Factor-Scoring Ranking Strategy in S&P 500: A Quantitative Approach to Stock Selection and Performance Optimization

This paper investigates the factor-scoring ranking stock selection strategy within the US S&P 500 stock market, employing a quantitative approach that integrates multiple technical factors to generate trading signals. Focusing on a period that mitigated the influence of extreme events, the analysis provided a nuanced examination of the market’s long-term trends and the efficacy of investment strategies. The research scrutinized the predictive capabilities of various technical factors through a rolling window framework and assessed the impact of different window sizes on strategy performance. Findings indicated that the strategy yields positive annualized returns across all selected stocks, yet it was accompanied by significant volatility, particularly for specific stocks. The study highlights the significance of factor selection and the dynamic adjustment of thresholds in optimizing strategy performance. By suggesting the integration of qualitative insights and advanced methodologies, such as machine learning for factor selection and dynamic thresholding, the research pointed to potential enhancements to the strategy. Enhancing the strategy with these elements is expected to bolster its capacity to manoeuvre through the intricacies of financial markets, consequently fortifying its resilience and flexibility to facilitate prudent investment choices.