【摘要】：與現(xiàn)代經(jīng)濟不斷的發(fā)展相適應，大眾的投資意識不斷地發(fā)生變化，股票投資已經(jīng)成為人們投資理財?shù)闹匾侄�，股票投資日益變成社會大眾關心的熱門話題，因此股票市場的質(zhì)量以及繁榮程度成為了管理人員和投資者關注和研究的熱點。股票投資有收益-風險成正比的特點，即預期收益越高，伴隨的可能的風險也越大。因而，對股票市場預測方法的探索具有非常高的經(jīng)濟價值和理論意義。由于股票的價格形成機制十分復雜，有很多的外部因素都會對其產(chǎn)生影響，因此要準確的預測股票市場不是一項容易的任務，以往使用過的預測方法在股市預測的應用中都不能取得非常滿意的效果。 本文在深入分析了行為金融學理論以及神經(jīng)網(wǎng)絡建模理論的基礎上，選取交易價格和交易者情緒這兩個能夠反映市場信息和價格形成過程的維度對上證指數(shù)建立一個BP神經(jīng)網(wǎng)絡模型進行短期的預測，用以檢驗BP神經(jīng)網(wǎng)絡在股價預測方面的應用效果。在以往關于投資者情緒的研究中通常的作法是選取一個技術指標作為交易者情緒的代理變量，，本文在前人研究的基礎上，應用主成分分析法構(gòu)造了一個交易者情緒指數(shù)用來代表市場上交易者的情緒，進而作為預測模型的一個輸入變量。同時，對上證指數(shù)進行線性模型的建模，作為對比模型來檢測神經(jīng)網(wǎng)絡的預測效果。 理論分析和實驗結(jié)果說明，應用神經(jīng)網(wǎng)絡對上證指數(shù)進行預測具有一定的有效性，加入構(gòu)造的情緒指數(shù)能夠顯著的提高模型的預測精度，在股指預測領域有一定的應用價值。
[Abstract]:In keeping with the constant development of modern economy, the public's investment consciousness is constantly changing, and stock investment has become an important means for people to invest and manage money. Stock investment has increasingly become a hot topic of public concern. Therefore, the quality and prosperity of stock market have become the focus of attention and research by managers and investors. The higher the expected return, the greater the possible risk associated with stock investment. Therefore, the exploration of stock market forecasting method has high economic value and theoretical significance. Because the stock price formation mechanism is very complex, there are many external factors will have an impact on it, so it is not an easy task to accurately predict the stock market. The prediction methods used in the past can not achieve very satisfactory results in the application of stock market forecasting. Based on the in-depth analysis of behavioral finance theory and neural network modeling theory, Selecting the two dimensions which can reflect the process of market information and price formation, the transaction price and the traders' sentiment are selected to establish a BP neural network model for short-term prediction of Shanghai Stock Exchange Index. It is used to test the application effect of BP neural network in stock price prediction. In the past studies on investor sentiment, the usual method is to select a technical index as the proxy variable of traders' sentiment. The principal component analysis (PCA) is used to construct a trader's emotion index to represent the traders' emotion in the market, and then it is used as an input variable of the prediction model. At the same time, the linear model of Shanghai Stock Exchange Index is built to detect the prediction effect of neural network as a contrast model. The theoretical analysis and experimental results show that the application of neural network to the prediction of Shanghai stock index has a certain effectiveness, adding the constructed emotional index can significantly improve the prediction accuracy of the model, and has a certain application value in the field of stock index prediction.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F832.51;F224

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