本研究旨在探討因子選股策略在現代金融市場中的應用與效果，特別 是雙因子選股模型在實際投資中的性能比較。隨著大數據技術和量化分析方法 的發展，因子選股已成為識別投資機會的重要工具。本研究採用交集篩選法、 過濾篩選法、加權內插法與加權排名法四種不同的雙因子選股模型進行實證分 析，通過對比各模型在相同回測條件下的表現，評估它們對投資組合績效的影 響。結果顯示，不同的選股策略對於提升回報率及風險管理具有顯著差異。本 研究開發了一個股票回測工具，支援因子分析，並通過多種回測指標檢視績 效。研究結果為投資者提供了一套更客觀、結構化的選股框架，幫助他們在多 變的市場環境中做出更精確的投資決策。;This study aims to explore the application and effectiveness of factor-based stock selection strategies in modern financial markets, with a particular focus on the performance comparison of two-factor stock selection models in practical investments. With the development of big data technology and quantitative analysis methods, factor-based stock selection has become an important tool for identifying investment opportunities. This study employs four different two -factor stock election models: Intersection screening method, Filter screening method, Weighted interpolation method, and Weighted ranking method, to conduct empirical analysis. By comparing the performance of each model under the same backtesting conditions, the study evaluates their impact on portfolio performance. The results indicate significant differences among the stock selection strategies in terms of improving returns and managing risk. This research develops a stock backtesting tool that supports factor analysis and examines performance through various backtesting indicators. The findings provide investors with a more objective and structured stock selection framework, assisting them in making more precise investment decisions in a volatile market environment.
