Commodity Trading Advisors Research Articles

The crisis alpha of managed futures: Myth or reality?

We examine whether commodity trading advisors (CTAs), or managed futures, provide investors with diversification benefits in times of crises, along with looking into the source of such a “crisis alpha”. We developed a systematic crisis identification methodology that takes into account both, the magnitude and the speed of price deterioration and apply it to a unique dataset, that includes realized returns and sector positions of CTAs on a daily basis. We find CTAs do acquire positive gains in most sectors during crises, which originate from two sources: Firstly, their diversification across multiple futures markets, i.e. positive yields in gaining markets can counterbalance low performance in the crisis market. Secondly, the fast reduction in CTAs' exposure to crisis markets (in less than 15 days for composite indices) allows them to stabilize their performance. Both factors together, quickly cutting losses in crisis sectors while staying profitable in the other ones, allow CTAs to generate crisis alpha.

Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection

Momentum strategies are an important part of alternative investments and are at the heart of the work of commodity trading advisors. These strategies have, however, been found to have difficulties adjusting to rapid changes in market conditions, such as during the 2020 market crash. In particular, immediately after momentum turning points, when a trend reverses from an uptrend (downtrend) to a downtrend (uptrend), time-series momentum strategies are prone to making bad bets. To improve the responsiveness to regime change, the authors introduce a novel approach, in which they insert an online changepoint detection (CPD) module into a deep momentum network pipeline, which uses a long short-term memory deep-learning architecture to simultaneously learn both trend estimation and position sizing. Furthermore, their model is able to optimize the way in which it balances (1) a slow momentum strategy that exploits persisting trends but does not overreact to localized price moves and (2) a fast mean-reversion strategy regime by quickly flipping its position and then swapping back again to exploit localized price moves. The CPD module outputs a changepoint location and severity score, allowing the model to learn to respond to varying degrees of disequilibrium, or smaller and more localized changepoints, in a data-driven manner. The authors back test their model over the period 1995–2020, and the addition of the CPD module leads to a 33% improvement in the Sharpe ratio. The module is especially beneficial in periods of significant nonstationarity; in particular, over the most recent years tested (2015–2020), the performance boost is approximately 66%. This is especially interesting because traditional momentum strategies underperformed in this period.

When it pays to follow the crowd: Strategy conformity and CTA performance

AbstractPrior research in hedge fund and mutual fund management finds a positive relation between portfolio distinctiveness and subsequent performance, suggesting that strategy differentiation is associated with superior skill. We find that commodity trading advisors (CTAs) with returns that correlate more strongly with those of peers feature higher performance and are more highly exposed to a time series momentum factor. Strategy conformity appears to be a signal of managerial skill in CTAs, in contrast to hedge funds and mutual funds. These results indicate that a common trend following strategy drives CTA returns and that CTAs offer investors an opportunity to invest in momentum.

Same same but different – Stylized facts of CTA sub strategies

Using a unique dataset of daily returns of 89 programmes of Commodity Trading Advisors (CTAs), we investigate the distributional properties of CTA strategies including trend following, fundamental and contrarian strategies. We find that daily data exhibits strong features of fat-tail, volatility clustering, and long memory in volatility. This is different from previous studies which are often based on monthly data. Our study contributes to the literature of stylized facts of financial markets, it also provides insights to practitioners because the information from monthly data might be misleading.

A Modified Hierarchical Risk Parity Framework for Portfolio Management

This article introduces a modified hierarchical risk parity (MHRP) approach that extends the HRP approach by incorporating three intuitive elements commonly used by practitioners. The new approach (1) replaces the sample covariance matrix with an exponentially weighted covariance matrix with Ledoit–Wolf shrinkage; (2) improves diversification across portfolio constituents both within and across clusters by relying on an equal volatility, rather than an inverse variance, allocation approach; and (3) improves diversification across time by applying volatility targeting to portfolios. The author examines the impact of the enhancements on portfolios of commodity trading advisors within a large-scale Monte Carlo simulation framework that accounts for the realistic constraints of institutional investors. The author finds a striking improvement in the out-of-sample Sharpe ratio of 50%, on average, along with a reduction in downside risk. TOPICS:Statistical methods, simulations, big data/machine learning Key Findings • The author introduces a modified hierarchical risk parity (MHRP) approach, which incorporates three popular portfolio management techniques into the hierarchical risk parity framework of Lopez de Prado. • He reports a striking improvement in the out-of-sample Sharpe ratio of 50%, on average, for portfolios of commodity trading advisors. • The author argues that the MHRP framework has broad applications for portfolios of traditional and alternative investments.

