本研究旨在針對臺灣股票市場建構與驗證一套以月營收月增率（MoM）為核心因子的量化選股策略，並進一步納入月營收年增率（YoY）形成雙因子模型。策略每月依據企業營收表現進行MoM與YoY加權排序，篩選出Top 5、Top 10及Top 15投資組合，並採等權重方式持有，每月依據最新資料進行再平衡，以貼近實務操作邏輯。回測期間涵蓋2013年1月至2024年10月，進場日設定為每月最後一個營收公告日之次一交易日。
本研究評估各策略組合於整體研究期間內之績效表現，並針對單因子策略於多頭與空頭市場環境下進行進一步分析，並與臺灣加權股價指數、市場主流ETF（0050、0051）及非Top 15股票群組進行績效比較，採用年化報酬率、Sharpe Ratio、Calmar Ratio、最大回撤與月勝率作為衡量指標。實證結果顯示，Top 15組合於風險調整後表現最為穩健，特別在多段空頭期間展現顯著抗跌能力與報酬穩定性，適合作為長期資產配置參考。Top 5組合雖具最高累積報酬，惟其波動性與回撤幅度相對較高，較適合風險承受能力較高之投資人。
進一步實證結果顯示，雙因子策略在報酬穩定性與下檔風險控制層面明顯優於單因子策略。其中以Top 15組合表現最佳，Sharpe Ratio達1.505、Calmar Ratio達1.58，月勝率高達57.04%，驗證年增率因子能有效補足月增率因子，有助於提升策略穩健性與市場適應能力。
綜合而言，月營收相關因子在臺灣高頻揭露的資訊環境中展現高度資訊解釋力與實務可行性。本研究所建構之雙因子量化選股策略，除具備理論貢獻外，亦展現高度應用潛力，未來可進一步擴展至其他市場環境與因子架構，作為量化投資之重要參考依據。

This study constructs and validates a quantitative stock-selection strategy for the Taiwanese stock market, using month-over-month (MoM) revenue growth as the core factor and incorporating year-over-year (YoY) revenue growth to establish a dual-factor model. Each month, firms are ranked based on weighted MoM and YoY performance, from which Top 5, Top 10, and Top 15 equally weighted portfolios are formed and rebalanced using updated revenue data. The back-testing period spans from January 2013 to October 2024, with trading initiated on the first session following the final monthly revenue announcement.
The study evaluates the performance of each strategy over the entire period and further analyzes the single-factor strategy under both bullish and bearish market conditions. Performance is benchmarked against the Taiwan Weighted Stock Index, major ETFs (0050, 0051), and non-Top 15 stock groups, using metrics including annualized return, Sharpe Ratio, Calmar Ratio, maximum drawdown, and monthly win rate. Results show that the Top 15 portfolio delivers the most robust risk-adjusted performance, particularly during extended bear markets, making it suitable for long-term allocation. Although the Top 5 portfolio achieves the highest cumulative return, it also experiences higher volatility and drawdowns, thus fitting investors with greater risk tolerance.
Further empirical evidence demonstrates that the dual-factor strategy significantly outperforms the single-factor approach in terms of return stability and downside risk control. The Top 15 portfolio performs best, with a Sharpe Ratio of 1.505, Calmar Ratio of 1.58, and a monthly win rate of 57.04%, confirming that the YoY factor serves as an effective complement to the MoM factor and enhances overall strategy robustness.
In conclusion, revenue-based factors exhibit strong informational value and practical applicability within Taiwan's high-frequency disclosure environment. The proposed dual-factor strategy contributes both theoretically and practically, offering a valuable framework that may be extended to other markets and factor models in the context of quantitative investing.

Chan, L. K. C., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. The Journal of Finance, 51(5), 1681–1713.
Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2014). Modern Portfolio Theory and Investment Analysis (9th ed.). Wiley.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56.
Graham, B., & Dodd, D. L. (1934). Security Analysis. McGraw-Hill.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: implications for stock market efficiency. The Journal of Finance, 48(1), 65–91.
顧廣平（2010）。〈應用月營收成長率建構動能策略之研究〉。國立中山大學財務管理研究所碩士論文。
黃國基（2011）。〈基本面選股策略與投資組合分散效果之關聯性研究〉。國立高雄第一科技大學財務管理研究所碩士論文。
沈勤瑋（2015）。〈每月營收公告對股價異常報酬之影響〉。明志科技大學財務金融系碩士論文。
林冠宏（2017）。〈因子投資策略之回顧及重現—以台灣股市為例〉。國立中正大學財務金融研究所碩士論文。
陳建志（2019）。〈台股量化因子模型策略之研究〉。國立政治大學財務管理研究所碩士論文。
吳信昌（2020）。〈應用月營收資訊於台股投資策略之研究〉。國立中山大學財務管理研究所碩士論文。
台灣經濟新報研究中心（2023）。〈量化投資因子：月營收資訊實證〉。TEJ 研究報告。取自：https://www.tejwin.com/news/量化投資因子-月營收資訊實證/