- We examine the manager performance by forming average monthly returns in each category
- Discretionary equity managers have delivered higher raw returns than systematic equity managers. However they take more risk and have higher factor exposures. After adjusting for these, the appraisal ratio for systematic managers is slightly higher
- Discretionary macro managers have underperformed systematic macro managers. After adjusting for volatility and factor exposures the underperformance is still clear (lower discretionary appraisal ratio)
- Factor attribution
- We select a set of factors that were well known at the beginning of our analysis
- For discretionary equity managers, more of the returns can be attributed to factor exposures compared to systematic equity managers. This mainly comes from a long equity exposure
- For macro managers, both discretionary and systematic have a long exposure to the bond market factor and a volatility factor. Discretionary managers also have exposure to the equity market and FX carry factors. The total amount of returns attributable to factors is again more for discretionary than for systematic managers
- We find that discretionary and systematic managers have similar levels of performance spread between top and bottom quartile managers in each category
We compare the performance and risk exposures of discretionary and systematic managers. Discretionary managers rely on human skills to interpret new information and make day-to-day investment decisions. Systematic managers, on the other hand, use strategies that are rules-based and implemented by a computer, with little or no daily human intervention.
In our experience, some allocators to hedge funds, including some of the largest in the world, either partially or entirely avoid allocating to systematic funds. The authors have heard various reasons, such as: systematic funds are homogeneous, systematic funds are hard to understand, the investing experience in systematic has been worse than discretionary, systematic funds are less transparent than discretionary and that they are bound to perform worse than discretionary because they only use data from the past. These reasons seem to be consistent with a distrust of systems, or “algorithm aversion”, as illustrated by a series of experiments in Dietvorst, Simmons, and Massey (2015). In line with our experience and algorithm aversion, only 31% of hedge funds are systematic and they manage just 26% of the total of assets under management (AUM), as at the end of 2014.
In this paper, we show that the lack of confidence in systematic funds is not justified in our opinion when comparing their performance to that of their discretionary counterparts. Our analysis covers over 9,000 funds from the Hedge Fund Research (HFR) database over the period 1996-2014. We classify funds as either systematic or discretionary based on algorithmic text analysis of the fund descriptions, as the categories used by HFR do not provide an exact match for our research question. We consider both macro and equity funds.
Our main results are summarized in Exhibit 1. In the first row, we report the average (unadjusted) return for the different styles considered. All returns are in excess of the local short-term interest rate. Hedge fund returns are averaged across funds of a particular style (i.e., we form an index) and are after transaction costs and fees. Based on unadjusted returns, systematic macro funds outperform discretionary macro funds, while the reverse is true for equity funds.
In the second row, we report the amount of the return that can be attributed to well-known and easy-to-implement risk factors, based on a regression analysis. For discretionary funds, more of the return can be attributed to factors than for their systematic counterparts. We consider three sets of risk factors: traditional factors (equity, bond, credit), dynamic factors (stock value, stock size, stock momentum, FX carry), and a volatility factor. The latter is defined as a strategy of buying one month, at-the-money S&P 500 calls and puts (i.e., straddles) at month-end and letting them expire at the next month’s end. In rows three to five of Exhibit 1, we show the attribution to the three underlying sets of factors. For all four styles, the return attributed to traditional factors is meaningful, as it ranges from 1.5% to 2.2%. The return attributed to dynamic factors is also positive in all cases, ranging from 0.2% to 1.3%. The return attributed to the volatility factor is negative for systematic and discretionary macro funds, at -3.2% and -1.3% respectively, and close to zero for equity funds. Macro funds on average have a long exposure to the volatility factor, which has negative returns over time. The negative risk premium for the long volatility factor makes sense, given that being long volatility can act as a hedge for holding risky assets in general. Correcting macro funds’ returns for their long volatility exposure essentially gives them credit for this hedging characteristic.
In the sixth row of Exhibit 1, we report the average risk-adjusted return, which is simply the difference between the average unadjusted return and the return attributed to risk factors. Systematic macro stands out with an average risk-adjusted return of 4.9%. Discretionary macro has an average risk-adjusted return of 1.6%, while systematic and discretionary equity funds have similar values at 1.1% and 1.2% respectively. However, the risk-adjusted returns of systematic macro also have the highest volatility, as shown in the seventh row. In the eighth row of Exhibit 1, we report the ratio of the average risk-adjusted return to its volatility, called the appraisal ratio, and see that systematic macro still outperforms, but by less.1
All in all, the above results show that the hedge fund styles we consider have historically realized positive alphas, which are determined: (1) in excess of the short-term interest rate, (2) after transaction costs and fees, and (3) corrected for any return attributed to risk factors. We note that the factors themselves (especially the dynamic factors) cannot be produced for zero cost, and so a manager simply implementing these factor exposures would undoubtedly show a negative alpha.
The empirical analysis conducted in this paper allows us not only to comment on performance statistics, like the alpha and appraisal ratio, but also on the return variances explained by the risk factors. We find that for systematic funds a slightly smaller proportion of variance is explained by the factors (both for macro and equity funds). A much larger proportion of variance is explained by factors for equity funds than for macro funds. This is mostly driven by a long equity market exposure in equity funds. For investors who already have a meaningful investment in equites outside of their hedge fund portfolio, it seems imperative to take this into account.
Finally, we look at the dispersion of manager returns (results discussed above were based on an index for each category). We establish that the dispersion in Sharpe and appraisal ratios across funds within a hedge fund style is similar (and large) for systematic and discretionary funds. This means that the common investor complaint that systematic funds are more homogeneous does not appear to stand up to scrutiny. So, in addition to style selection, fund selection seems to be just as important in each category. Particular attention should be paid to this when holding a concentrated portfolio of hedge funds.
This paper proceeds as follows. In Section 1, we describe the hedge fund data and text analysis used to classify funds as either systematic or discretionary. In Section 2 we discuss the risk factors. We analyse the alpha and exposure to risk factors for systematic and discretionary macro funds in Section 3. In Section 4 we repeat our empirical analysis for equity funds. We discuss the diversification potential of different hedge fund styles and some fund-level results in Section 5. Finally, we offer some concluding remarks in Section 6.
1. The appraisal ratio is given by the ratio of the average risk-adjusted return and the standard deviation of the risk-adjusted return. It is the risk-adjusted analogue to the Sharpe ratio, which is based on the average and standard deviation of unadjusted returns.