本文選題：股市預測 + 神經(jīng)網(wǎng)絡　； 參考：《蘭州大學》2013年碩士論文

【摘要】：股票交易市場的波動與投資者息息相關。股市的預測研究具有很強的理論和實際意義。傳統(tǒng)的預測方法一般是對股市進行定性和長時間范圍內(nèi)的預測,存在較大局限性。現(xiàn)在,以神經(jīng)網(wǎng)絡為代表的智能方法,由于良好的學習能力、容錯性等特點,成為股市預測中較為成熟和使用較廣的一種方法。 本文即在此背景下,對神經(jīng)網(wǎng)絡的方法進行了介紹�；谏窠�(jīng)網(wǎng)絡存在的一些缺點,研究了利用遺傳算法對神經(jīng)網(wǎng)絡的權值和閾值進行優(yōu)化,以提高預測的速度和精度。采用上證50指數(shù)進行了實證分析。把指數(shù)價格前一天的收盤價和當天的開盤價作為輸入樣本,預測當天的收盤價。結果表明,神經(jīng)網(wǎng)絡經(jīng)過遺傳算法優(yōu)化后,預測結果比原先單純使用神經(jīng)網(wǎng)絡方法有所提高,結果令人滿意。 但是,輸入量的選擇是否合理、神經(jīng)網(wǎng)絡和遺傳算法中參數(shù)確定并未有明確理論指導等問題依然有待解決,這些也都是在運用智能方法進行股市預測中值得進一步探討的問題。
[Abstract]:The volatility of the stock market is closely related to investors. The research of stock market prediction has strong theoretical and practical significance. The traditional forecasting method is to predict the stock market qualitatively and within a long period of time, which has some limitations. Now the intelligent method represented by neural network has become a mature and widely used method in stock market forecasting because of its good learning ability and fault tolerance. In this context, the method of neural network is introduced in this paper. Based on the shortcomings of neural networks, the genetic algorithm is used to optimize the weights and thresholds of neural networks in order to improve the speed and accuracy of prediction. Using the Shanghai Stock Exchange 50 index for empirical analysis. The closing price of the previous day and the opening price of the day were used as input samples to forecast the closing price of the day. The results show that the prediction results of the neural network are better than those of the original neural network method after genetic algorithm optimization, and the results are satisfactory. However, whether the selection of input is reasonable or not, and whether the parameters in neural network and genetic algorithm are not clear theoretical guidance are still to be solved, which are also worthy of further discussion in the use of intelligent methods for stock market prediction.
【學位授予單位】：蘭州大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP18;F830.91

【參考文獻】

相關期刊論文 前1條

1 段馬威;商潔;;遺傳算法在音頻去噪中的應用[J];電子設計工程;2011年07期

相關碩士學位論文 前5條

1 趙程;基于遺傳神經(jīng)網(wǎng)絡的股市預測[D];北京工業(yè)大學;2003年

2 劉莉華;神經(jīng)網(wǎng)絡方法在股市預測中的應用研究[D];電子科技大學;2005年

3 朱磊;基于BP神經(jīng)網(wǎng)絡的軟件可靠性模型選擇研究[D];重慶大學;2006年

4 王莎;BP神經(jīng)網(wǎng)絡在股票預測中的應用研究[D];中南大學;2008年

5 李艷;快速公交調(diào)度算法與研究[D];東北大學;2008年

，

本文編號：1825403