以實驗計畫法與迴歸分析建構多因子選股系統

過去多因子選股模型的研究文獻大多用評分法，但其各項評分項目的權重都使用主觀設定。這種主觀設定的方法除了不能最佳化選股績效外，也無法依投資人的偏好，決定最佳的選股因子權重。本文以配方實驗設計與迴歸分析建構證券投資決策系統，以克服上述缺點。本文篩選出六個選股概念：小股價淨值比(PBR)、大股東權益報酬率(ROE)、大近三月年營收成長、大季報酬率、大總市值、小系統風險β。結果顯示(1) 除了模型的解釋力較差的月報酬率相對大盤勝率、月報酬率絕對勝率兩個模型之外，全二階迴歸模型優於簡化後的二階迴歸模型與一階迴歸模型。(2) 在最大化年化報酬率模最佳化模型中，最重要的選股策略因子是ROE 及 PBR。(3) 在最小化年化報酬率標準差最佳化模型中，則是ROE 及β。

關鍵字

股市；選股；多因子模型；配方；實驗計畫法；迴歸分析；最佳化

並列摘要

Over the past most multi-factor stock selection models used score approach, and subjectively set the score weight for each factor. This subjective approach not only can not optimize performance of stock selection model, but also can not determine the best weights in accordance with the preferences of investors. This study employed mixture design of experiment to construct stock investment decision-making system to overcome these shortcomings. Factor screening experiments picked six stock selection concepts: small price to book value ratio (PBR), large return on equity (ROE), large annual revenue growth rate during recent three months, large quarter return, large total market capitalization, and small systematic risk β. The results showed that (1) except to the monthly relative winning rate model and the monthly absolute winning rate model, whose explanatory power was rather poor, the full second-order regression model is superior to the simplified second-order regression model and the first order regression model. (2) In the maximizing annual rate of return optimization model, the most important stock selection factors were the ROE and PBR. (3) In the minimizing standard deviation of annual rate of return optimization model, they were the ROE and β.