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.

本篇論文以滬深300 指數調整前後期的異常報酬、影子成本、流動性、資訊不稱性及套利風險的變動觀察中國投資者的行為。本研究發現在調整後的短期間中中國股票的報酬與國外文獻的變動方向一致,調入股將上漲而調出股則下跌，但於長期則有十分明顯的相反傾向。本文將影子成本等四個變數加入作前後期變動的觀察。發現調整期前後影子成本、流動性、套利風險和資訊不對稱性的變動都與與文獻變動方向假設一致。最後本文把異常報酬作應變數，其餘各項作自變數去觀察四個變數影響報酬的程度和方向。回歸後發現只有流動性的影響符合前人以S&P500 作指標的研究,其他則是有著不一致的影響。本文認為這個現象與文獻中不同的原因是滬 深300 指數偏向於納入高估的股票而剔除低估的股票。滬深300 指數是以股票前一年的交易量大小作標準，這使得81%交易量為個人投資者提供的滬深300 指數偏向納入高估股票。這可能使得中國市場的指數調整效應與文獻並不一致。 / This paper empirically examines the differences of abnormal return, shadow cost, liquidity effect, information asymmetry and arbitrage risk during the composition change of CSI300 index to observe the behavior of investors in China market. Although this paper examines the short term return of adjusted stock change in the same direction as recent studies, added stocks increase and deleted stocks decrease, the long term return reverse. This paper also computes those four variables to observe their changes during the adjustment. The results show that the movements of these four variables are similar to the previous studies. To observe how these variables affect the return of the stocks, this paper computes a regression analysis with the cumulative abnormal return as the dependent variable. The results show that only the affection of liquidity matches the recent studies of S&P 500, when the others are not. The reason of this phenomenon maybe because of the CSI 300 index intends to include the overestimated stocks and exclude the underestimated stocks. The adjustment is determined by the past one year trading volume, which means that the market, with individual investors provided 81 % of trading volume, may possibly overestimate the included stocks. That maybe the reason why the influence of composition change is not similar to recent studies.

指數效應是指當股票調入或調出某個指數的時候，該股票的價格及成交量所出現的異常反應。這種有悖於市場有效性的金融現象廣受學者和投資者的關注，國外學者對指數效應的研究已較為成熟，形成了價格壓力假說、流動性假說、向下傾斜的需求曲線假說、資訊含量假說以及市場區隔假說等5種用於解釋這一現象的理論假說。 中國指數的發展較晚，所以學者對其指數效應的研究還不夠完善。本文以2006年至2015年滬深300指數調入和調出股票為研究對象，利用事件窗研究法檢驗股票的價格效應和成交量效應是否存。本文發現滬深300指數調入和調出股票的指數效應顯著，且具有不對稱性。調入股票的正異常報酬長期存在且不反轉，而調出股票出現的負異常報酬短期內會反轉為正，且反轉後在長事件窗內累計異常報酬甚至高於調入股票；調出股票的平均交易比率也要比調入股票更為顯著且活躍。 本文研究指出，歸因於指數基金追蹤行為的價格壓力假說與向下傾斜的需求曲線假說只能部分的解釋滬深300指數效應。因此本文將調入和調出股票價格效應不對稱性與公司基本面的研究相結合，通過分析滬深300指數調整後調入和調出股票的EPS預測值、實際EPS的變動、分析師覆蓋以及機構投資者數量證實了資訊含量假說以及市場區隔假說的解釋力。本文以長期累計異常報酬對流動性假說、資訊含量假說和市場區隔假說中各個經典指標進行多元迴歸分析，發現指數成分股調整前後EPS的變動可以解釋調入股票和調出股票價格效應的存在性和不對稱性，此外資訊含量假說對滬深300指數效應的解釋力度相對較大。由此證明了滬深300指數調整隱含有關公司基本面的額外資訊。

