I have recently been browsing the great resource that is the SSRN (Social Science Research Network), looking for some Managed Futures/CTA/Trend Following papers.
Here is a list of some I have bookmarked:
Is Managed Futures an Asset Class? The Search for the Beta of Commodity Futures
We hypothesize that the classic arbitrage pricing theory contains circular logic, and as a consequence, its natural state is disequilibrium, not equilibrium. We extend this hypothesis to suggest that the term structure of the futures price curve, while indicative of a potential roll return benefit, in fact implies a complex series of roll yield permutations. Similarly, the hedging response function elicits a behavioral risk management mechanisms, and therefore, corroborates social reflexivity. Such models are inter-related and each reflects certain qualities and dynamics within the overall futures market paradigm.
With respect to managed futures, it is an observable materialization of behavioral finance, where risk, return, leverage and skill operate un-tethered from the anchor of an accurate representation of beta. In other words, it defies rational expectations equilibrium, the efficient market hypothesis and allied models – the CAPM, arbitrage pricing theory or otherwise – to single-handedly isolate a persistent source of return without that source eventually slipping away.
CTA/Managed Futures Strategy Benchmarks: Performance and Review
In this paper we provide: 1) a brief synopsis of the benefits of managed futures investment; 2) a short review of manager based CTA benchmark construction; and 3) an empirical analysis on the relative performance of various CTA benchmarks (non-investible manager based indices, investible manager based indices, and passive security based indices). In this analysis the various CTA indices are also compared on a zero risk (e.g., Treasury Bill), total risk (Sharpe Ratio), market factor risk (e.g., S&P 500) and strategy risk (e.g., passive futures based CTA index) and peer group basis (investible and noninvestible manager based indices). Lastly, for a selected set of CTAs with full data over the period of analysis an example of the use of various CTA benchmarks in determining excess peer group return, and zero risk, total risk, market risk or strategy (futures based) risk excess return is provided.
Fooling Some of the People All of the Time: The Inefficient Performance and Persistence of Commodity Trading Advisors
nvestors face significant barriers in evaluating the performance of hedge funds and commodity trading advisors (CTAs). The only available performance data comes from voluntary reporting to private companies. Funds have incentives to strategically report to these companies, causing these data sets to be severely biased. And, because hedge funds use nonlinear, state-dependent, leveraged strategies, it has proven difficult to determine whether they add value relative to benchmarks. We focus on commodity trading advisors, a subset of hedge funds, and show that during the period 1994-2007 CTA excess returns to investors (i.e., net of fees) averaged 85 basis points per annum over US T-bills, which is insignificantly different from zero. We estimate that CTAs on average earned gross excess returns (i.e., before fees) of 5.4%, which implies that funds captured most of their performance through charging fees. Yet, even before fees we find that CTAs display no alpha relative to simple futures strategies that are in the public domain. We argue that CTAs appear to persist as an asset class despite their poor performance, because they face no market discipline based on credible information. Our evidence suggests that investors’ experience of poor performance is not common knowledge.
Performance Persistence for Managed Futures
Past literature on managed futures funds has found little evidence that the top performing funds can be predicted. But, the past literature has used small datasets and methods which had little power to reject the null hypothesis of no performance persistence. The objective of this research is to: determine whether performance persists for managed futures advisors using large datasets and methods which have power to reject the null hypothesis.
We use data from public funds, private funds, and commodity trading advisors (CTAs). The analysis proceeds in four steps. First, a regression approach is used to determine whether after adjusting for changes in overall returns and differences in leverage that funds all have the same mean returns. Second, we use Monte Carlo methods to demonstrate that Elton, Gruber, and Rentzler’s methods have little power to reject false null hypotheses and will reject true null hypotheses too often. Third, we conduct an out-of-sample test of various methods of selecting the top funds. Fourth, since we do find some performance persistence, we seek to explain the sources of this performance persistence by using regressions of (a) returns against CTA characteristics, (b) return risk against CTA characteristics, (c) returns against lagged returns, and (d) changes in investment against lagged returns.
Performance persistence could exist due to either differences in cost or differences in the skill of the manager. Our results favor skill as the explanation since returns were positively correlated with cost. The performance persistence is statistically significant, but is small relative to the variation in the data (only 2-4% of the total variation). But, the performance persistence is large relative to the mean. Monte Carlo methods showed that the methods used in past research could often not reject false null hypotheses and would reject true null hypotheses too often.
Out-of-sample tests confirmed that there is some performance persistence, but it is small relative to the noise in the data. A return/risk measure showed more persistence than either of the return measures. Picking CTAs based on returns in the most recent year may even be worse than a strategy of randomly picking a CTA.
Managed Futures and Hedge Funds: A Match Made in Heaven
In this paper we study the possible role of managed futures in portfolios of stocks, bonds and hedge funds. We find that allocating to managed futures allow investors to achieve a very substantial degree of overall risk reduction at limited costs. Apart from their lower expected return, managed futures appear to be more effective diversifiers than hedge funds. Adding managed futures to a portfolio of stocks and bonds will reduce that portfolio’s standard deviation more and quicker than hedge funds will, and without the undesirable side-effects on skewness and kurtosis. Overall portfolio standard deviation can be reduced further by combining both hedge funds and managed futures with stocks and bonds. As long as at least 45-50% of the alternatives allocation is allocated to managed futures, this again will not have any negative side-effects on skewness and kurtosis.
A Quantitative Analysis of CTA Funds
Our research studies various properties of commodity trading advisors (CTAs) from a quantitative point of view. Our investigation is based on a commercial database of 549 funds and focuses on the period 1990 to present.
Firstly, CTAs’ return distributions are analyzed and strong evidence of non-normality is found, stressing the need for portfolio allocation techniques which take into account higher-order moments.
Secondly, relative persistence in return distribution parameters is studied. We find strong persistence for volatility, but fail to find significant persistence for average return or higher order moments.
Thirdly, we review the major benchmarks available to the industry and build new benchmarks from our dataset. This allows us to infer the magnitude of various biases. We study homogeneity of 2 CTA subsets, namely trend-followers and non-trend-followers, and study the diversification possibilities in a CTA portfolio.
In the second part of the study, we focus on linking CTAs returns with that of traditional assets. After showing that a buy and hold multi-factor linear model fails to explain CTAs returns, we point out the presence of option-like payoffs in CTAs return patterns. Trend-following CTAs exhibit straddle-like payoffs, while non-trend-followers’ return patterns that are reminiscient of a long call option.
Lastly, using simple trading algorithms based on moving averages, we propose a linear market model in which factors capture the dynamic nature of CTA managers’ strategies. Our model leads to significant improvements over the classical model. Notably, we show that our model is able to closely replicate a broad index of CTAs for long out-of-sample periods.
Have a great weekend!
Picture credits: rugosa rosa@flickr