本文選題：神經(jīng)網(wǎng)絡　切入點：股指預測　出處：《重慶師范大學》2017年碩士論文　論文類型：學位論文

【摘要】：股市在金融市場中占據(jù)著不可替代的作用,而反應股市總的走勢是股指。股指的走勢是監(jiān)管部門對股票市場合理調(diào)控和針對性監(jiān)管的依據(jù),同時股指的走勢能讓投資者更正確認識和把握我國股票市場波動的混沌規(guī)律,并且它可以為投資組合等重大課題提供一些實際依據(jù)。因此,如何對股指進行準確預測就具有非常重要的意義。由于股指的影響因素眾多、波動幅度較大,因素間具有高噪聲和非線性等諸多特征,傳統(tǒng)的股票價格預測方法無法消除數(shù)據(jù)的高噪聲。因此,為了提高股指預測精度,在前人研究的基礎上,本文做了如下的研究:本文首先綜述了影響股指走勢的相關因素,在此基礎上,本文選擇了21個宏觀經(jīng)濟指標和6個輿情指標,構成了27個變量指標,隨后研究了這些變量與滬深300指數(shù)收盤價之間的相關性,并對27個變量指標進行主成分分析,將27個變量指標降到6個維度,從而消除各因素之間的冗余性。其次,基于神經(jīng)網(wǎng)絡的股票價格預測的相關研究,本文采用NARX神經(jīng)網(wǎng)絡對股指進行預測。本文構建了一個基于主成分分析(PCA)的NARX神經(jīng)網(wǎng)絡股指預測模型(PCA-NARX),并用Levenberg-Marquardt算法、Bayesian-Regularization算法、Scaled Conjugate Gradient算法三種算法對模型進行訓練。最后,利用PCA-NARX模型對滬深300指數(shù)數(shù)據(jù)進行驗證性測試和分析,且將PCA-NARX模型與NARX模型的研究結果進行對比分析。論文研究結果表明:不同的算法求解的效果存在差異性,但是整體對于模型求解的R值都大于0.85。另外,訓練的算法的復雜度越高其求解所需的時間越長,擬合效果越好。采用主成分降維后,網(wǎng)絡的訓練效率明顯提高了,模型的過擬合優(yōu)化得到降低,模型的泛化能力也得到改善。這驗證了采用新聞輿情結合宏觀經(jīng)濟指標,構建PCA-NARX模型對股市進行預測的方法是可靠有效的。
[Abstract]:The stock market plays an irreplaceable role in the financial market, and the general trend is the reaction of the stock market. The stock index is the trend of the stock supervision department on the reasonable regulation of the stock market and the regulatory basis, chaotic laws at the same time the trend of the stock investors can more correctly understand and grasp the fluctuation of stock market in our country, and it can be provide some practical basis for the major issue of portfolio. Therefore, how to accurately predict it has very important significance for the index. Because there are many factors affecting stock, large fluctuation, high noise and nonlinear characteristics of many factors, the traditional stock price forecasting methods cannot eliminate the high noise data. Therefore, in order to to improve the prediction accuracy, on the basis of previous studies, this paper has done the following research: This paper firstly summarizes the related factors affecting the trend of the index, on the basis of Last, this paper chooses 21 macroeconomic indicators and 6 indicators of public opinion, constitute the 27 variables, then studied these variables and the Shanghai and Shenzhen 300 index closing price and the correlation between the 27 variables for principal component analysis, 27 variables is reduced to 6 dimensions, thus eliminating redundancy among all the factors. Secondly, the related research on neural network prediction of stock price based on the NARX neural network to forecast the stock index. This paper constructs one based on principal component analysis (PCA) of the NARX neural network prediction model of stock index (PCA-NARX), and using Levenberg-Marquardt algorithm, Bayesian-Regularization algorithm, Scaled Conjugate algorithm Gradient three an algorithm for training the model. Finally, test and Analysis on the Shanghai and Shenzhen 300 index data using the PCA-NARX model, and the research results of PCA-NARX model and NARX model Were analyzed. The results indicate that the algorithm for solving the effect of different differences, but the overall model for solving the R value are higher than 0.85.. In addition, the required complexity of the training algorithm is higher for the longer, the better fit. Using principal components, network training efficiency improved, over fitting optimization reduced model, the generalization ability of the model is also improved. This confirmed the news public opinion combined with macroeconomic indicators, construction method of PCA-NARX model to predict the stock market is effective and reliable.

【學位授予單位】：重慶師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F832.51;TP183

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