Essays on Momentum Strategies in Finance

This section briefly summarizes in which way we have investigated momentum in this thesis. In Chapter 2 we alter the momentum strategy to improve its performance, while in Chapter 3 we leave the strategy as is, but aim at improving its performance by hedging. In Chapter 4 we develop a Bayesian latent factor model and apply this model to momentum. In Chapter 2 we apply mean-variance optimization to the equity momentum strategy and compare its performance to other alterations of the momentum strategy. Next to comparing these strategies we test if combining these alterations, including our meanvariance optimization, is able to further improve momentum’s performance. We evaluate whether the optimized and other altered momentum strategies as well as their combinations reduce momentum’s crash risk and its time-varying risks and returns. In Chapter 3 we hedge equity momentum’s time-varying exposures to the three equity risk factors. We determine the hedge coefficients in two different ways. First, we use the estimated factor loadings of each stock in the momentum strategy at that time to determine momentum’s exposures. Secondly, we use a conditional factor model to estimate momentum’s exposures using momentum’s recent returns. We test whether these hedging strategies reduce momentum’s time-varying risks and returns as well as its crash risk. Finally, we investigate whether the bias in the estimated factor loadings used for hedging varies over time. In Chapter 4 we use a Bayesian latent factor model to investigate the time variation in the comovements of stocks. In particular, we investigate how the optimal number of latent factors varies over time. We determine this optimal number of factors by comparing the predictive likelihoods for models with different numbers of latent factors. Subsequently, we apply the model in a residual industry momentum strategy.This thesis discusses several aspects and possible improvements of equity momentum strategies in finance. Equity momentum is the phenomenon that stocks that have recently outperformed continue to outperform, while underperformers will continue to underperform. Equity strategies that exploit this phenomenon by buying the recent outperformers and short-selling the recent underperformers have proven to be profitable for investors. In his Nobel prize lecture in 2013 Eugene Fama referred to this performance of the momentum strategy as being the biggest challenge for the efficient market hypothesis. Nevertheless, equity momentum is also known for its crash risk, wiping out years of average positive returns in just a few months, and the fact that its risk and returns vary over time. In this thesis different hedging strategies are applied to reduce momentum’s crash risk and time varying exposures without reducing its positive average returns. Furthermore, different recent improvements of momentum are combined in a mean-variance optimization set-up. Optimization also reduces momentum’s crash risk and its time varying exposures. Moreover it improves momentum’s Sharpe ratio for moderate transaction costs. Finally, this thesis addresses momentum’s time varying risks and returns in a different way. A Bayesian latent factor model where the number of latent factors is allowed to vary over time is derived. Using the predictive likelihood approach this model is then applied to a residual industry momentum strategy. In turbulent times, like the crisis that started in 2008, the Bayesian latent factor model performs well in terms of risk and return characteristics

Robust Optimization of the Equity Momentum Strategy

Quadratic optimization for asset portfolios often leads to error maximization, with optimizers zooming in on large errors in the predicted inputs, that is, expected returns and risks. The consequence in most cases is a poor real-time performance. In this paper we show how to improve real-time performance of the popular equity momentum strategy with robust optimization in an empirical application involving 1500-2500 US stocks over the period 1963-2006. We also show that popular procedures like Bayes-Stein estimated expected returns, shrinking the covariance matrix and adding weight constraints fail in such a practical cas