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題名:多重檢定與股票報酬率橫斷面分析
作者:羅秉政 引用關係
作者(外文):Kendro Vincent
校院名稱:國立臺灣大學
系所名稱:國際企業學研究所
指導教授:林修葳
許育進
學位類別:博士
出版日期:2017
主題關鍵詞:股票報酬率橫斷面分析資料竊探偏誤市場效率多因子投資多重檢定智能選股股市異常報酬
原始連結:連回原系統網址new window
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本論文使用多重檢定方法探討實證金融文獻裡的股市異常報酬現象。為了尋找可獲得異常報酬之選股策略,投資領域專家已不斷的檢定許多模型。若使用傳統假設檢定方法,所發現的優異選股策略可能為統計推論上偏誤所造成。在第一章,我們探討多因子投資策略是否真的提供投資人附加價值。由於投資人可使用不同排列組合建構多因子投資策略,我們必須使用多重檢定方法測試該選股策略之有效性。第二章探索股市橫斷面報酬率是否存在異常現象。該章節討論當我們可以針對異常現象進行分類時,是否影響多重檢定結果。
This dissertation joins the vibrant debate in the empirical finance literature about the cross-sectional stock return anomalies. The endless effort by the finance community to find profitable stock-picking rules has raised questions of data-snooping bias in the empirical findings. The two main chapters of this dissertation investigate the cross-section of stock returns with multiple testing method to eliminate the data-snooping bias concern. The first one examines the efficiency of multi-factor investment strategies prevalent in the equity ETF market. The second one explores the relevance of group information in testing the stock market anomalies.
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