本文關(guān)鍵詞： 網(wǎng)絡(luò)輿情 支持向量機(jī) 股票價格預(yù)測　出處：《南京信息工程大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：自證券市場建立以來,作為高收益和高風(fēng)險并存的股票,一直是眾多投資者關(guān)注的對象。隨著互聯(lián)網(wǎng)絡(luò)平臺的快速發(fā)展,大數(shù)據(jù)時代到來,傳統(tǒng)的股票技術(shù)指標(biāo)數(shù)據(jù)已不能滿足人們分析預(yù)測股票價格的需求。 本文提出一種基于網(wǎng)絡(luò)輿情和股票技術(shù)指標(biāo)數(shù)據(jù)的支持向量機(jī)回歸模型(NPO-SVM),該模型提高了股票價格的預(yù)測精度。模型首先抓取股吧、微博等股評信息,將這些股評觀點(diǎn)用支持向量機(jī)算法分為看漲、看跌、看平三種股評情感傾向,計算看漲股評觀點(diǎn)占看漲股評和看跌股評觀點(diǎn)的比例作為網(wǎng)絡(luò)輿情；然后對網(wǎng)路輿情以及與股票收盤價相關(guān)系數(shù)在0.6以上的股票技術(shù)數(shù)據(jù)作主成分分析,最后對保留的主成分運(yùn)用支持向量機(jī)回歸模型預(yù)測。并與基于股票技術(shù)指標(biāo)數(shù)據(jù)的支持向量機(jī)回歸模型(TI-SVM)以及基于經(jīng)驗?zāi)B(tài)分解的支持向量機(jī)回歸模型(EMD-SVM)作對比,實證分析四只具有代表性的股票,得出NPO-SVM模型比TI-SVM模型、EMD-SVM模型具有更高的預(yù)測精度,可為股票投資者提供一種可靠的預(yù)測股票價格的方法。本文主要研究工作如下： (1)提出了一種將股評文本信息利用SVM機(jī)器學(xué)習(xí),實現(xiàn)文本信息情感分類的新方法。該方法能夠?qū)⒑Ａ?日均百萬條)文本信息準(zhǔn)確分類,測試分類準(zhǔn)確率為85.4%。計算文本分類后的網(wǎng)絡(luò)輿情值,得出網(wǎng)絡(luò)輿情與股票收盤價之間的相關(guān)系數(shù)為0.7,說明網(wǎng)絡(luò)輿情與收盤價之間的相關(guān)性較強(qiáng)。 (2)提出了一種基于網(wǎng)絡(luò)輿情和股票技術(shù)指標(biāo)的支持向量機(jī)回歸模型,對股票收盤價預(yù)測。實證分析結(jié)果表明,NPO-SVM模型的最大相對誤差為2.7%,平均絕對誤差為0.092,平均相對誤差為0.7%,趨勢正確率為76.37%。與TI-SVM模型、EMD-SVM模型相比,NPO-SVM模型的預(yù)測精度明顯提高。
[Abstract]:Since the establishment of the securities market, as a stock with high yield and high risk, it has always been the object of attention of many investors. With the rapid development of the Internet platform, the era of big data has come. The traditional stock technical index data can not meet the demand of people to analyze and forecast the stock price. This paper presents a support vector machine regression model based on network public opinion and stock technical index data. The model improves the precision of stock price prediction. These points of view are divided into bullish, bearish and leveling three kinds of stock review emotional tendency by using support vector machine algorithm, and the proportion of bullish and bearish opinion is calculated as network public opinion. Then the principal component analysis is made on the network public opinion and the stock technical data with a correlation coefficient of 0.6 or more with the closing price of the stock. Finally, support vector machine regression model is used to predict the retained principal components, and compared with the support vector machine regression model (TI-SVM) based on stock technical index data and the support vector machine regression model (EMD-SVM) based on empirical mode decomposition. Through the empirical analysis of four representative stocks, it is concluded that the NPO-SVM model has higher prediction accuracy than the TI-SVM model and can provide a reliable method for stock investors to predict the stock price. The main work of this paper is as follows:. This paper proposes a new method to classify the text information by using SVM machine learning. This method can classify the massive text information (millions of text information per day) accurately. The accuracy of test classification is 85.4. The correlation coefficient between network public opinion and stock closing price is 0.7, which shows that the correlation between network public opinion and closing price is strong. (2) A support vector machine regression model based on network public opinion and stock technical index is proposed. The results of empirical analysis show that the maximum relative error of NPO-SVM model is 2.7, the average absolute error is 0.092, the average relative error is 0.7, and the trend accuracy is 76.370.Compared with the TI-SVM model EMD-SVM model, the prediction accuracy of this model is obviously improved.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F830.91;F224

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