Demystifying Quant Hedge Funds

People have long noted the esoteric and secretive nature of hedge funds, especially those that employ quantitative (“quant”) strategies. While hedge funds typically do not let outsiders in on the ‘secret sauce’ of their investing strategies, quant hedge funds have a reputation of taking this to the extreme and are often referred to as “Black Box[es].”

A quant fund is an investment vehicle whose securities are chosen based on numerical data, often compiled proprietarily, and quantitatively analyzed. In other words, fund managers develop algorithmic computer programs to create and systematically execute several types of investment strategies depending on the inputs and instruments traded.

Quant fund managers feed data inputs into a system, which analyzes the data. Trading signals, designed and programmed by the quant fund managers, will then establish a trading position based upon the data, which can range from economic data points (e.g. GDP estimates, etc.) to market- or company-specific news reports to price trends in asset values (such as stocks and bonds, including sector-specific exposures), among others. Quantitative models can be simple or extremely sophisticated, and many are built to take advantage of persistent market anomalies, such as trend following/momentum-based trading and mean-reversion/value investing.

Quant hedge funds differ from discretionary hedge funds, which generally base their investment strategies on deep fundamental research and human judgment. Quant funds, on the other hand, remove the human element and rely on mathematical and statistical modeling. Quant funds can also utilize fundamental data but rarely dive as deep into a company as a human conducting fundamental research. However, the emergence of natural language processing techniques is gradually enabling machines to dive deeper than ever before. While thoughts of fast-paced trading floors with multiple monitors displaying complex charts may enter one’s mind with regard to hedge funds, quantitative analysis can actually provide a calmer, more systematic approach that removes emotions from the investment process.

Quant strategies that heavily rely on computer technology for investment decisions might sound foreign or even scary to certain investors, especially to those who have any apprehensions regarding the idea of technology controlling their money. But today’s quantitative investing is a bit different from the typical consumer product or website glitch; it is honed with large troves of historical data mixed with high-powered computing as well engineering/mathematical expertise from some of the brightest minds assembled under the same roofs. With the proliferation of more and more data than ever before, a strong argument in favor of systematic funds is that humans cannot possibly digest all of the relevant information available to them as efficiently as computers.

There are numerous points in history that people point to as the birth of, or basis for, quantitative investing. Investopedia’s Adam Hayes believes that quantitative analysis finds its origins in the famous Security Analysis, written by Benjamin Graham and David Dodd in 1934. Viewed by many as the bible to investing, the book promoted making investments in public companies based on rigorous measurement of objective financial metrics. Reuters, on the other hand, believes that quantitative analysis as we know it today was born out of Harry Markowitz’s Nobel Prize-winning “Modern Portfolio Theory”, an exercise in mean-variance optimization showing that a portfolio’s diversification leads to risk reduction. This “Modern Portfolio Theory” eventually led to the publication of William Sharpe’s Capital Asset Pricing Model, which separates systemic risk (the risk that affects all securities in a market) from asset-specific, or idiosyncratic, risk.

But computers did not truly enter the fray until a few years after this. In the 1960s, a group of investors and economists, including Markowitz, pioneered the use of computers in arbitrage trading, marking a true birth of computational finance.

In 1969, a mathematician with a Ph.D. from UCLA, named Edward O. Thorp, entered the fray with his first hedge fund, Princeton/Newport Partners. As one of Wall Street’s first quants, Dr. Thorp used his knowledge of probability and statistics to discover and exploit a number of pricing anomalies in the securities markets, managing his portfolio with mathematical formulas, economic models, and computers. (Dr. Thorp also was one of the earliest skeptics of Bernie Madoff, calling him out for fraud as early as 1991!) The focus of computational finance, the earliest quantitative investing programs, shifted during the 1970s to options pricing and mortgage securitizations. Fast-forward to the 1980s: with the Cold War winding down, physicists, applied mathematicians, and codebreakers around the world found themselves with a bit more free time, leading them to new careers as financial engineers. The quantitative revolution as we know it began in these formative years.

In many respects, the quant hedge fund revolution became more notable with the rebranding of Monemetrics to Renaissance Technologies in 1982. The birth of Renaissance, a quantitative hedge fund founded by Jim Simons, launched a revolution of predictive modeling and historical data dredging that was mainly focused on gaining a competitive edge. Armed with a team of academics with degrees in computer science, physics, mathematics, and engineering, Simons aimed to beat the market by utilizing data dating back to the 1700s and models based on human behavior.

There are myriad quantitative strategies employed by hedge funds today, of which some of the most popular are outlined below:

Sources:
Street of Walls
High-Frequency Trading (HFT) Definition (investopedia.com)

As tales of the market-beating quantitative strategies reach the masses and gain notoriety, the amount of hedge fund money in quantitative investments has also grown steadily over time. The appetite for hedge funds overall has ebbed and flowed over recent years but has edged towards an industry all-time high of nearly $4 trillion this year, according to Hedge Fund Research. And the interest in quant hedge funds has not waned in 2021, as the quant asset classes have reportedly seen some large net inflows for the year, helping increase total industry market share to over 40% of overall assets.

According to SIGTech’s Hedge Fund Research Report for 2021, a poll of 100 leading hedge fund managers showed that 80% of those polled expect institutional investors to increase their allocations to quantitative strategies this year, with 51% expecting a ‘slight’ increase, and 29% forecasting a ‘dramatic’ increase.

According to Håkan Samuelsson of The Robust Trader, algorithmic trading generates 70-80% of overall trading volume in the U.S. and many other developed financial markets.

Quant hedge funds may feel like a mystery, but the strategies employed by these managers have unique attributes that potentially make them desirable for one’s portfolio, helping take the emotion out of decision making and systematizing financial security. Although quant investing is complex and comes with inherent risks, with careful manager selection and a partner who understands how to evaluate the performance of quant funds, such strategies can unlock potential for those seeking to be part of the continuing story of the quant investing revolution.

Sources:
Investopedia, May 2021. “Quant Fund.”
Yahoo!, May 2021. “What Is a Quant Hedge Fund?”
Reuters, December 2008. “CHRONOLOGY: History of quantitative analysis.”
Wikipedia, “Computational finance.”
Street of Walls, “Quantitative Trading Strategies.”
Medium, April 2020. “The quant revolution; Jim Simons and Renaissance Technology.”
NYMag, January 2018. “How a Misfit Group of Computer Geeks and English Majors Transformed Wall Street.”
ValueWalk, August 2017. “How Ed Thorp Delivered 20% Returns For 30 Years.”
The Robust Trader, October 2021. “What Percentage of Trading is Algorithmic?”
Hedge Fund Research, October 2021. “Global Hedge Fund Capital Steady as Industry Positions Inflation, Rising Rates.”
Hedgeweek, October 2021. “Hedge funds edge towards USD4 trillion milestone as volatility surges.”
Hedgeweek, August 2021. “Quant hedge funds tipped to flourish with institutional inflows set to rise.”