Moskowitz, Ooi, and Pedersen (2012) show that time series momentum delivers a large and significant alpha for a diversified portfolio of international futures contracts. We find that their results are largely driven by volatility-scaling returns (or the so-called risk parity approach to asset allocation) rather than by time series momentum. Without scaling by volatility, time series momentum and a buy-and-hold strategy offer similar cumulative returns, and their alphas are not significantly different. This similarity holds for most sectors and for a combined portfolio of futures contracts. Cross-sectional momentum also offers a higher (similar) alpha than unscaled (scaled) time series momentum.
While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this paper, we introduce Deep Momentum Networks -- a hybrid approach which injects deep learning based trading rules into the volatility scaling framework of time series momentum. The model also simultaneously learns both trend estimation and position sizing in a data-driven manner, with networks directly trained by optimising the Sharpe ratio of the signal. Backtesting on a portfolio of 88 continuous futures contracts, we demonstrate that the Sharpe-optimised LSTM improved traditional methods by more than two times in the absence of transactions costs, and continue outperforming when considering transaction costs up to 2-3 basis points. To account for more illiquid assets, we also propose a turnover regularisation term which trains the network to factor in costs at run-time.
Although portfolio management didn't change much during the 40 years after the seminal works of Markowitz and Sharpe, the development of risk budgeting techniques marked an important milestone in the deepening of the relationship between risk and asset management. Risk parity then became a popular financial model of investment after the global fina
Time series momentum returns are driven by volatility states. Controlling for standard risk factors, assets in a low volatility state produce positive and significant time series alphas, whereas high volatility state assets do not. Contrary to what is known about cross-sectional momentum, and to what behavioral models imply, time series momentum is most profitable in futures characterized by declining or low risk levels.
This book is a compilation of recent articles written by leading academics and practitioners in the area of risk-based and factor investing (RBFI). The articles are intended to introduce readers to some of the latest, cutting edge research encountered by academics and professionals dealing with RBFI solutions. Together the authors detail both alternative non-return based portfolio construction techniques and investing style risk premia strategies. Each chapter deals with new methods of building strategic and tactical risk-based portfolios, constructing and combining systematic factor strategies and assessing the related rules-based investment performances. This book can assist portfolio managers, asset owners, consultants, academics and students who wish to further their understanding of the science and art of risk-based and factor investing. Contains up-to-date research from the areas of RBFI Features contributions from leading academics and practitioners in this field Features discussions of new methods of building strategic and tactical risk-based portfolios for practitioners, academics and students
Your Essential Guide to Quantitative Hedge Fund Investing provides a conceptual framework for understanding effective hedge fund investment strategies. The book offers a mathematically rigorous exploration of different topics, framed in an easy to digest set of examples and analogies, including stories from some legendary hedge fund investors. Readers will be guided from the historical to the cutting edge, while building a framework of understanding that encompasses it all. Features Filled with novel examples and analogies from within and beyond the world of finance Suitable for practitioners and graduate-level students with a passion for understanding the complexities that lie behind the raw mechanics of quantitative hedge fund investment A unique insight from an author with experience of both the practical and academic spheres.
We document significant “time series momentum” in equity index, currency, commodity, and bond futures for each of the 58 liquid instruments we consider. We find persistence in returns for 1 to 12 months that partially reverses over longer horizons, consistent with sentiment theories of initial under-reaction and delayed over-reaction. A diversified portfolio of time series momentum strategies across all asset classes delivers substantial abnormal returns with little exposure to standard asset pricing factors and performs best during extreme markets. Examining the trading activities of speculators and hedgers, we find that speculators profit from time series momentum at the expense of hedgers.
This thesis investigates volatility clustering, scaling and dynamics in financial series of asset returns and studies the underlying mechanism. We propose a direct measure of volatility clustering based on the conditional probability distribution (CPD) of the returns given the return in the previous time interval. We found that the CPDs of returns in real financial time series exhibits universal scaling, characterized by a collapse of the CPDs (of different time lags and of different returns in the previous interval) into to a universal curve exhibiting a power-law tail with an exponent of amp;−4. We construct a simple phenomenological model to explain the emergence of VC and the associated volatility scaling. We also study agent-based models of financial markets, and explore the impact of dynamical risk aversion (DRA) of heterogeneous agents on the price fluctuations. We found that the DRA is the primary driving force responsible for excess price fluctuations and the associated volatility clustering. Both our models (phenomenological model and agent-based model) are able to generate time series that reproduces stylized facts of the market data on different time scales. We have also studied general herding behavior often exhibited in financial markets in the context of an evolutionary Minority Game. We discovered a general mechanism for the transition from segregation into opposing groups to clustering towards cautious behavior.
The individual investor's comprehensive guide to momentum investing Quantitative Momentum brings momentum investing out of Wall Street and into the hands of individual investors. In his last book, Quantitative Value, author Wes Gray brought systematic value strategy from the hedge funds to the masses; in this book, he does the same for momentum investing, the system that has been shown to beat the market and regularly enriches the coffers of Wall Street's most sophisticated investors. First, you'll learn what momentum investing is not: it's not 'growth' investing, nor is it an esoteric academic concept. You may have seen it used for asset allocation, but this book details the ways in which momentum stands on its own as a stock selection strategy, and gives you the expert insight you need to make it work for you. You'll dig into its behavioral psychology roots, and discover the key tactics that are bringing both institutional and individual investors flocking into the momentum fold. Systematic investment strategies always seem to look good on paper, but many fall down in practice. Momentum investing is one of the few systematic strategies with legs, withstanding the test of time and the rigor of academic investigation. This book provides invaluable guidance on constructing your own momentum strategy from the ground up. Learn what momentum is and is not Discover how momentum can beat the market Take momentum beyond asset allocation into stock selection Access the tools that ease DIY implementation The large Wall Street hedge funds tend to portray themselves as the sophisticated elite, but momentum investing allows you to 'borrow' one of their top strategies to enrich your own portfolio. Quantitative Momentum is the individual investor's guide to boosting market success with a robust momentum strategy.
Motivated by studies of the impact of frictions on asset prices, we examine the effect of key components of time-series momentum strategies on turnover and performance. We show that more efficient volatility estimation and price trend detection can significantly reduce portfolio turnover by more than one third, without causing statistically significant performance degradation. We propose a novel implementation of the strategy that incorporates the pairwise signed correlations by means of a dynamic leverage mechanism. The correlation-adjusted variant outperforms the naive implementation of the strategy and the outperformance is more pronounced in the post-2008 period. Finally, using a transaction costs model for futures-based strategies that separates costs into roll-over and rebalancing costs, we show that our findings remain robust to the inclusion of transaction costs.