本研究依據國內、外學者對指數股票型基金(ETF)所做之相關研究架構，探討寶滬深300(0061)、恆中國(0080)以及恆香港(0081)分別自2009年8月17日及2009年8月14日上市以來之交易表現，主要實證結果為：一、在追蹤誤差方面，香港聯交所的標智滬深300(2827)和台灣證交所的寶滬深300(0061)、恆中國(0080)及恆香港(0081)之淨資產日報酬平均低於其標的指數日報酬，但均顯著不為零；除寶滬深300(0061)外，其餘3檔ETF追蹤誤差均不大，寶滬深300(0061)在層層之商品關聯架構下，無法有效複製滬深300指數之表現，產生極大的追蹤誤差。二、在折溢價方面，寶滬深300(0061)折價溢價出現比例無顯著不同，就資產淨值減去市價之衡量方式而言，大部分交易情形為-0.3元(溢價)至0.2元(折價)進行交易；恆中國(0080)與恆香港(0081)樣本期間多以折價情形交易，恆中國(0080)折溢價幅度為-5元(溢價)至15元(折價)，恆香港(0081)則以-5元(溢價)至20元(折價)間進行交易，3檔ETF仍存在套利空間。三、在折溢價持續性方面，寶滬深300(0061)、恆中國(0080)以及恆香港(0081)之折溢價情形均持續存在，存續時間為兩日。四、在成交量方面，寶滬深300(0061)平均每日交易量為6,131張，成交量顯著受標的指數市場波動度與淨值市價差額影響；恆中國(0080)與恆香港(0081)日均成交量分別只有154張及21張。此外，迴歸檢定後發現，市場波動度與套利價差會顯著影響恆中國(0080)成交量，恆香港(0081)之成交量則只受套利價差影響。

本文係利用高頻資料研究股票指數期貨的到期日效應，考量到中國的衍生性商品起步甚晚，相關研究不如台灣來的多，因此選取中國的滬深300股指期貨市場作為本研究的主題，希望能夠為後續有興趣的研究者提供參考。但是因為中國市場的資料取得不易，本文所使用的樣本資料只為期兩年，選取2010年4月16日到2012年4月20日的滬深300股指期貨的1分鐘高頻數據作為原始數據。 本文目的在於研究滬深300股指期貨經由考慮成交量、價格反轉以及波動度的到期日效應，實證結果發現在期貨到期日當天與隔一天的某些交易時段明顯存在著型0、型I與型II價格反轉，成交量檢驗指出，到期日成交量明顯大於非到期日成交量，波動度異常檢驗顯示，到期日收盤前五分鐘的波動度有異常放大的現象。本文的實證結果部分，利用模擬投資策略去檢驗價格反轉在經濟上是否有意義，發現價格反轉不只是在統計上顯著，同樣也是具有經濟意義的，但是把資料依據時間區分為前後兩部分並做檢驗之後也發現，這種經濟意義會隨著時間而呈現遞減的狀態。 / The central idea of this thesis is studying expiration effects of stock index futures. As we know, China stock index futures market, which is also known as CSI 300 Index futures market, is experiencing its early stage with fewer related studies comparing to Taiwan stock futures market. In order to provide research references for succeeding researchers interested in CSI 300 Index futures market. However, having difficulties collecting high frequency market data from CSI 300 Index futures market, we use only two years data from the beginning of CSI 300 Index futures market. The main purpose of this thesis is to study the expiration effect of CSI 300 Index futures by from three aspects, price reversals, volume effects and abnormal return volatility. The empirical results shows that type 0, type I and type II existed in several trading hours in both the expiration day and the next trading day. Second, it indicated that the trading volume in expiration days is significantly larger than in non-expiration days. Third, the empirical result also pointed out that magnified return volatilities existing in five minutes before market closes on the expiration day. Moreover, we used simulated investment strategies as analysis tools and found that price-reversal effect is significant on economical basis. However, we discovered that the level of these effects is declining gradually from the beginning to the end of data period.

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.