Essays on Momentum Strategies in Finance

textabstractThis 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