本文選題：多元線(xiàn)性回歸 + 多重共線(xiàn)性�。� 參考：《南京大學(xué)》2014年碩士論文

【摘要】：股票價(jià)格是中國(guó)絕大多數(shù)公民關(guān)心的問(wèn)題,也是金融、經(jīng)濟(jì)、系統(tǒng)科學(xué)等領(lǐng)域研究的熱點(diǎn)問(wèn)題。由于中國(guó)股票市場(chǎng)的股票價(jià)格時(shí)間序列是序列相關(guān)的(即歷史數(shù)據(jù)對(duì)股票的價(jià)格形成起作用),因此,我們可以通過(guò)對(duì)歷史信息進(jìn)行分析來(lái)預(yù)測(cè)未來(lái)的股票價(jià)格�；诖�,本文即在傳統(tǒng)的多元線(xiàn)性回歸基礎(chǔ)之上,利用因子分析模型進(jìn)行優(yōu)化,從而消除模型的多重共線(xiàn)性而達(dá)到更好的擬合效果。相較于現(xiàn)有的股票預(yù)測(cè)方法,該方法數(shù)據(jù)搜集簡(jiǎn)便且對(duì)于數(shù)據(jù)的選取無(wú)特定要求,預(yù)測(cè)結(jié)果擬合度高,適用于大多數(shù)股票。本文以廣州藥業(yè)和西部礦業(yè)兩只股票的歷史價(jià)格為實(shí)例,以當(dāng)日開(kāi)盤(pán)價(jià)、最高價(jià)、最低價(jià)、收盤(pán)價(jià)、成交額、成交量及次日開(kāi)盤(pán)價(jià)為自變量,預(yù)測(cè)該股票的次日收盤(pán)價(jià),通過(guò)對(duì)比消除共線(xiàn)性前后的兩個(gè)模型對(duì)于收盤(pán)價(jià)的預(yù)測(cè)結(jié)果,驗(yàn)證了利用因子分析模型消除共線(xiàn)性后的回歸方程預(yù)測(cè)效果更好。
[Abstract]:Stock price is a hot issue in the fields of finance, economy, system science and so on. Because the time series of stock price in Chinese stock market is sequence-dependent (that is, historical data play an important role in the formation of stock price), we can predict the stock price in the future by analyzing the historical information. Based on this, based on the traditional multivariate linear regression, the factor analysis model is used to optimize the model so as to eliminate the multiple collinearity of the model and achieve a better fitting effect. Compared with the existing stock forecasting methods, the proposed method is easy to collect and has no specific requirements for data selection. It is suitable for most stocks because of its high fitting degree. This paper takes the historical price of Guangzhou Pharmaceutical Industry and the West Mining Industry as an example, taking the opening price, the highest price, the lowest price, the closing price, the turnover, the volume and the opening price of the next day as independent variables to predict the closing price of the stock the next day. By comparing the prediction results of two models before and after elimination of collinearity to the closing price, it is verified that the regression equation with factor analysis model is better than the other models.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F832.51

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