換個角度選股！學會這兩個因子勝率大幅提升 (Fama and French,1992)

from 論文投資學 Quantified Dialogues · host J&A

這裡是論文投資學 Quantified Dialogues，每周用AI探討有意思的投資學術論文和我們一起踏入投資的專業世界論文大綱: 此研究論文探討了股票平均報酬率的橫截面變異，並質疑了夏普-林特納-布萊克（SLB）模型的核心預測，即市場貝他值（β）足以解釋股票報酬。作者發現，在1963年至1990年間，公司規模（市值）和帳面市值比這兩個容易衡量的變數，更能有效捕捉平均股票報酬率的變異，甚至能取代槓桿率和盈餘價格比的解釋力。內文討論:   A. 為什麼市值（Size）和帳面市價比（B/M）能預測股票的預期報酬？ B. 論文裡說 β 沒有解釋力，這不就打臉 CAPM 模型了嗎？ C. 市值小的公司為什麼長期報酬比較高？是風險還是市場低估？ D. 高 B/M 比的股票就是「價值股」嗎？這樣分類真的可靠嗎？ E. 他們是怎麼用回歸方式來檢驗哪些因子對報酬有影響？ F. 有沒有可能 Size 和 B/M 的效果只是統計巧合？ G. 這個模型跟三因子模型有什麼關聯？三因子模型就是從這篇來的嗎？ H. 論文的資料是從哪個市場？這個結果能套用到其他國家嗎？ I. 這些變數預測的報酬是超額報酬嗎？還是總報酬？ J. 如果我照這篇的方法選股，實務上要怎麼操作？有什麼工具可以用？   資料來源: Fama, Eugene F., and Kenneth R. French. "The cross‐section of expected stock returns." the Journal of Finance 47.2 (1992): 427-465. Powered by Firstory Hosting

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換個角度選股！學會這兩個因子勝率大幅提升 (Fama and French,1992)

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