本文關(guān)鍵詞： 股價預(yù)測 bp神經(jīng)網(wǎng)絡(luò) 粒子群算法 股票的可預(yù)測性　出處：《復(fù)旦大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著中國股票市場的發(fā)展,股票市場的投資活動逐漸變得頻繁,股票市場逐漸成為證券市場中最活躍的市場,股票成為投資者們最熱衷的投資產(chǎn)品。所以股票價格的預(yù)測成為了一項熱門研究。有效的預(yù)測分析方法可以很好的幫助投資者制定投資策略,在增加收益的同時降低風(fēng)險。股票市場是一個非常復(fù)雜的系統(tǒng),但是它的內(nèi)在規(guī)律具有一定的趨勢性,而且受到經(jīng)濟(jì)政治等許多因素的影響,但就是如此股票市場這個系統(tǒng)的運(yùn)動規(guī)律仍然是很難掌握的。許多的學(xué)者對股票市場進(jìn)行研究,并且產(chǎn)生了許多的方法模型。傳統(tǒng)的研究方法主要是基于數(shù)理統(tǒng)計理論的模型,先建立主觀的數(shù)據(jù)序列模型,再對模型進(jìn)行預(yù)測和研究,像時間序列等等模型在這方面有很多的應(yīng)用,但是其預(yù)測精度無法達(dá)到人們的要求,事實上人們在研究中逐漸發(fā)現(xiàn)股票市場系統(tǒng)是一個復(fù)雜的非線性系統(tǒng),傳統(tǒng)的線性模型無法很好的逼近其內(nèi)在規(guī)律,許多的學(xué)者開始研究股票的混沌性質(zhì),而且隨著非線性算法的發(fā)展,許多學(xué)者開始使用神經(jīng)網(wǎng)絡(luò),遺傳算法等非線性算法對股票是場進(jìn)行預(yù)測,各種基于非線性算法的股票預(yù)測模型被建立。 本文將粒子群優(yōu)化算法和bp神經(jīng)網(wǎng)絡(luò)進(jìn)行了融合,利用粒子群算法對神經(jīng)網(wǎng)絡(luò)的連接權(quán)重和閾值的訓(xùn)練進(jìn)行優(yōu)化,討論了各個參數(shù)的選取設(shè)定優(yōu)化,建立了粒子群優(yōu)化bp神經(jīng)網(wǎng)絡(luò)模型并將其用于股票預(yù)測的實證研究。通過對三種具有市場代表性的指數(shù)進(jìn)行實證分析,以及將粒子群優(yōu)化神經(jīng)網(wǎng)絡(luò)的預(yù)測效果與傳統(tǒng)的單一bp神經(jīng)網(wǎng)絡(luò)預(yù)測效果進(jìn)行對比分析,得到的結(jié)果表明粒子群算法能夠有效的加強(qiáng)神經(jīng)網(wǎng)絡(luò)的預(yù)測能力,減小預(yù)測誤差,提高訓(xùn)練速度；三大市場的預(yù)測結(jié)果都比較理想,說明了股票市場的可預(yù)測性；美國股票市場相對其他兩種市場具有更強(qiáng)的預(yù)測性,規(guī)律性更強(qiáng)。
[Abstract]:With the development of China's stock market, the investment activities of the stock market become more and more frequent, and the stock market gradually becomes the most active market in the stock market. Stocks have become the most popular investment products for investors. Therefore, the prediction of stock prices has become a hot research. Effective forecasting and analysis methods can help investors to make investment strategies. Stock market is a very complex system, but its inherent law has certain tendency, and is influenced by many factors such as economy and politics. However, it is still very difficult to master the movement law of the stock market system. Many scholars study the stock market. Traditional research methods are mainly based on mathematical statistics theory model. First, the subjective data sequence model is established, then the model is predicted and studied. Models such as time series have many applications in this field, but their prediction accuracy can not meet the requirements of people. In fact, people have gradually found that the stock market system is a complex nonlinear system. The traditional linear model can not approach its inherent law very well, many scholars begin to study the chaos property of stock, and with the development of nonlinear algorithm, many scholars begin to use neural network. The nonlinear algorithm such as genetic algorithm is used to predict the stock field, and a variety of stock prediction models based on nonlinear algorithm are established. In this paper, particle swarm optimization algorithm and BP neural network are fused, the training of connection weight and threshold of neural network is optimized by particle swarm optimization, and the selection and optimization of each parameter are discussed. The BP neural network model of particle swarm optimization is established and applied to the empirical research of stock forecasting. Through the empirical analysis of three representative indices of market. The prediction effect of PSO neural network is compared with that of traditional BP neural network. The results show that PSO can effectively enhance the prediction ability of neural network. Reduce the prediction error and improve the training speed; The forecast results of the three major markets are all satisfactory, which shows the predictability of the stock market; The US stock market is more predictable and more regular than the other two markets.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP18;F832.51

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