Same Same But Different -- Stylized Facts of CTA Sub Strategies

Using a unique dataset of daily returns of 89 programmes of Commodity Trading Advisors (CTA), we investigate the distributional properties of CTA strategies including trend following, fundamental and contrarian strategies. We find that daily data exhibits strong features of fat-tail, volatility clustering, and long memory in volatility. This is different from previous studies which are often based on monthly data. Our study contributes to the literature of stylized facts of financial markets, it also provides insights to practitioners because the information from monthly data might be misleading.

Portfolio management of Commodity Trading Advisors with volatility-targeting

I show analytically that a volatility-targeted allocation methodology improves the risk-adjusted performance of portfolios under a broad set of assumptions regarding the serial correlation of returns, the variability of volatility and dependence of the expected Sharpe ratio on the level of volatility. I examine the impact of volatility targeting on portfolios of Commodity Trading Advisors within the large-scale simulation framework of Molyboga and L'Ahelec (2016) that accounts for the realistic constraints on institutional investors. I find a consistent and statistically significant improvement in the out-of-sample returns that ranges between 0.53% and 0.80% per annum, on average. The performance enhancement is robust to portfolio size and manager selection, and is implementable inside managed account investments.

Portfolio Management of Commodity Trading Advisors with Volatility Targeting

I show analytically that a volatility-targeted allocation methodology improves the risk-adjusted performance of portfolios under a broad set of assumptions regarding the serial correlation of returns, the variability of volatility and dependence of the expected Sharpe ratio on the level of volatility. I examine the impact of volatility targeting on portfolios of Commodity Trading Advisors within the large-scale simulation framework of Molyboga and L'Ahelec (2016) that accounts for the realistic constraints on institutional investors. I find a consistent and statistically significant improvement in the out-of-sample returns that ranges between 0.53% and 0.80% per annum, on average. The performance enhancement is robust to portfolio size and manager selection, and is implementable inside managed account investments.

The Impact of Performance Fees on Multi-Manager CTA Portfolios

The authors study the impact of fees on the performance of multi-manager portfolios within managed futures. Using net-of-fee monthly returns of commodity trading advisors (CTAs) and their fee structures as reported in the BarclayHedge database, they estimate the time series of gross returns. They find that fees represent approximately 50% of gross performance, on average. They consider three fee structures: management fee only; a standard structure that includes both management and incentive fees; and a pooled, or netted, fee structure in which the incentive fee is based on aggregate portfolio performance rather than the performance of individual managers. They also vary the number of managers in a portfolio from 1 to 20 and consider crystallization frequencies between 3 and 12 months. Regardless of the fee structure, they find that average performance increases monotonically with the number of managers in a portfolio, and the distribution of performance becomes tighter. Performance improvement relative to a single-manager investment is highest for multi-manager portfolios that rely on a pooled fee structure. Pooling fees results in fee savings of up to 40% relative to the standard fee management and performance fee structures. They also find that less frequent crystallization consistently improves performance. <b>TOPICS:</b>Commodities, futures and forward contracts, manager selection, performance measurement

Who Holds Positions in Agricultural Futures Markets

We use non-public data regarding all trader-level futures positions, reported to the U.S. grain and oilseed derivatives market regulator (the CFTC), in order to describe the nature of market participants, the maturity structure of their holdings, and the aggregate position patterns for nine different categories of traders that we separate based on their main lines of business. We provide novel evidence about the overall extent of calendar spreading and about the contribution of commercial traders to total spreading activity. Our sample’s 3,854 traders account for 86 to 93 percent of the total futures open interest at the end of an average day in 2015–2018. Well over 90 percent of their positions have maturities of less than a year. Among our nine trader categories, just three (hedge funds and commercial dealers/merchants, plus commodity index traders on the long side) account for about four fifths of all reported trader positions. In fact, fewer than 200 “permanent” large traders (overwhelmingly from these three categories) make up the bulk of the daily open interest in the four largest agricultural futures markets. In the aggregate, the positions of commercial dealers and hedge funds (including commodity pool operators, commodity trading advisors, managed money traders, and associated persons) are highly negatively correlated. This correlation is strikingly strong for short positions: as a result, the sum total of commercial dealers’ and hedge funds’ respective shares of the short open interest fluctuates relatively little over time. We show, for the first time, that calendar spreads account for more than a third of all large trader positions; that much of the intra-year variation in the total futures open interest can be tied to changes in the extent of calendar spreading; that about half of all spread positions involve contracts expiring in 4 to 12 months (either spreading with shorter-dated contracts, or involving only maturities of 4 to 12 months); and that commercial traders who are not swap dealers (dealers and merchants, mostly) make up from a quarter to two fifths of all calendar spread positions. Again, commercial dealers’ and hedge funds’ shares of the spread open interest are negatively correlated. None of these patterns can be inferred from public data, as the CFTC’s Commitments of Traders Reports (COT) do not break out spreads for “traditional” commercial traders in general and commercial dealers and merchants in particular.

