發(fā)布時間：2018-02-21 03:35

  本文關(guān)鍵詞： 股市預(yù)測 灰色理論 馬爾柯夫過程 BP神經(jīng)網(wǎng)絡(luò) 遺傳算法 組合模型　出處：《華中科技大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：股票已成為金融市場中不可缺少的組成部分，股票價格跌宕起伏，股票市場風(fēng)云變幻，不僅受內(nèi)部規(guī)律的影響，而且還受到外部環(huán)境像政治、經(jīng)濟等因素的影響，但投資者為了獲取豐厚的收益，還是希望能夠準(zhǔn)確分析和預(yù)測股票價格。那么如何建立一個比較理想的預(yù)測模型，是眾多學(xué)者一直研究的內(nèi)容。 本文選取了兩種組合模型分別對上證綜合指數(shù)進(jìn)行分析、預(yù)測和比較，兩種組合方法分別為灰色-馬爾柯夫模型和遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)模型。首先，建立了灰色系統(tǒng)模型GM(1,1)，它所描述的是灰色變量，比較適合于數(shù)據(jù)少、短期的、波動小的系統(tǒng)對象，而對于波動性比較大的數(shù)列，擬合度比較差，預(yù)測精度不夠理想。針對灰色模型這一缺點引入了馬爾柯夫過程，然后運用組合方法灰色-馬爾柯夫模型對上證指數(shù)進(jìn)行預(yù)測；其次，通過對BP神經(jīng)網(wǎng)絡(luò)的建模與分析，發(fā)現(xiàn)它在局部極小點和收斂速度上存在一些問題，并引入了遺傳算法的分析和研究，建立了第二種組合模型基于遺傳算法的BP神經(jīng)網(wǎng)絡(luò)，并對單一的BP神經(jīng)網(wǎng)絡(luò)作了比較，得出組合模型預(yù)測效果更好的結(jié)論；最后，，通過兩種組合方法的比較，來分析出它們各自所呈現(xiàn)的數(shù)據(jù)特征，文章的最后又對數(shù)據(jù)波動比較大的上證指數(shù)進(jìn)行了預(yù)測。 實驗結(jié)果表明：兩種組合方法在數(shù)據(jù)平穩(wěn)和數(shù)據(jù)波動的情況下，從預(yù)測相對誤差上來衡量，GM(1,N)-Markov模型整體上要比GA-BP模型的預(yù)測精度高些。
[Abstract]:The stock market has become an indispensable part of the financial market. The stock price has fluctuated and the stock market has changed. The stock market is not only affected by internal laws, but also by external factors such as politics and economy. However, in order to obtain rich profits, investors still hope to accurately analyze and predict the stock price, so how to establish an ideal forecasting model is the content that many scholars have been studying all the time. In this paper, two combination models are selected to analyze, predict and compare the composite index of Shanghai Stock Exchange. The two combination methods are gray Markov model and genetic algorithm optimized BP neural network model. In this paper, a grey system model, GM1 / 1, is established, which describes grey variable, which is suitable for the system object with less data, short term and low fluctuation, but the fitting degree is poor for the series with high volatility. Aiming at the shortcoming of grey model, the Markov process is introduced, and then the combination method is used to predict the index of Shanghai Stock Exchange. Secondly, through the modeling and analysis of BP neural network, It is found that there are some problems in the local minima and convergence rate, and the analysis and research of genetic algorithm are introduced. The second combinatorial model BP neural network based on genetic algorithm is established, and the single BP neural network is compared. Finally, through the comparison of the two combination methods to analyze their respective data characteristics, the last part of the paper forecasts the Shanghai Stock Exchange Index which has a large data fluctuation. The experimental results show that the prediction accuracy of the two combined methods is higher than that of the GA-BP model on the whole, when the data is stable and fluctuating.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F832.51;N945.12

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