funds’ that continues today to be successful at scale. The tools at their disposal were statistics and linear models – limited by the computational capacity and the availability of structured data to apply statistics to.
This article is an in-depth discussion on how quantitative hedge funds work. The content is mostly based on an excellent discussion from Meson Capital’s Q1 2017 letter to investors. We performed some minor edits and added explanations at the end. To see the original discussion on quant funds please see the link at the bottom of this article.
What is a Quantitative Hedge Fund?
Today – the successful quant hedge funds have become enormous, managing $30+ billion and the smaller players moved towards high-frequency trading. At their core, traditional hedge fund strategies are based on statistical correlations: i.e. linear models. At a small scale – stand on the surface of the earth, the horizon looks flat – step back to big scale in space and you see the curvature. The same idea with statistical correlations with time scale – in the next day or week the linear correlation can be a fairly good representation of reality but over the next year, you’re in the flat earth society.
The consequence of this is that the strategies tend to be high-turnover: a 1 week holding period means trading your portfolio 52x per year. Strategies with lots of trading tend to require scale to have low enough trading costs (including the infrastructure to execute) to be practical.
But of course scale (>$1B+ AUM) means that you can’t take meaningful positions in small companies to move the needle. This size barrier to entry has meant there have been few new entrants to the quant fund landscape for some time and why, despite having an engineering background, we did not focus on this direction at first. Long-term success is driven by competitive advantage and we had little chance of creating against large incumbents playing their game.
Instead, our approach has been to ‘depth first search’ as investors by being entrepreneurs and activists and invest in smaller companies where we could be the largest and most sophisticated – and energetic!
– Stakeholder. This gave us a competitive advantage and a number of demonstrated successes. Along the way we have thought hard about how to generalize what we have learned about what drives the change in intrinsic value over time and codify it so that we could continue to screen and search for similar situations later in a systematic way. Lots of investors know how to look for clues to a stock being mispriced relative to its apparent intrinsic value NOW but very few have been in the trenches seeing how intrinsic value can increase or decrease from management decisions in the boardroom.
How a Quant Fund Works: Machine Learning Changes the Game
Starting several years ago the landscape around the traditional quant funds shifted. New technology has allowed for 1) the ability to work with unstructured data (i.e. natural language) that can be gathered less expensively and 2) nonlinear predictive models. These tools were extremely expensive until the last year or two and impractical to use for the investment process. Now it’s possible to build a machine learning investing system with a small group of talented engineers using open source software and low cost cloud computing. Add to the formula an activist investor, who also happens to be an engineer, to help direct what data factors are important to predict how a company will perform in the future and that is exactly what we have now built. I introduce: Meson Gravity, our machine learning system to predict the long term performance of companies using data.
Our approach, although utilizing computational tools, is fundamentally the same business-focused approach we have been deploying for years as a hedge fund employing a proactive perspective. The term “quantamental” (quantitative + fundamental) has been recently popularized to describe this class of quantitative hedge funds. We aren’t competing with other quants on trading-like timescales – we continue to focus on small companies where the markets are less efficient and we can compete against other predominantly human, emotional, biased investors. Now we have a machine. Most of our competitors don’t, because it is very hard to build. What usually happens when machines compete against humans at the same game? These are the years when machines defeated the human at-the-time world champions: Checkers (1990), chess (1997), Jeopardy! (2011), go (2016), poker (2017).
Going back to principles in my very first letter to investors from 2009:
Hedge Fund Revenue Structure
Our investment strategy has always been to buy companies at a big discount to their intrinsic value and either a) be patient or b) act as a catalyst to close the gap or increase the intrinsic value over time. Sounds simple – but how do you determine intrinsic value? Even if you could determine intrinsic value today precisely, what if the company changes? The static view of intrinsic value is so incomplete that it can be a dangerous concept. The world is dynamic and we can only see a little bit up the road – even companies themselves can’t predict their own revenues a year without the benefit of total inside information. We can estimate a range for intrinsic values but that too is limited when framed in linear thinking tied to today.