Practical Applications of The Long and Short of Trend Followers

Practical Applications Summary In The Long and Short of Trend Followers, published in the Fall 2019 issue of The Journal of Alternative Investments, Joakim Agerback of Finserve Nordic, Tor Gudmundsen-Sinclair of Global Defense and Security Fund, and Jarkko Peltmaki of Stockholm Business School develop a new set of risk factors to better understand the performance of trend following commodity trading advisors (CTAs). Trend following CTAs use futures to follow both upward trends with long positions and downward trends using short positions. They do not face significant short-selling constraints that are present in many other investment vehicles. The relatively constraint-free ability to follow both upward and downward trends implies that skilled CTAs should be able to make money in both bull and bear markets. Theoretically, investment in CTAs can therefore provide desirable diversification to portfolios and enhance returns. Until now, an understanding of the exact nature of long-short CTA strategy performance has been thwarted by the inability of performance attribution studies to disentangle the effects of the long and short sides. The results of this article reveal new insights into the determinants of CTA performance. TOPICS:Commodities, futures and forward contracts, performance measurement

Practical Applications of The Impact of Performance Fees on Multi-Manager CTA Portfolios

<h3>Practical Applications Summary</h3> In <b>The Impact of Performance Fees on Multi-Manager CTA Portfolios</b>, published in the Fall 2019 issue of <b><i>The Journal of Alternative Investments</i></b>, <b>Kathryn Kaminski</b> of <b>AlphaSimplex Group</b> and <b>Marat Molyboga</b> of <b>Efficient Capital Management</b> quantify the relative performance of traditional and pooled fee structures for multi-manager commodity trading advisor (CTA) portfolios. Absent their simulation approach to estimating performance differences, the relative impact of these fee structures is not plainly evident. Databases of CTA returns not only suffer from several biases, but also report returns net of fees. This net fee return may not be applicable to a host of investors that can negotiate different fees and fee structures. Manager fees represent a very large portion of gross CTA portfolio returns. The authors examine relative net portfolio returns and Sharpe ratios for different fee structures, levels of management fees and incentive fees, and across different crystallization frequencies and different numbers of managers within CTA portoflios. The results are important to investors who wish to make informed, net return-maximizing decisions regarding fee structures and fee-related characteristics for multi-manager CTA portfolio investments. <b>TOPICS:</b>Commodities, futures and forward contracts, performance measurement

Practical Applications of Testing Futures Trading Strategy Assumptions

<h3>Practical Applications Summary</h3> In <b>Testing Futures Trading Strategy Assumptions</b>, from the the Fall 2019 issue of <b><i>The Journal of Alternative Investments</i></b>, <b>Mark C. Hutchinson</b> and <b>John O’Brien</b> (both of the <b>Cork University Business School, University College, Cork</b>) investigate the robustness of common assumptions made in studies of commodity trading advisor (CTA) performance. Two factors motivate their inquiry: The first is an increasing interest in performance studies of these futures-based trading strategies. The second is that existing skepticism in reported high risk-adjusted CTA returns is attributed to faulty assumptions underlying prior studies, and that such assumptions produced overstated performance. The authors examined (1) the appropriateness of using synthetic futures as a proxy for CTA strategy returns, and (2) the reasonableness of typical assumptions about transaction costs, management fees, and incentive fees. They found that strategy returns using synthetic futures were highly correlated with those based on the corresponding exchange-traded instruments. Transaction costs vary across asset classes. Both transaction costs and management fees have declined over time. In general, the authors found that assumptions that account for these differences in transaction costs over time were realistic approximations of the true costs. <b>TOPICS:</b>Futures and forward contracts, commodities, performance measurement

Testing Futures Trading Strategy Assumptions

There is a growing literature examining futures-based trading strategies and the performance of Commodity Trading Advisors (CTAs). In this article, the authors test the validity of three key assumptions used in these studies. They test the validity of basing conclusions on analysis of synthetic rather than market price data; they review the evidence on the level of transaction costs, to test the cost model used in modeling futures-based trading strategy; and finally, they test the assumption that CTAs generally charge a management fee of 2% and incentive (performance) fee of 20%. In addition, they present the trend over time in the structure of fees. Their findings suggest that inferences based on synthetic futures replicate those based on exchange-traded data. Over the full period, the average fee levels were 1.82% (management) and 20.2% (incentive)—not significantly different from the levels used in the literature. TOPICS:Futures and forward contracts, real assets/alternative investments/private equity, commodities

