變更S&P 500指數成分股對標的股效率性之探討

Study on efficiency for changing S&P 500 index component stocks

周用智(Yung-Chih Chou)

指導教授 ： 段昌文

淡江大學/商學院/財務金融學系碩士班/碩士(2010年)

https://doi.org/10.6846/TKU.2010.00711

摘要

Standard and Poor’s公司於其發行 S&P 500指數成份股的篩選原則中，即認為成份股應該要能代表美國甚至全球的經濟情況，因此指數成份股的選取原則將是可觀察其篩選成份股的適當性。本文以S&P 500指數新增成份股為樣本，主要觀察S&P500在宣告新增股與剔除成份股前後的效率性變化情況。首先，使用連檢定和一階自我相關方法檢定新增股與剔除股在宣告日前後之報酬序列隨機性和可預測性的變化情況，進一步將樣本區分為集中與店頭市場，以探討兩者報酬序列隨機性的差異；其次，使用市場模型和Fama-French三因子模型估計累積異常報酬，觀察全市場、集中市場和店頭市場是否為半強式效率。
結果發現，在S&P 500指數成分股變動宣告日後，全市場樣本與集中市場的新增股，報酬序列變的較隨機，而店頭市場在宣告日前後的變化情況，沒有顯著性的變化；且剔除股沒有顯著隨機性的改變。而在S&P 500指數成份變更宣告日後，全市場和分類成集中市場和店頭市場的新增股、剔除股，預測能力皆顯著下降。最後我們發現，在指數成份變更宣告日隔天，全市場樣本與分類的集中市場和店頭市場之新增股有顯著正的異常報酬現象，而剔除股則有顯著為負的異常報酬現象。

關鍵字

隨機性 ； 可預測性 ； 市場模型 ； Fama-French三因子模型

並列摘要

The paper mainly studies the efficiency on market by the sampling the addition and deletion of S&P 500 index components arrounding pre-/post-announcement day. We use the runs test and the first-order autocorrelation test to check the randomness and predictable of returns series. Furthermore, our samples divided into two subsamples base on trading centralized and OTC markets. We also use the market model and the Fama-French three-factor model to estimate the cumulative abnormal returns and to confirm the semi-strong form efficient market.
Empirical results find that return series become more random for stocks added in entire market and concentrated market, but OTC market is not significant changes. And, deletions are not significant changes. We also find that return series become less predictable for stocks added and deleted in entire market, concentrated market and OTC market. Finally, we find that abnormal returns are positive for stocks added and negative for stocks deleted. In comparison with market model and the Fama-French three-factor model, the Fama-French three-factor model can be ruled out a vision, so addictions (deletions) of stocks have more (less) positive (negative) cumulative abnormal returns.

並列關鍵字

Random ； Predictability ； Market model ； Fama-French three-factor model

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