A 30-year lease on a commercial building has a pretty clear intrinsic value but the range for operating businesses is as wide as ever. The core value of businesses is increasingly the intangible (i.e. informational) component and decreasingly the stack of bricks or factory floor with easy to observe GAAP accounting metrics. The first derivative of value is the quality of the people and the thousands of decisions that get made each day to cumulatively determine future value.
Quantitative Trading Models: A Dynamic View of Intrinsic Value
There are two potential sources of return when investing in financial markets: 1) Mispricing and 2) Intrinsic value change. Phrased another way, these two sources are: 1) The Market and 2) The Business. Ben Graham famously says, “In the short run, the market is a voting machine, but in the long run it is a weighing machine.” The market source of return is from a change in “votes” while the business source is a change in “weight.”
Mr. Market’s “votes” reflect the current state of the world and the clarity of a company’s prospects ahead.
The data accounting for this has continued to become more available – from credit card transaction data to Walmart parking lot satellite imagery. I can’t imagine making a strong argument for our ability to compete from a position of strength in this dimension. We have become experts in the “weighing machine” and understanding the effects of “diet” on future weight of a business, to extend the analogy to include time. This has been learned from the inside out after experience on half a dozen corporate boards and numerous other entrepreneurial experiences. I believe understanding what drives a business to change over time in a fundamental way is likely to be out of reach from the pure quant hedge funds in financial markets.
Requirements for Quant Funds
Requirements to follow these tenets and the barriers to entry are:
1) A long term approach requires non-linear models to make investment decisions – i.e. machine learning, not merely statistics. The technology to implement this has only recently become feasible at a reasonable cost and the software engineering requirements are substantial. As an investment strategy – this type of model can capture nuances about a company the same way that a human investor does.
How Do They Differ from Mutual Funds?
A mutual fund is a financial structure that encapsulates significant capital- collected by a plethora of different investors in a variety of different market instruments. Unlike a quant fund, a mutual fund is managed by human operators, who- with human knowledge and judgment, attempt to extrapolate the likely future market trends and consequently reap prolific monetary benefits for the fund’s investors and themselves.
Types of Quantitative Hedge Funds
Quantitative hedge funds can differ quite significantly from one another; ranging from relatively small-scale financial firms with small teams of maybe a couple dozen to large-scale global funds that operate in multiple continents. Unsurprisingly, therefore, such funds often tend to adopt different approaches in relation to the investment strategies that they employ.
1) Convertible Arbitrage: This involves a synchronized purchase of convertible security (such as a convertible bond) and the short sale of the acquirers common stock by targeting the existing pricing anomalies that persist between underlying shares and convertible bonds.
2) Fixed Income Arbitrage: Here, the purchaser (investing party) exploits anomalies between related bonds by purchasing securities at a relatively depreciated price and reselling them at a much higher price within a few seconds.
3) Risk Arbitrage: also known as Event-Driven or Merger Arbitrage, is an investment strategy that relies on trades that are proactively based on anticipated commercial events in the future- such as a bankruptcy declaration or a merger and consequently involves profiting from the diminished gap between the market price of stock following the aforementioned ‘anticipated’ event, and the acquirer’s initial evaluation of said stock.
1) Global Macro- A global macro strategy bases its holdings on the macroeconomic environment- often concentrating on key interest-rate advances and currencies, as well as on the overall political and economic views of relevant world economies.
2)Equity Market Neutral- Equity Market Neutral Strategy relies on a fund’s forecasted exposure- both in the short and long term, by hedging against the relative market trends appropriately. In practice, the manager of the quantitative fund attempts to make the most of two stock prices by consistently being in both: a short and long market position to an equal degree in any related stocks.
What Makes a Good Quantitative Fund?
We aim to compete with other human investors while utilizing our machine learning tools to achieve superior consistency and depth of research for our own fundamentally driven investment process. As long as there are emotional and biased people in the market and Jim Cramer, et al. have the attention of investors wielding meaningful capital and 95% of sell-side broker recommendations are “BUY” etc. there will be an opportunity set for a cool rational approach. Stocks are not bought, they are sold.
