指數成分股調整效應是行為財務領域的一大研究課題。近年來隨著中國股市不斷發展，各類指數衍生品層出不窮，指數的編制和調整也就產生越來越大的影響。另一方面，中國股市仍屬於新興市場，指數成分股調整的效應相較國外發達市場也許存在其特殊之處。而面對這一重要課題，中國學界和業界的研究卻略顯不足。鑒於此，本文從短期和長期兩個角度來研究對中國股市影響最大的指數——滬深300指數的成分股調整效應。 在滬深300指數成分股調整的短期效應方面，本文從股價和成交量兩個方面進行了研究。實證結果顯示，在股票剛被調入指數後，股價會產生正的異常報酬且成交量上升，而被調出指數的股票成交量會略微上升且產生負的異常報酬。但是與國外的實證結果相比，滬深300指數成分股調整的短期效應並沒有非常明顯，本文認為這可能與中國股市機構投資人占比過少有關。 在指數成分股調整對調入股和調出股的長期影響方面，本文首先研究了指數調整後的長期股價表現，發現調入股的股價累積報酬優於指數，但不如調入指數前自身的股價表現，調出股則與之相反。接著對股東人數、機構投資人數量和股價波動度進行比較分析。研究發現，指數調整之後，調入股的股東人數會顯著上升，調出股的股東會減少，但該因素對指數調整後股票的長期異常報酬沒有明顯影響；指數成分股調整後機構投資人數量和股價波動度也有明顯變化——調入股的機構投資人增加，波動度降低，調出股機構投資人減少，波動度上升——且這兩個因素對股價異常報酬的影響是顯著的。另外，公司規模大小也是影響股價異常報酬的一個顯著因素。 / The effect of stock index composition changes is one of the important subjects in the field of behavioral finance. With the rapid development of Chinese equity market, stock index is playing an increasingly important part. Chinese equity market, on the other hand, is still at emerging stage, the stock index composition changes may have the different effect from that of the developed countries. However，the correlative study in China is far from enough. This paper investigates the CSI 300 which is the most influential stock index in China to find out the the effect of stock index composition changes in both short term and long term. In the short term, the study focuses on the price and volume. The empirical results show that there is a positive abnormal returns and increasing trading volume of added firms, while a negative abnormal returns and slightly increasing trading volume of deleted firms. However, compared with empirical results abroad, short-term effects associated with the change of the CSI 300 index list is not very obvious, which may be accounted for too little institutional investors in the Chinese stock market. In the long term, this paper firstly studies the long-term stock price performance of the index adjustment. For additions, cumulative return after index adjustment is better than that of the CSI 300 index, but is worse than the performance before the adjustment, while the deletions performance is opposite. Secondly, number of shareholders, institutional investors and stock price volatility are analyzed. There is a significant increase in the number of shareholders of added firms and a decline for deleted firms, but this factor has little influence for abnormal stock price returns. Similarly, for additions, institutional investors increases and volatility reduces, deletions are opposite. Abnormal stock price returns are significantly affected by the number of institutional investors and volatility. In addition, the company size is also a significant factor affecting the abnormal returns.

本文以在中國大陸證券市場上交易量最大，流動性最好的兩隻指數型ETF——華夏上證50ETF（SH510050）和華泰柏瑞滬深300ETF（SH510300）,為一個配對組合，進行統計套利。本文先簡要配對交易的實質和常用方法，以及這一策略目前在全球市場和中國大陸市場上的應用和研究狀況。而後又介紹了這兩隻ETF的標的物——上證50指數和滬深300指數，並闡明為何選取這兩個指數相關的ETF作為統計套利的原因。 接著，分析了華夏上證50ETF和華泰柏瑞滬深300ETF的相關性，從這兩隻ETF的相關性出發，建立共振合模型，並建立一階誤差修正模型對兩隻ETF的短期非均衡狀態進行補充。在此基礎上設定交易規則進行模擬交易。同時我們還在文中後續探討了交易成本和止損點的設置情況。 經過模擬交易，我們發現在一個標準差為開倉閾值的情況下出現的套利機會非常少且收益率較低。因此我們修改交易規則，來探討模型存在的問題，發現當將開倉閾值設為價差序列兩個標準差時，交易次數沒有增加，但收益率有所好轉。當將開倉閾值設為移動平均數和移動標準差，交易次數明顯增加，但收益率並沒有好轉。為進一步驗證上述結論，我們通過樣本外資料進行測試，發現與上述結果一致。此外，我們還通過延長時間序列的方式增加樣本量，得到結果也與上述一致。在用高頻資料交易結果不理想的情況下，我們採用了兩隻ETF的日收盤價格序列建立統計模型和模擬交易，發現在這種情況下，存在套利空間，但第一和第二種策略的套利機會較少，第三種策略套利機會相較前兩種策略要多得多。 分析上述結果產生的原因，主要原因有二：第一，在採用高頻資料的時候，模型的殘差項標準差較小，也就意味著該模型的偏離程度不高，因此套利空間較小。第二，這一配對組合所建立的模型其ECM項係數均非常小，也就意味著模型的長期穩定對時間序列的短期波動影響很小，因此出現的套利機會非常少。 此外，在此說明的是本文所採用的樣本資料為華夏上證50ETF和華泰柏瑞滬深300ETF在2016年7月1日到2016年10月31日每十分鐘的高頻交易價格資料，資料來源為中國大陸的WIND資料庫。 / This essay uses Huaxia SSE50 ETF (Code: SH510050) and Huataiborui CSI300 ETF (Code: SH510300), the two ETFs with the largest trading volume and the best liquidity in the China’s security market, as a pair for statistical arbitrage. Firstly, we introduce the definition of the strategy—pair trading, and its current application in the global and China’s mainland stock market. Then, the essay presents the underlying assets of the two ETFs, SSE50 Index and CSI300 Index, and explains why we choose the two ETFs for statistical arbitrage. Secondly, we analyze the correlation between Huaxia SSE50 ETF and Huataiborui CSI300 ETF, and build the co-integration model based on the correlation. Meanwhile, we establish the first-order error correction model to supplement the short-term imbalance of the two ETFs. On this basis, we set trading rules for simulated transaction. Moreover, we consider trading costs and stop-loss points in this article. After simulated trading, we find that both the trading time and the return are not good enough when we set a standard deviation as the threshold. So we modify trading rules, using the two standard deviations and moving standard deviation as thresholds, but it still doesn’t work. In order to further verify the above conclusion, we change the sample data by adding two times of the original and using the daily closing price, and it reveals that when we use the daily closing price to trade, the yield is better than the high-frequency trading price. There are two reasons for this conclusion. First, the standard deviation of the model’s residual is so little that the arbitrage space is small. Second, the coefficients of ECM is too little, which means the long-term stability of the model has little effect on the short-term volatility of the time series, thus leading to fewer arbitrage chances. In addition, the data used in the article are from the Wind Database in China.