【摘要】：股票市場(chǎng)是證券市場(chǎng)的重要組成部分,隨著人們對(duì)證券市場(chǎng)的深入了解,人們?cè)絹碓疥P(guān)心的股市問題歸根結(jié)底就是股票價(jià)格變化及走向情況,以期獲得短期收益。股票價(jià)格有很大的波動(dòng)性、不確定性,要掌握所有影響股票價(jià)格的信息是極其困難的,現(xiàn)在已有多種股票價(jià)格預(yù)測(cè)模型產(chǎn)生,并取得了良好的效果,但只根據(jù)股票價(jià)格做預(yù)測(cè)模型的方法并不多,其中較為典型的數(shù)學(xué)模型有灰色模型和馬爾可夫鏈�；疑Ｐ途褪且粋�(gè)研究預(yù)測(cè)股價(jià)走向的灰色系統(tǒng),在該系統(tǒng)中股票市場(chǎng)除了股票價(jià)格信息被看作已知外,其他任何信息均看作未知,即在模型中股價(jià)被看作一個(gè)灰色量,然后采用已知的指定股價(jià)序列通過該模型來預(yù)測(cè)該只股票未來短期內(nèi)的價(jià)格走向甚至價(jià)格；在應(yīng)用過程中該模型表現(xiàn)出了建立數(shù)學(xué)模型所需數(shù)據(jù)較少、模型簡(jiǎn)單、預(yù)測(cè)精度較高等特點(diǎn)�；疑Ｐ皖A(yù)測(cè)是建立在GM(1,1)數(shù)學(xué)模型基礎(chǔ)之上,該模型以具體量化的形式來預(yù)測(cè)某股票未來價(jià)格走向；它的解集表現(xiàn)為指數(shù)型曲線,它不適合對(duì)具有較大波動(dòng)性的股票價(jià)格走向進(jìn)行研究預(yù)測(cè)。馬爾可夫過程是一種隨機(jī)過程,馬爾可夫鏈以具體形式反映了馬爾可夫過程,它先依據(jù)不同的標(biāo)準(zhǔn)將已知的股票價(jià)格序列數(shù)據(jù)劃分到不同的狀態(tài)中,再依據(jù)各狀態(tài)之間的轉(zhuǎn)移概率來預(yù)測(cè)股票價(jià)格未來所處的狀態(tài),反映了股票價(jià)格之間的內(nèi)在規(guī)律性,它適合對(duì)波動(dòng)性較大及數(shù)目足夠準(zhǔn)確的股票價(jià)格序列進(jìn)行預(yù)測(cè)。因此將兩種方法結(jié)合起來,作灰色馬爾可夫預(yù)測(cè),既避免了兩者的缺點(diǎn),又發(fā)揚(yáng)了兩者的優(yōu)點(diǎn),可以明顯提高股票價(jià)格的預(yù)測(cè)精度。本文節(jié)選了2012年1月4日到2012年6月13日的滬深300指數(shù)日收盤價(jià)格作為模型的樣本序列,通過對(duì)該樣本序列建立灰色馬爾可夫模型,預(yù)測(cè)出了該股票未來5天內(nèi)的價(jià)格值。結(jié)果表明：雖然該模型下的預(yù)測(cè)結(jié)果和實(shí)際結(jié)果之間有誤差,但誤差很小,且預(yù)測(cè)精度比GM(1,1)模型高,表明了該模型用于股票價(jià)格預(yù)測(cè)的優(yōu)越性；利用該模型做股票價(jià)格預(yù)測(cè)能夠?yàn)槿藗冋咛峁┎糠值耐顿Y參考作用。
[Abstract]:The stock market is an important part of the securities market. With the in-depth understanding of the securities market, people are more and more concerned about the stock market in the final analysis is the stock price change and trend, in order to obtain short-term returns. The stock price has great volatility and uncertainty, so it is extremely difficult to master all the information that affects the stock price. Now a variety of stock price prediction models have been produced, and good results have been achieved. However, there are not many methods to make prediction models only according to stock price, among which the more typical mathematical models are grey model and Markov chain. Grey model is a grey system to predict the trend of stock price. In this system, except that the stock price information is regarded as known, any other information is regarded as unknown, that is to say, the stock price is regarded as a gray quantity in the model. Then the known specified stock price sequence is used to predict the price trend and even the price of the stock in the short term in the future. In the process of application, the model shows the characteristics of less data needed to establish the mathematical model, simple model and high prediction accuracy. Grey model prediction is based on GM (1, 1) mathematical model, which forecasts the future price trend of a stock in a specific quantitative form. Its solution set is an exponential curve, which is not suitable for the research and prediction of the stock price trend with great volatility. Markov process is a kind of stochastic process. Markov chain reflects Markov process in concrete form. It first divides the known stock price sequence data into different states according to different standards. Then, according to the transition probability between the states, the future state of the stock price is predicted, which reflects the inherent regularity between the stock prices, and it is suitable for predicting the stock price series with large volatility and sufficient accuracy. Therefore, combining the two methods to make grey Markov prediction can not only avoid the shortcomings of the two methods, but also carry forward the advantages of the two methods, which can obviously improve the prediction accuracy of stock price. In this paper, the daily closing price of Shanghai and Shenzhen 300 Index from January 4, 2012 to June 13, 2012 is selected as the sample sequence of the model. Through the establishment of grey Markov model for the sample sequence, the price value of the stock in the next five days is predicted. The results show that although there is an error between the prediction results and the actual results, the error is very small, and the prediction accuracy is higher than that of GM (1, 1) model, which shows the superiority of the model in stock price prediction. Using this model to predict the stock price can provide some investment reference for people.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F224;F830.91

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本文編號(hào)：2496767