Core tenets of our machine learning enabled strategy include:
1) A long term fundamental approach over a year or longer, not weeks or months with short term traders. This timescale is uncompetitive with quant investors as few have the necessary conviction about a business to ride out a month or quarter that isn’t ‘working’ in the stock.
2) We focus on the deeper causal factors in our data – people, business quality over time, and supply/demand dynamics in an industry. What drives the change in intrinsic value over time for a company? Valuation metrics are important but generally too obvious to gain an edge.
3) We short for the long term: economic gravity always wins in the end with low quality businesses run by self-motivated people.
The computing infrastructure required for this would have cost millions of dollars before recent cost reductions and software advances in cloud computing. As a quick aside – why short at all if it’s so hard? A) Despite the rising index, the performance is mostly from the big winners and most stocks perform worse than T-bills. We are trying to predict the future using historical data – great businesses are almost always doing something new and harder to predict whereas failure is much easier to predict.
This is in stark contrast to ‘smart beta’ or ‘factor’ investing where the characteristics are easily measured (such as low price/book value) and arbitraged away. 2) Knowing what data to look at requires real domain expertise as a specialist investor and the ability to translate that into the same language that a machine can understand: typically orthogonal skillsets. This is the most proprietary piece of our investment process and requires meaningful effort to gather and structure data that is not available from commercial vendors.
We agree with Google’s Alon Halevy that the hardest problems and biggest breakthroughs integrating different datasets into one multifaceted view of reality.
3) To maintain a sustainable conviction in shorts, diversification is required so a short going against you doesn’t need to be reacted against adversely solely due to price action. A problem still remains: if you are right on 99% of your shorts but 1% of time the time you short Amazon in 2003, you lose. To validate our models, we had to build a proprietary simulation architecture to realistically play out thousands of alternate versions of history.
Note that this approach is very different from how typical quant funds work: rather than running 1000 back-tests to search for what strategy would have worked, we forward-test our fundamental ideas with many variations on history to reduce tail risks before anything starts. Risk control is paramount and simple metrics like VaR are inadequate. We will undoubtedly encounter new market situations that have never happened before and will respond our best, but at least we can start with knowing we won’t likely repeat the same mistakes from history.
Quant Fund Pros
Since the technological algorithms which are utilized in quant funds can survey a prolific amount of information and financial data, they are able to ascertain exceptionally unusual patterns. In turn, these can act as a signal to the managers within the quantitative funds and allow them to transcend human: judgment, investment strategies and overall track record.
Another major advantage to quantitative hedge funds is that the aforementioned algorithms can scan a wide range of strategies across hundreds of different financial markets whereas human models can seldom manage more than a few dozen positions at any one time.
Since the development of the first Exchange Traded Funds- known as ETFs back in the late 1980s, the trading strategies developed within the quantitative hedge funds have progressed significantly due to the noteworthy innovations in the sphere of Artificial Intelligence. This is because the proliferation of AI within the hedge fund sphere inadvertently implies that trading strategies can self-learn and consequently improve their: effectiveness, human judgement, and accountability.
As of 2021, such quantitative hedge funds now own over 35% of market capitalization, meaning they encapsulate more equities within the U.S. financial market than all of the human fund managers combined.
Efficiency on quantitative costs is another significant plus of quantitative funds; fees are an essential part of a quant fund’s strategy for two reasons. Firstly, over a long period of time, such costs can accumulate a significant opportunity cost for potential investors which can act as a disincentive, and secondly, in the context of a strategy based measurement of performance, high fees can serve as a significant obstacle as the higher the fees, the higher the minimum benchmark performance has to be to justify such fees.
