本文關鍵詞： 傳統(tǒng) 動量策略 反轉(zhuǎn)策略 贏家組合 輸家組合 K近鄰 增強　出處：《杭州電子科技大學》2014年碩士論文　論文類型：學位論文

【摘要】：上個世紀八十年代，有效市場假設理論不能解釋證券市場上出現(xiàn)反應不足和過度反應的異�，F(xiàn)象。行為金融學者認為證券市場上出現(xiàn)反應不足和過度反應是因為投資者對于證券市場上的信息的認知存在偏差，證券投資者的反應不足和過度反應造成市場上的證券價格的動量效應和反轉(zhuǎn)效應。無論是發(fā)達的歐美證券市場，還是發(fā)展中的我國證券市場，均存在動量效應和反轉(zhuǎn)效應。本文以我國A股市場存在動量效應和反轉(zhuǎn)效應為前提，考察傳統(tǒng)動量策略和反轉(zhuǎn)策略收益的同時，對傳統(tǒng)的方法做出改進探索。 考察傳統(tǒng)動量策略和反轉(zhuǎn)策略的收益情況時，利用A股市場2008年1月1日至2013年6月1日的日交易數(shù)據(jù)，對這個時間區(qū)間內(nèi)的入選編制滬深300指數(shù)的股票作為研究對象。不考慮賣空機制的情況下，，運用傳統(tǒng)動量策略和反轉(zhuǎn)策略，形成期采用重疊取樣方法，持有期采用非重疊取樣方法，根據(jù)股票在形成期的累積收益率的大小構造贏家組合和輸家組合，考察投資策略組合的收益情況。 為提高投資回報率，本文對傳統(tǒng)的動量策略和反轉(zhuǎn)策略進行改進探索。傳統(tǒng)動量策略和反轉(zhuǎn)策略在計算形成期股票的累積收益率時，嚴重依賴于形成期期初和期末的價格，而股票單個交易日的價格更多表現(xiàn)為隨機游走，這種度量方法不一定能得到的真正的贏家或輸家組合。為消除股票單個交易日的價格隨機游走帶來的影響，本文嘗試性將統(tǒng)計學知識和數(shù)據(jù)挖掘算法中的K近鄰算法與傳統(tǒng)動量策略和反轉(zhuǎn)策略結合，形成了簡單增強方法和KNN增強方法。 實證得出：運用傳統(tǒng)動量策略和反轉(zhuǎn)策略進行投資時，動量策略和反轉(zhuǎn)策略均存在正的超額收益；傳統(tǒng)的動量策略和反轉(zhuǎn)策略，持有期相同的情況下，月度收益率隨形成期的增加呈增加趨勢；傳統(tǒng)動量策略的超額收益的均值小于反轉(zhuǎn)策略；在改進的方法下，無論是動量策略還是反轉(zhuǎn)策略，兩種增強方法均能提高投資回報率，KNN增強方法的效果好于傳統(tǒng)動量方法和簡單增強方法。
[Abstract]:-20s, The theory of efficient market hypothesis can not explain the abnormal phenomenon of underreaction and overreaction in the securities market. Behavioral finance scholars believe that the underreaction and overreaction in the securities market is due to investors' reaction to the securities market. There is a bias in the cognition of the information on the. The underreaction and overreaction of securities investors cause the momentum effect and reverse effect of the stock price in the market. Whether it is the developed securities market in Europe or America, or the developing securities market in our country, Based on the premise of momentum effect and reversal effect in China's A-share market, this paper investigates the traditional momentum strategy and inversion strategy, and explores the improvement of the traditional methods at the same time. Using the daily trading data from January 1st 2008 to June 1st 2013 in the A-share market, we investigate the returns of the traditional momentum strategy and reverse strategy. For the stocks selected in this time interval to compile the CSI 300 index as the research object, without considering the short selling mechanism, the traditional momentum strategy and the reverse strategy are used, and the overlapping sampling method is used in the formation period. The non-overlapping sampling method is used in the holding period. According to the size of the cumulative return rate of the stock in the forming period, the winner portfolio and the loser portfolio are constructed to investigate the return of the investment strategy portfolio. In order to improve the rate of return on investment, this paper attempts to improve the traditional momentum strategy and reverse strategy, which depend heavily on the price at the beginning and end of the forming period when calculating the cumulative return rate of the stock in the forming period. The price of a stock on a single trading day is more likely to be a random walk, a measure that does not necessarily result in a real winner or loser combination, in order to eliminate the impact of the random walk of the stock price on a single trading day. This paper attempts to combine the K-nearest neighbor algorithm of statistical knowledge and data mining algorithm with the traditional momentum strategy and inversion strategy to form a simple enhancement method and a KNN enhancement method. The empirical results show that the momentum strategy and the reversal strategy have positive excess returns when using the traditional momentum strategy and the reverse strategy, and the traditional momentum strategy and the reversal strategy have the same holding period. The monthly rate of return increases with the increase of the formation period; the average value of the excess return of the traditional momentum strategy is smaller than that of the reverse strategy; under the improved method, the momentum strategy or the inversion strategy, Both methods can improve the rate of return on investment and the effect of KNN enhancement method is better than that of traditional momentum method and simple enhancement method.
【學位授予單位】：杭州電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F224;F832.51

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