Effect of Speculators’ Position Changes on the LME Futures Market

This paper employs Granger causality tests to analyze the role of speculators using weekly COTR (commitment of traders reports) data covering the period of August 2014 to July 2017. The paper presents statistically significant evidence that the position changes of speculators, such as hedge funds and CTAs (commodity trading advisors), unidirectionally Granger-cause the prices of base metals, such as aluminum, copper, and zinc. This finding is a result of causality going from the levels of net futures positions of money managers to futures price changes on the London Metal Exchange (LME). However, producers’ and swap dealers’ speculative roles in price-formation are rejected in Granger causality tests. This paper presents clear results with important market implications.

Trends’ Signal Strength and the Performance of CTAs

We propose a new asset-based factor that relies on aggregating momentum signals over different horizons. Aggregating signals this way captures assets’ trend signal strength, thereby addressing a limitation in existing time series momentum strategies. Our factor mimics a trend-following manager that increases exposure to markets where trends develop and decreases exposure to markets where trends fade. Taking into account a number of practical implementation issues, we found that our proposed factor performs better at replicating the stylized facts of Commodity Trading Advisors’ returns than previous methods and allows a more meaningful assessment of fund alpha.

Diversification and Financialization in Commodity Markets: Evidence from Commodity Trading Advisors

This paper provides new and integrated evidence on the causes and consequences of commodity financialization from the perspective of diversification. First, based on a large sample of commodity trading advisors, we find that financial investment in commodity markets is related to the diversification benefits against stock market downturns. Second, the aggregate financial investment positively predicts future commodity-stock return correlation. Finally, using terrorist attacks as shocks to diversification need, we present causal evidence that diversification-motivated investment in commodity markets increases the commodity-stock market comovement. Overall, our findings reveal the economic link between diversification benefits, commodity financialization, and the commodity-stock market comovement.

Testing Futures Trading Strategies: How Robust are Standard Assumptions?

There is a growing literature examining futures based trading strategies and the performance of Commodity Trading Advisors (CTAs). In this paper, we test the validity of three key assumptions used in these studies. The validity of basing conclusions on analysis of synthetic rather than market price data is tested. We review the evidence on the level of transaction costs to test the cost model used in modelling futures based trading strategy. Finally, we test the assumption that CTAs generally charge a management fee of 2% and incentive (performance) fee of 20%. In addition, the trend over time in the structure of fees is presented. Our findings suggest that inferences based upon synthetic futures replicate those based on exchange-traded data. Over the full period, the average fee levels were measured at 1.82% (management) and 20.2% (incentive) close to and not significantly different from the levels used in the literature.

CTAs: Superior Performance or Diversification Only?

Commodity Trading Advisors (CTAs) are associated with preferable risk–return characteristics, especially during market dislocations. However, it is still difficult to identify the underlying return sources of the strategy due both to lack of data and to potentially biased available data. After setting up a cleaned universe of CTAs building on data from 1971 to 2016 and accounting for specific biases, the authors empirically analyze their return distributions. In line with existing research, their results show the non-normal distribution of CTA return profiles, in particular statistically significant leptokurtic distributions. The common claim that CTAs exhibit positive skewness, however, cannot be confirmed. Further, they empirically study the two most commonly claimed risk–return properties typically associated with CTAs: improved risk-adjusted returns of traditional portfolios through allocation to CTAs and above-average risk-adjusted returns of CTAs on a stand-alone basis. They confirm the first claim but do not find sufficient evidence supporting the second claim.

Practical Applications of The Moral Hazard Problem in Hedge Funds: A Study of Commodity Trading Advisors

<h3>Practical Applications Summary</h3> Research shows that poorly performing hedge funds tend to engage in risk-shifting behavior—increasing volatility—to boost returns and, in turn, performance fees, especially when fee structures contain triggers such as high-water marks. In <b>The Moral Hazard Problem in Hedge Funds: <i>A Study of Commodity Trading Advisors</i></b>, <b>Li Cai</b>, <b>Chris (Cheng) Jiang</b>, and <b>Marat Molyboga</b> demonstrate that the market environment affects risk-shifting behavior among hedge funds—less risk-shifting behavior occurs in unfavorable market environments, and vice versa. The study also provides new evidence linking investment strategy and risk choices. The authors find that although risk-shifting behavior can increase fees for managers, it lowers investors9 risk-adjusted returns.