Hirschey, Nicholas H.Eusébio, Hugo2023-12-132023-12-132022-12-162022-12-16http://hdl.handle.net/10362/161183This paper 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.engSystematic trading strategyMomentumValueVolatility forecastingMachine learningNeural networksQuantitative trading strategymaster thesis203311620