本文關鍵詞：利用BP神經(jīng)網(wǎng)絡系統(tǒng)對股票市場進行預測與分析的研究　出處：《天津大學》2013年碩士論文　論文類型：學位論文

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【摘要】：股票市場是一個充滿了機遇與陷阱的地方。自從1990年起，股票公開在上海、深圳兩地發(fā)行以來，炒股票已經(jīng)成為國人日常經(jīng)濟行為的中的一部分。雖然股票的收益可以非常高，但是股票同樣具有著高風險。在這種形勢下，越來越的投資人和投資機構(gòu)開始關注于對于股市行情的走向，試圖通過股票背后大量的數(shù)據(jù)來實現(xiàn)對股票走勢的預測。在這種形勢下，對股票市場內(nèi)在規(guī)律的研究和預測具有著極其重要的理論意義和實用價值。 本文試圖通過利用BP（Back Propagation）神經(jīng)網(wǎng)絡進行對股票的分析和預測。股市是一個非常復雜的非線性的動力學系統(tǒng)，而神經(jīng)網(wǎng)絡具有很強的非線性逼近能力和自學、自適應等特性。通過整理股票價格歷史數(shù)據(jù)，并使BP神經(jīng)網(wǎng)絡訓練學習歷史數(shù)據(jù)，可以有效的找到股票市場價格變動的規(guī)律，，來達到預測股票未來價格趨勢的目的。 本文分析了使用BP網(wǎng)絡對股市價格進行預測分析中的原理，建立三層前饋神經(jīng)網(wǎng)絡建立對股票的預測模型。在實驗中，通過對BP網(wǎng)絡參數(shù)的調(diào)整，以達到比較好的學習效果。再以五只納斯達克股票為例，應用已經(jīng)實現(xiàn)的預測模型對其股價的未來走勢進行預測，取得了比較好的效果。通過過往的理論研究和BP神經(jīng)網(wǎng)絡的特點，可以證明可以使用BP神經(jīng)網(wǎng)絡對股票價格進行有效預測。
[Abstract]:The stock market is a place full of opportunities and pitfalls. Since 1990, stocks have been publicly issued in Shanghai and Shenzhen. Stock speculation has become a part of people's daily economic behavior. Although the return of stocks can be very high, but stocks also have high risk. In this situation. More and more investors and investment institutions are focusing on the direction of the stock market, trying to achieve the prediction of the stock market through a lot of data behind the stock. In this situation. The research and prediction of the inherent law of stock market is of great theoretical significance and practical value. This paper attempts to analyze and predict stocks by using BP(Back Propagation neural network. Stock market is a very complex nonlinear dynamic system. The neural network has strong ability of nonlinear approximation, self-learning, self-adaptation and so on. Through sorting out historical data of stock price, BP neural network is trained to learn historical data. We can find the law of stock market price change effectively to predict the stock price trend in the future. In this paper, the principle of using BP neural network to predict the stock price is analyzed, and the three-layer feedforward neural network is established to build the forecasting model of stock. In the experiment, the parameters of BP network are adjusted. In order to achieve a better learning effect. Then take five NASDAQ stocks as an example, using the realized prediction model to predict the future trend of its stock price. Through the past theoretical research and the characteristics of BP neural network, it can be proved that the BP neural network can be used to predict the stock price effectively.
【學位授予單位】：天津大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP183;F832.51

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相關期刊論文 前1條

1 徐迪,馬大軍,李元熹;神經(jīng)元網(wǎng)絡在股價預測中的應用[J];系統(tǒng)工程理論與實踐;1998年11期

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