Value and momentum recently: analysis of quantitative investment strategy

• Autor(a) principal: Scherzler, Fynn
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10362/154325
• topic: Systematic trading strategy; Momentum; Value; Volatility; United States; Python; Quantitative trading strategy; Domínio/Área Científica::Ciências Sociais::Economia e Gestão

Both momentum and value strategies earn consistent and significant premia and are negatively correlated, with their equal weight combination improving the risk-return trade-off. This paper shows that allocation based on market volatility further improves the risk-return trade-off, particularly by limiting the large drawdowns momentum experiences in market crashes, where value tends to perform better. Both long-short strategy legs achieve comparably low Sharpe ratios in the past 20 years. There is no clear picture of high momentum stocks performing better than their low momentum counterparts, similar for value, which seems to off-set the long-short returns, while the long legs perform comparably well. The group report tests the combination of five different sub strategies, resembling the performance of a multi-strategy hedge fund benchmarked against the popular buy-and-hold S&P 500 investing approach. The sub-strategies are: residual momentum, value including intangibles, value and momentum, volatility forecasting, and a long short-term memory strategy, the latter two being machine-learning-based, and all investing in the U.S. universe. The combined strategy’s performance is analyzed by three weighting schemes: equal-weight, momentum, and mean variance, resulting in a gamut of robustness and performance. The combined strategies reap diversification benefits, thereby giving investors a superior risk-reward trade-off compared to the buy-and hold S&P 500 approach