Finally, as quantitative hedge funds can unequivocally make quicker performance-based investment decisions, they are arguably better suited to exploit opportunities from within narrow price differentials and provide a more consistently effective model; this is particularly the case when accounting for the fact that quantitative fund models omit the arguably ‘natural bias’ which reasonably persists with some of the human managers in the funds. An industry leading coder- who can introduce a wide range of variables into his AI-driven quant hedge, may outcompete an industry leading trader given the competitive advantage that can exist by simply having a better quality machine with faster computing power that can assess a higher degree of data analysis and risk management.
Quant Fund Cons
Despite the apparent fruitful advantages that quant hedge funds seem to bring in comparison to their respective counterparts, it should be noted that some skepticism in the area remains- albeit at a much lower degree than a couple of years ago.
For example, a common presumed drawback that the aforementioned quant funds will bring in the future is that- with the continuous expansion of the usage of AI in the financial fund markets, a plethora of distinct quantitative funds may in fact start to conclude the same exact decisions; obviously, in time this could very easily exacerbate an already problematic issue within the financial hedge fund market.
Moreover, a key disadvantage of quant funds- which lack a human-led managerial role, is that they lack the ability to detect individualistic market characteristics to the same extent that a human can and instead make consistent decisions made on quantitative data- not qualitative.
A final point that may be worth noting is to what extent the automated objectivity which encompasses the quantitative hedge fund models discussed above leaves pragmatic room for differentiation. For example, to what extent can a quantitative fund practically gain a real advantage over one of its rivals. In the traditional human-managed strategies, if a fund has attained a holistically better understanding of the financial market over one of its competitors, it can directly hold a competitive advantage over it- one that is gained through years of personal development, market research, and field-expertise. In fact, in an attempt to attain a competitive advantage over their competitors, some quant funds have started to create and rely on their own individual fiber optic network that directly connects them with the stock exchange market- the premise being that even the most marginal difference in latency can place one party in a significantly advantageous position over their competitors.
Hedge Fund Industry Today
The hedge fund industry has changed drastically over the last 20 years and in particular the last 10. Market conditions have caused many a quant funds manager with strong track records to be humbled by amateurs trading on Robinhood. Market conditions always change but many quantitative hedge fund managers have not been able to adjust their investment strategies to the post 2008 Federal Reserve supported markets. Additionally, quant trading strategies have been hard to implement when asset classes constantly go up. Many decisions based on both algos and human judgement by quant fund traders requires a divergence of returns, which has not been the case recently.
The above has been true in particular for long short hedge fund managers. Those with significant short books have been wiped out by the endless bull market. Whether an investing or trading strategy, a relentless uptrend in assets is more beneficial to those with a strategy levered to high beta assets.
Do hedge funds pay well?
Hedge funds tend to pay incredibly well. Fund managers with higher assets under management make more and can afford higher salaries, but even smaller hedge funds have good pay. According to a recent survey, hedge fund portfolio managers expect to earn $346,164 in base and over one million dollars when bonus is included.
Who runs the largest hedge fund?
Ray Dalio’s Bridgewater Associates tops the list of the biggest hedge fund managers. The hedge fund has $138 billion in assets under management.
How much does a quantitative trader make?
Quant fund traders earn on average a high salary of 316,764. However, a significant portion of the salary depends on a bonuses which means how well the quant traders stocks returns are in a given year.
What is the biggest fund in the world?
While Ray Dalio tops the list of hedge fund managers, he is not the biggest fund manager in the world. Vanguard’s 500 Index Admiral fund, a passive vehicle, is the world’s largest with 577.38 billion in assets under management.
Do hedge funds pay dividends?
Hedge fund managers do not make cash distributions or pay any dividends. Investors receive their money by requesting withdrawals from the funds, which are many times subject to lock-ups.
Having briefly discussed the nature of quantitative funds, we will conclude by commenting on the fact that investors should- like always, conduct substantial due diligence on all market-related quant funds before they commit their money to one. Taking the necessary time and steps to grasp the fundamental differences between the distinct strategies that many quant models currently utilize, as well as their relative trading history and risk management, is a crucial part of every investment.