本文選題：股票預(yù)測(cè)　切入點(diǎn)：情感分析　出處：《內(nèi)蒙古大學(xué)》2017年碩士論文

【摘要】：股票投資是一種非�；钴S的投資理財(cái)方式。投資者在股票市場(chǎng)上的交易行為都以盈利為目的。目前,股票預(yù)測(cè)大多僅基于股票交易的歷史數(shù)據(jù)。本文研究結(jié)合股票評(píng)論文本情感分析的股票預(yù)測(cè)模型。模型設(shè)計(jì)中分析情感傾向、股票交易指標(biāo)、時(shí)間序列等方面的數(shù)據(jù)。情感分析:本文以活躍股評(píng)論壇特定股票的股評(píng)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(diǎn)。使用SVM分類器,利用JAVA版本的LIBSVM工具包進(jìn)行文本分類,計(jì)算分析得到情感傾向指數(shù)Bs。工作改進(jìn):分析多時(shí)段數(shù)據(jù)建立BP網(wǎng)絡(luò)預(yù)測(cè)模型,并根據(jù)MIV算法求解不同時(shí)段數(shù)據(jù)的不同影響值;在計(jì)算文本情感傾向指數(shù)Bs時(shí),加入文本作者影響權(quán)重。模型設(shè)計(jì):只有五個(gè)股票交易指標(biāo)作為輸入量的BP神經(jīng)網(wǎng)絡(luò)模型,作為參考模型,記為模型一;結(jié)合情感指數(shù)Bs和五個(gè)股票交易指標(biāo)的多指標(biāo)BP網(wǎng)絡(luò)模型,記為模型二;分析預(yù)測(cè)日之前五個(gè)交易日收盤價(jià)的多時(shí)段BP網(wǎng)絡(luò)預(yù)測(cè)模型,記為模型三。結(jié)論:包括情感指數(shù)的預(yù)測(cè)模型二要比模型一的準(zhǔn)確性高;模型三結(jié)合MIV算法得出了預(yù)測(cè)日前五個(gè)交易日的影響權(quán)重值,結(jié)果符合越靠近預(yù)測(cè)日的數(shù)據(jù)影響權(quán)重越大的趨勢(shì)。
[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(xué)位授予單位】：內(nèi)蒙古大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.1

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本文編號(hào)：1729348