本文選題：股市預(yù)測 + 神經(jīng)網(wǎng)絡(luò)��； 參考：《蘭州大學(xué)》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(guān)。股市的預(yù)測研究具有很強(qiáng)的理論和實(shí)際意義。傳統(tǒng)的預(yù)測方法一般是對股市進(jìn)行定性和長時間范圍內(nèi)的預(yù)測,存在較大局限性�，F(xiàn)在,以神經(jīng)網(wǎng)絡(luò)為代表的智能方法,由于良好的學(xué)習(xí)能力、容錯性等特點(diǎn),成為股市預(yù)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(luò)的方法進(jìn)行了介紹�；谏窠�(jīng)網(wǎng)絡(luò)存在的一些缺點(diǎn),研究了利用遺傳算法對神經(jīng)網(wǎng)絡(luò)的權(quán)值和閾值進(jìn)行優(yōu)化,以提高預(yù)測的速度和精度。采用上證50指數(shù)進(jìn)行了實(shí)證分析。把指數(shù)價(jià)格前一天的收盤價(jià)和當(dāng)天的開盤價(jià)作為輸入樣本,預(yù)測當(dāng)天的收盤價(jià)。結(jié)果表明,神經(jīng)網(wǎng)絡(luò)經(jīng)過遺傳算法優(yōu)化后,預(yù)測結(jié)果比原先單純使用神經(jīng)網(wǎng)絡(luò)方法有所提高,結(jié)果令人滿意。 但是,輸入量的選擇是否合理、神經(jīng)網(wǎng)絡(luò)和遺傳算法中參數(shù)確定并未有明確理論指導(dǎo)等問題依然有待解決,這些也都是在運(yùn)用智能方法進(jìn)行股市預(yù)測中值得進(jìn)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(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP18;F830.91

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本文編號：1825403