| dc.description.abstract | Momentum strategies are receiving increasing attention in the field of finance due to their potential to generate superior returns. Momentum investing is based on the premise that assets that have exhibited strong performance in the recent past are likely to continue to perform well in the near future. This thesis explores the different momentum strategies in diverse markets and asset classes, such as the US equity market utilizing industry portfolios, multi-asset funds, and developed countries. Our study involves a comprehensive analysis of various momentum strategies, including relative momentum and absolute momentum, as well as more recently extended strategies such as dual and triple momentum. In this study, we investigated the optimal parameters for various momentum trading strategies. Our findings suggest that there is no single set of optimal parameters for past price performance and the number of chosen best/worst performing assets that is universally applicable across all datasets and strategies. The study examines momentum strategies using three distinct datasets. The long-only dual momentum strategy has been identified as the optimal approach for the dataset of US industry portfolios, exhibiting a statistically significant Sharpe ratio. The multi-asset fund dataset reveals that the optimal strategy is the triple momentum strategy, which presents the highest statistically significant Sharpe ratio. Similarly, the dataset of developed countries presents the triple momentum strategy as the optimal strategy with the highest Sharpe ratio and the highest statistically significant alpha values from both the Capital Asset Pricing Model and the Fama-French-Carhart Four-Factor Model. Overall, the study highlights the importance of considering various performance and risk measures to determine the optimal momentum strategy. | |
