本文選題：股市預測　切入點：BP算法　出處：《重慶大學》2014年碩士論文　論文類型：學位論文

【摘要】：股票市場在當今經(jīng)濟活動中起著舉足輕重的作用，是一個國家或地區(qū)經(jīng)濟和金融活動的“晴雨表”和“報警器”。研究股市走勢對于國家和股民都具有舉足輕重的意義。 股市是一個多因素影響、高度非線性的系統(tǒng)，神經(jīng)網(wǎng)絡能夠自動抽取數(shù)據(jù)集合中的非線性關系并進行模擬，因此常用于分析類似于股價預測的時間序列數(shù)據(jù)。本文對股票價格短期預測進行了理論與實證研究，建立了相應的BP神經(jīng)網(wǎng)絡，并針對其易陷入局部最優(yōu)值、收斂速度慢、預測精度不高等缺陷，進一步使用具有全局搜索優(yōu)化特點的遺傳算法對神經(jīng)網(wǎng)絡的初始權值和閥值進行了優(yōu)化，建立了GA-BP算法模型，并以長安股票進行了實證分析，并以MAPE誤差評價指標對兩種算法模型的預測精度進行了對比。實證結果表明GA-BP算法的預測精度較之BP網(wǎng)絡有了明顯的提高，但是隱層節(jié)點個數(shù)選擇等問題仍需要進一步的理論研究。
[Abstract]:The stock market plays an important role in the current economic activities and is a "barometer" and a "warning device" for the economic and financial activities of a country or region, and it is of great significance to study the trend of the stock market for both the country and the investors. The stock market is a multi-factor, highly nonlinear system. The neural network can automatically extract and simulate the nonlinear relations in the data set. Therefore, it is often used to analyze time series data similar to stock price prediction. In this paper, theoretical and empirical research on short-term stock price forecasting is carried out, and a corresponding BP neural network is established, which is easy to fall into local optimal value and converges slowly. Because the prediction accuracy is not high, the genetic algorithm with global search optimization is used to optimize the initial weight and threshold value of neural network, and the GA-BP algorithm model is established, and the empirical analysis is carried out with Chang'an stock. The prediction accuracy of the two algorithm models is compared with the MAPE error evaluation index. The empirical results show that the prediction accuracy of the GA-BP algorithm is significantly improved than that of the BP neural network. However, the selection of hidden layer nodes still needs further theoretical research.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F830.91;F224

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