Managed Futures Funds Research Articles

Tracking speculative trading

Managed futures funds are predominantly trend-followers. By analyzing positioning data, we provide novel evidence for this claim and estimate signals applied by these funds. We write trend-followers aggregate position as a weighted sum of past daily returns and use a generalized ridge regression for regularization and parameter estimation. This procedure prevents overfitting but remains flexible enough to capture various patterns. For the 23 commodities considered, trend-following can explain speculators’ position changes with an average R2 of more than 40%. Finally, we document that producers act as contrarians in a way that closely mirrors the behavior of momentum traders.

Which Trend Is Your Friend?

Managed futures funds and commodity trading advisers (CTAs) use heuristics or statistical measures often called “filters” to trade on price trends. Two key statistical measures of trends are “time-series momentum” and “moving-average crossovers.” We show, empirically and theoretically, that these trend indicators are closely related. In fact, they are equivalent representations in their most general forms. They also capture many other types of filters, such as the Hodrick–Prescott (HP) filter, the Kalman filter, and all other linear filters. We show how these filters can be represented through “trend signature plots,” demonstrating their dependence on past prices and returns by horizon.

Adaptive Time Series Momentum: An Objective Benchmark for Systematic Trend-Following Strategies

We propose a market-based benchmark for Managed Futures funds that relies on simple time series momentum strategies. Our benchmark mimics managers’ allocation process by increasing exposure when trends are developing and decreasing exposure when trends are fading by allocating to short and long-term trend-following strategies simultaneously. In addition, we take into account transaction costs, allowing a meaningful assessment of fund alpha. Our approach offers investors an alternative to widely used manager-based CTA benchmarks. Our model outperforms the explanatory power of existing market-based factor models applied to Managed Futures funds.

Which Trend Is Your Friend?

Managed-futures funds and CTAs trade predominantly on trends. There are several ways of identifying trends, either using heuristics or statistical measures often called “filters.” Two important statistical measures of price trends are time series momentum and moving average crossovers. We show both empirically and theoretically that these trend indicators are closely connected. In fact, they are equivalent representations in their most general forms, and they also capture many other types of filters such as the HP filter, the Kalman filter, and all other linear filters. Further, we show how these filters can be represented through “trend signature plots” showing their dependence on past prices and returns by horizon. Our results unify and broaden a range of trend-following strategies, and we discuss the implications for investors.

Hedge Fund Performance in Bull and Bear Markets: Alpha Creation and Risk Exposure

This article investigates whether hedge funds create abnormal risk-adjusted returns, during both bull and bear markets. The model applied is an extended multi-factor model, using a dataset consisting of hedge fund return series with data from a 15-year period ranging from 1994 to 2009. The whole set, as well as bull and bear subperiods, has been analyzed. The authors apply a slightly different model from that used in previous studies and use a comprehensive database that includes the recent financial crisis. A limited amount of the hedge fund literature distinguishes between performance during bull markets and bear markets. The authors find that most hedge fund strategies reduce their exposure to the equity markets during adverse market conditions and invest more in commodities. The inclusion of global macro and managed futures funds in an investor’s portfolio offers a good hedge for bear market conditions.

The effect of downside risk reduction on UK equity portfolios included with Managed Futures Funds

The concept of asymmetric risk estimation has become more widely applied in risk management in recent years with the increased use of Value-at-risk (VaR) methodologies. This paper uses the n-degree lower partial moment (LPM) models, of which VaR is a special case, to empirically analyse the effect of downside risk reduction on UK portfolio diversification and returns. Data on Managed Futures Funds are used to replicate the increasingly popular preference of investors for including hedge funds and fund-of-funds type investments in the UK equity portfolios. The result indicates, however that the potential benefits of fund diversification may deteriorate following reductions in downside risk tolerance levels. These results appear to reinforce the importance of risk (tolerance) perception, particularly downside risk, when making decisions to include Managed Futures Funds in UK equity portfolios as the empirical analysis suggests that this could negatively affect portfolio returns.

Price Shocks and the Performance of Managed Futures Funds

Can specialized trading firms capture the potentially substantial returns associated with large sudden price moves? This study attempts to answer that question by examining the monthly performance of managed futures funds during months in which large daily price shocks in the foreign exchange market occur. Specifically, it attempts to assess whether Commodity Trading Advisors (CTAs) that specialize in speculating on changes in foreign exchange rates are able to capture the price change precipitated by the shock in the month of the price shock <b>TOPICS:</b>Global markets, performance measurement, statistical methods

Incentives for asset growth: The different causes of monthly in-flows and out-flows of surviving managed futures funds

The typical hedge fund incentive contract stipulates that 20 per cent of the profits are kept for the manager as an incentive to perform well: that is, provide large, persistent positive returns to investors. Rather than focusing on maximising their investor's wealth, managers may subvert this incentive scheme and focus on maximising assets under management instead. By doing so, managers may generate profits for themselves, even if their returns to their investors are small and do not persist. The aim of this work is to understand how a surviving manager may maximise monthly asset in-flow and minimise out-flow, and hence, maximise overall asset growth. Surprisingly, we find that non-return-based performance characteristics like fund age, relative performance, and fund size are key factors when forecasting monthly asset flow. Furthermore, we find that different causes of in- and out-flows exist.

Why have the returns to technical analysis decreased?

Returns to managed futures funds and Commodity Trading Advisors (CTAs) have decreased dramatically. Funds overwhelmingly use technical analysis. This research determines if structural change in futures price movements could explain the reduced fund returns. Bootstrap tests are used to test significance of a change in statistics related to daily returns, close-to-open changes, breakaway gaps, and serial correlation. Several statistics have changed across a broad range of commodities. Lower price volatility is the most likely explanation of the lower returns from technical analysis. The structural changes likely caused the decreased returns rather than increased technical trading causing the structural changes.

Market Vision and Investment Styles

The development of a simple taxonomy for trading style based on a worldview can serve as an important differentiation across money managers and provide insight into how many managers expect to generate returns. For instance, a taxonomy for active management can divide trading behavior into two broad types: convergent and divergent. The convergent–divergent dichotomy effectively encompasses the behavior of a broad universe of money managers and can be applied across a wide variety of asset types. This trading dichotomy can describe the philosophical differences between hedge funds and managed futures funds as well as similarities across asset types.

Managed futures, positive feedback trading, and futures price volatility

A major issue in recent years is the role that large, managed futures funds and pools play in futures markets. Many market participants argue that managed futures trading increases price volatility due to the size of managed futures trading and reliance on positive feedback trading systems. The purpose of this study is to provide new evidence on the impact of managed futures trading on futures price volatility. A unique data set on managed futures trading is analyzed for the period 1 December 1988 through 31 March 1989. The data set includes the daily trading volume of large commodity pools for 36 different futures markets. Regression results are unequivocal with respect to the impact of commodity pool trading on futures price volatility. For the 72 estimated regressions (two for each market), the coefficient on commodity pool trading volume is significantly different from zero in only four cases. These results constitute strong evidence that, at least for this sample period, commodity pool trading is not associated with increases in futures price volatility. © 1999 John Wiley & Sons, Inc. Jrl Fut Mark 19: 759–776, 1999