發(fā)布時(shí)間：2018-04-02 19:57

  本文選題：多信息源　切入點(diǎn)：股價(jià)趨勢(shì)預(yù)測(cè)　出處：《計(jì)算機(jī)科學(xué)》2017年10期

【摘要】：股票價(jià)格及趨勢(shì)預(yù)測(cè)是金融智能研究的熱門(mén)話題。一直以來(lái),各種各樣的信息源被不斷嘗試用于股價(jià)預(yù)測(cè),例如基本經(jīng)濟(jì)特征、技術(shù)指標(biāo)、網(wǎng)絡(luò)輿情、財(cái)務(wù)公告、財(cái)政新聞、金融研報(bào)等。然而,此類研究大多數(shù)只使用一種或兩種信息源,使用3種及以上信息源的極為少見(jiàn)。信息源越多意味著能夠提供更加豐富的信息內(nèi)容和更多不同的信息層面。但是由于各種信源的本質(zhì)不同,其對(duì)股票市場(chǎng)的影響程度不同,因此將多種信源融合起來(lái)進(jìn)行股價(jià)預(yù)測(cè)并非易事。此外,多信源也增加了維度災(zāi)難的風(fēng)險(xiǎn)�；谛畔⑷诤系哪康�,嘗試同時(shí)利用基本經(jīng)濟(jì)特征、技術(shù)指標(biāo)、網(wǎng)絡(luò)輿情3種信息源來(lái)進(jìn)行股價(jià)預(yù)測(cè)。具體做法:先對(duì)不同類型的信息源數(shù)據(jù)進(jìn)行針對(duì)性的處理,使其形成統(tǒng)一的數(shù)據(jù)集,然后使用SVM分類器建立預(yù)測(cè)模型。實(shí)驗(yàn)結(jié)果表明,在選用線性核函數(shù)和考慮非交易日數(shù)據(jù)時(shí),使用這3種信源組合的預(yù)測(cè)模型的預(yù)測(cè)效果要比使用單一信源或者兩兩組合的預(yù)測(cè)效果好。此外,在收集數(shù)據(jù)時(shí)發(fā)現(xiàn),在非交易日(例如周末或停牌期)雖沒(méi)有買賣但網(wǎng)絡(luò)輿情劇增。因此,在實(shí)驗(yàn)數(shù)據(jù)中添加了非交易日的輿情情感數(shù)據(jù),分類精準(zhǔn)度有所提高。研究結(jié)果表明,基于多信源融合的股價(jià)預(yù)測(cè)雖然困難,但是在適當(dāng)?shù)剡x擇特征和針對(duì)性地進(jìn)行數(shù)據(jù)預(yù)處理后會(huì)有較好的預(yù)測(cè)效果。
[Abstract]:Stock price and trend prediction is a hot topic in financial intelligence research.For a long time, various information sources have been used in stock price prediction, such as basic economic characteristics, technical indicators, network public opinion, financial announcement, financial news, financial research and so on.However, most of these studies use only one or two sources of information, and the use of three or more sources is extremely rare.The more information sources are available, the richer the information content and the more different levels of information.However, due to the different nature of various information sources and their different impact on the stock market, it is not easy to combine various information sources to predict stock prices.In addition, multiple sources also increase the risk of dimensional disasters.Based on the purpose of information fusion, this paper tries to use three kinds of information sources, such as basic economic characteristics, technical index and network public opinion, to forecast stock price simultaneously.Concrete measures: firstly, the different types of information source data are processed pertinently to form a unified data set, and then the prediction model is established by using SVM classifier.The experimental results show that the prediction effect of the three sources combination is better than that of single source or pairwise combination when the linear kernel function is selected and the non-trading date is considered.In addition, data collection found that in non-trading days (such as weekend or suspension period) although not bought and sold, but the Internet public opinion surge.Therefore, the non-trading day public sentiment data are added to the experimental data, and the classification accuracy is improved.The results show that the stock price prediction based on multi-source fusion is difficult, but it will have a better prediction effect after proper selection of features and targeted data preprocessing.
【作者單位】： 廣東工業(yè)大學(xué)計(jì)算機(jī)學(xué)院;暨南大學(xué)信息科學(xué)技術(shù)學(xué)院計(jì)算機(jī)科學(xué)系;
【基金】：廣東省自然科學(xué)基金(2016A030313084,2016A030313700,2014A030313374) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目(21615438) 廣東省科技計(jì)劃項(xiàng)目(2015B010128007)資助
【分類號(hào)】：F832.51;TP18

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