What is a Quantitative Hedge Fund?

of Quantitative Hedge Fund Training

Brief Summary of Hedge Funds

Hedge Funds, broadly speaking, are investment funds that have less regulation and more flexibility relative to other, “classic” investment funds, such as mutual funds (more on this distinction is written below). A Hedge Fund will have an investment manager, and will typically be open to a limited range of investors who pay a performance fee to the fund’s manager on profits earned by the fund.  Each Hedge Fund has its own investment philosophy that determines the type of investments and strategies it employs.

In general, the Hedge Fund community undertakes a much wider range of investment and trading activities than do traditional investment funds. Hedge Funds can employ high-risk or exotic trading, such as investing with borrowed money or selling securities for short sale, in hopes of realizing large capital gains. Additionally Hedge Funds invest in a broader range of assets, including long and short positions in Equities, Fixed Income, Foreign Exchange, Commodities and illiquid hard assets, such as Real Estate.

The first hedge funds were thought to have existed prior to the Great Depression in the 1920s, though they did not gain in popularity until the 1980s, with funds managed by legendary investors including Julian Robertson, Michael Steinhardt and George Soros.   Soros gained widespread notoriety in 1992 when his Quantum Investment Fund correctly bet against the Bank of England by predicting that the pound would be devalued, having been pushed into the European Rate Mechanism at too high a rate.   Soros’ bet paid off to the tune of $1 billion, and set the stage for future hedge fund entrants, who speculated on markets based on fundamental and quantitative factors.

Hedge Funds:  How Do They Differ from Mutual Funds?

Similar to Hedge Funds, mutual funds are pools of investment capital. However, there are many differences between the two, including the following:

  • Mutual funds are regulated by the SEC; Hedge Funds are not.
  • Hedge Fund investors must be accredited (meaning they have a certain amount of liquid assets).
  • Many mutual fund strategies are “long-only,” suggesting that individual securities cannot be sold short (although increasingly, long-only managers are able to sell short indices via futures and options).
  • Mutual funds generally do not have a performance fee, generally only charging a management fee.

Hedge Fund Revenue Structure

Hedge funds charge both a management fee and a performance fee. While this varies by fund, typical management fees consist of 1-2% of assets under management and performance or incentive fees of approximately 20% taken from gross profits.  The performance fee is a key defining characteristic of a hedge fund, motivating the hedge fund manager to generate superior returns by aligning his interests with those of the investors. In contrast, mutual funds and long-only managers usually charge only a management fee.

Hedge Fund Industry Today

Total investor capital inflow allocated to hedge funds in Q1 2012 exceeded $16 billion, with the number of funds having increased for 9 consecutive quarters to reach 7,477 total funds as of Q3 2011.  Asset growth has risen faster than growth in the number of new funds, implying investor preference for allocation to the industry’s largest firms.  As of Q4 2011, assets under management across all hedge fund strategies was estimated at $1.641 trillion (with an additional $315 billion residing with managed futures/CTA accounts), reflecting a drop of more than $200 billion from Q2 2011.

What is a Quant Hedge Fund?

A Quantitative Hedge Fund is any Hedge Fund that relies upon algorithmic or systematic strategies for implementing its trading decisions. Quant trading strategies may focus on any asset class (equities, derivatives, fixed income, foreign exchange, commodities, etc.), with trades that are based on systematic strategies, rather than discretionary decisions.  In other words, at least to some degree Quantitative Hedge Funds employ “automatic” trading rules rather than ones that employees at the fund identify and evaluate. Of course, these two strategies can be mixed, but nearly all Hedge Funds are either primarily a Quant Hedge Fund or primarily a non-Quant Hedge Fund.

For the rest of this discussion, we will refer to non-Quant Hedge Funds as “Fundamental Hedge Funds”—in other words, funds whose investment style is largely or entirely driven by fundamental research that attempts to value securities in the marketplace and identify “undervalued” and “overvalued” assets.

Both Fundamental and Quantitative Hedge Funds may use fundamental information, such as economic data, accounting/financial data as well as governmental, demographic and industry measures of supply and demand.   However, the primary difference is that Quantitative Analysts will look to use this data in a systematic, automated way. Often, the Quantitative Analyst will use tens if not hundreds of different types of data to predict a single output (rules about which assets to buy and sell); these analyses will then be used to identify attractive long and short positions. Much of this data will take the form of time-series information (for example, Ten-Year Treasury yield over time), or cross-sectional information (for example, different Price/Earnings ratios for companies in a given industry).  Quantitative Analysts will not perform detailed, “bottom-up”  fundamental analysis of stocks or other individual securities; rather they may try to get a sense of the relative attractiveness of dozens or hundreds of different assets simultaneously.

