本文關(guān)鍵詞：利用BP神經(jīng)網(wǎng)絡(luò)系統(tǒng)對股票市場進(jìn)行預(yù)測與分析的研究　出處：《天津大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

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【摘要】：股票市場是一個充滿了機(jī)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(jì)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(fēng)險。在這種形勢下，越來越的投資人和投資機(jī)構(gòu)開始關(guān)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(shí)現(xiàn)對股票走勢的預(yù)測。在這種形勢下，對股票市場內(nèi)在規(guī)律的研究和預(yù)測具有著極其重要的理論意義和實(shí)用價值。 本文試圖通過利用BP（Back Propagation）神經(jīng)網(wǎng)絡(luò)進(jìn)行對股票的分析和預(yù)測。股市是一個非常復(fù)雜的非線性的動力學(xué)系統(tǒng)，而神經(jīng)網(wǎng)絡(luò)具有很強(qiáng)的非線性逼近能力和自學(xué)、自適應(yīng)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(luò)訓(xùn)練學(xué)習(xí)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(dá)到預(yù)測股票未來價格趨勢的目的。 本文分析了使用BP網(wǎng)絡(luò)對股市價格進(jìn)行預(yù)測分析中的原理，建立三層前饋神經(jīng)網(wǎng)絡(luò)建立對股票的預(yù)測模型。在實(shí)驗(yàn)中，通過對BP網(wǎng)絡(luò)參數(shù)的調(diào)整，以達(dá)到比較好的學(xué)習(xí)效果。再以五只納斯達(dá)克股票為例，應(yīng)用已經(jīng)實(shí)現(xiàn)的預(yù)測模型對其股價的未來走勢進(jìn)行預(yù)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(luò)的特點(diǎn)，可以證明可以使用BP神經(jīng)網(wǎng)絡(luò)對股票價格進(jìn)行有效預(yù)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(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP183;F832.51

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相關(guān)期刊論文 前1條

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