投入與產出因子對資料包絡分析法選股績效之影響

The Effect of Input and Output Factors to Stock Selection Performance Using DEA Methods

10.6338/JDA.201104_6(2).0006

陳信宏(Hsin-Hung Chen)；李秀玉(Hsiu-Yu Lee)

資料包絡分析法 ； 投入因子 ； 產出因子 ； 選股 ； 超額報酬 ； Data envelopment analysis ； input factors ； output factors ； stock selection ； superior return

Journal of Data Analysis

6卷2期（2011 / 04 / 01）

107 - 124

繁體中文

過去的研究顯示，基金經理人及一般股市投資人的操作績效，很少能持續擊敗大盤，可見要提出能夠擊敗大盤的具體方法，並不是一件容易的事情。本研究目的在應用DEA方法，篩選出相對較佳之投資組合的平均股價與大盤平均股價比較，探討其是否能擊敗大盤績效，並評估兩組不同投入與產出因子對資料包絡分析法選股績效之影響。研究結果顯示，資料包絡分析法中的CCR與BCC模型皆能獲得優於大盤的超額報酬，而兩組投入產出因子的比較結果顯示有相關文獻支持的第二組因子所篩選的投資組合，其績效略優於第一組因子篩選的投資組合。

Previous studies have indicated that investors and fund managers in financial service industry were rarely able to earn superior returns. Therefore, selecting stocks with high expected returns to beat the market seems difficult to achieve. The objective of this study is to apply data envelopment analysis (DEA) models to evaluate the efficiency of the firms and construct portfolios by selecting stocks with high efficiency. The return rates of the portfolios constructed by DEA models and market indexes were compared via empirical data analysis. Moreover, this study evaluated the effect of input and output factors to stock selection performance using DEA methods by comparing two groups of input and output factors. The results showed that the return performances of CCR and BCC models in DEA methods significantly better than the Industry Average in the empirical time period. In addition, the portfolios constructed by the second group of factors with previous literature support had better performance than the portfolios constructed by the first group of factors.

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