Similarly, Quant Hedge Funds will rarely employ macro-driven analysis like those found at a Global Macro Hedge Fund, such as monetary policy and its impact on bond markets or currency exchange rates, or assessments of political stability or labor relations in a given market. For Quant Analysts, this data is most likely too subjective, unless it can somehow be rigorously quantified.

In short:  all Hedge Fund managers may analyze fundamental factors, but Quantitative Hedge Funds will not use any qualitative or subjective information that cannot be aggregated systematically and statistically analyzed.  A Quantitative Hedge Fund will base trading decisions on a mathematical model (which may be populated in part by fundamental factors), but there is generally little human judgment with respect to trading decisions outside of this model. In other words, Quantitative Analysts try to develop intelligent models that predict which trades to make.

Quantitative Trading Models

Quantitative Hedge Funds development complex mathematical models to try to predict investment opportunities—typically in the form of predictions about which assets are projected to have high returns (for long investments) or low/negative returns (for short investments). As computing power has blossomed over the past couple of decades, so has the use of sophisticated modeling techniques, such as optimization, prediction modeling, neural networks and other forms of machine-learning algorithms (where trading strategies evolve over time by “learning” from past data).

One common, classic Quant Hedge Fund modeling approach is called Factor-Based Modeling. In this data, predictor (or “independent”) variables, such as Price/Earnings ratio, or inflation rates, or the change in unemployment rates, are used to attempt to predict the value of another variable of interest (“dependent” variables), such as the predicted change in the price of a stock. Factor models may base trading decisions on a pre-determined set of factors (such as returns on the S&P 500, the U.S. dollar index, a corporate bond index, a commodity index such as the CRB, and a measure of changes in corporate bond spreads and the VIX) or a set of factors related mathematically (but with no explicit specification) such as those gleaned through Principal Component Analysis (PCA).

A generalized one-factor model attempting to predict returns R using  factor F would take the following form:

Rit = α + βiλ + βiFt + σiεit

where Rit, i = 1, … , N and t = 1, … , T, is the excess (over the risk-free rate) return of asset i at time t, and Ft is the factor under consideration with zero mean and variance σ2F.

Clearly, this model can be expanded to include multiple factors in predicting R.

Factor models may incorporate fundamental information, including determinants of “value” versus “growth” stocks such as cash flow multiples, sales to price ratios, P/E to earnings growth figures, dividend payout ratios, return on equity and the like.  For fixed income models, such factors tend to be related to macroeconomic variables (such as industrial production, employment growth, or inflation relative to trend) or factors such as interest coverage ratios or Debt/EBITDA.   Tests may be run on portfolios of securities (grouped by cross-section) or on individual securities.   Such models may also use prior price momentum to capture behavioral trends that may be correlated to future price performance, and may incorporate measures of supply versus demand (such as open interest in puts versus calls or money flow metric).  Sentiment indicators, such as analysts’ earnings revisions for equities or economists’ GDP growth estimates for fixed income, may also appear as factors.

Models may be separated by sub-groups (different equity sectors, for example), may have interaction terms (F1t may appear as an individual factor in addition to the product of F1 and F2, i.e., F1t × F2t ) or may have dummy variables (for example, D1 may represent a dummy variable for whether a company is larger than a certain size).  Factor models need not be linear; quadratic terms (and higher-order terms) may be added to a linear model as well.

Note that factor models may be used in a for both return prediction and risk modeling purposes—with the goal that specific factors can be used to explain the degree of variability in performance on an absolute basis or relative to the factors being modeled.   Improved risk management techniques, designed to weight strategies according to different markets conditions and changes in liquidity and sentiment, are gaining more attention, particularly against a macroeconomic environment where policy tools (and their associated impact on the markets) are unprecedented.

Note that there are risks with using quantitative models to predict assets returns. Factor-based models, for example, use historical data to determine the relationship between factors and returns. These relationships may not continue. Non-linear relationships among the variables may go undetected. Also, unprecedented events are likely not to be captured in historical data. Finally, some traditional quantitative approaches (such as factor-based models) can fail to adapt to changing market conditions, using the same set of static factors, which “work”—until they don’t. This happened to many Quant Hedge Funds in 2008, when many funds had similar positions in similar assets, because they were looking at similar sets of factors. When those positions started losing value and funds were forced to reduce or eliminate those positions, the losses increased, triggering further selling, etc.

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