發(fā)布時間：2018-04-10 03:02

  本文選題：股票預測　切入點：情感分析　出處：《內蒙古大學》2017年碩士論文

【摘要】：股票投資是一種非常活躍的投資理財方式。投資者在股票市場上的交易行為都以盈利為目的。目前,股票預測大多僅基于股票交易的歷史數(shù)據(jù)。本文研究結合股票評論文本情感分析的股票預測模型。模型設計中分析情感傾向、股票交易指標、時間序列等方面的數(shù)據(jù)。情感分析:本文以活躍股評論壇特定股票的股評文本作為分析數(shù)據(jù)。這些論壇數(shù)據(jù)是大量含噪音的短文本,反映的是中小股票投資者的觀點。使用SVM分類器,利用JAVA版本的LIBSVM工具包進行文本分類,計算分析得到情感傾向指數(shù)Bs。工作改進:分析多時段數(shù)據(jù)建立BP網(wǎng)絡預測模型,并根據(jù)MIV算法求解不同時段數(shù)據(jù)的不同影響值;在計算文本情感傾向指數(shù)Bs時,加入文本作者影響權重。模型設計:只有五個股票交易指標作為輸入量的BP神經網(wǎng)絡模型,作為參考模型,記為模型一;結合情感指數(shù)Bs和五個股票交易指標的多指標BP網(wǎng)絡模型,記為模型二;分析預測日之前五個交易日收盤價的多時段BP網(wǎng)絡預測模型,記為模型三。結論:包括情感指數(shù)的預測模型二要比模型一的準確性高;模型三結合MIV算法得出了預測日前五個交易日的影響權重值,結果符合越靠近預測日的數(shù)據(jù)影響權重越大的趨勢。
[Abstract]:The stock investment is one kind of very active investment finance way.Investors in the stock market trading behavior with the purpose of profit.At present, stock forecasts are mostly based on historical data of stock trading.This paper studies the stock prediction model based on the emotion analysis of stock review text.In the design of the model, we analyze the data of emotion tendency, stock trading index, time series and so on.Affective Analysis: this paper uses the stock review text of the active Stock Review Forum as the analysis data.These forum data are a lot of noisy short-text, reflecting the views of small and medium-sized stock investors.By using SVM classifier and LIBSVM toolkit of JAVA version, text classification is carried out, and the affective tendency index (Bs.) is obtained by calculation and analysis.Work improvement: the BP neural network prediction model is established by analyzing the multi-period data, and the different influence values of the data in different periods are solved according to the MIV algorithm, and the influence weight of the text author is added in the calculation of the text affective tendency index Bs.Model design: there are only five stock trading indicators as input BP neural network model, as a reference model, as model one, combined with emotion index Bs and five stock trading indicators of multi-index BP network model, as model two;The BP neural network forecasting model of five trading days before the forecast date is described as model 3.Conclusion: the accuracy of the prediction model 2 including emotion index is higher than that of model 1. Model 3 combined with MIV algorithm has obtained the influence weight of the first five trading days of the forecast day, and the result accords with the trend that the influence weight of the data closer to the forecast day is greater.
【學位授予單位】：內蒙古大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.1

【參考文獻】

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本文編號：1729348