本文關鍵詞：基于深度學習的股票價格趨勢預測方法研究　出處：《云南財經大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 深度學習 受限的玻爾茲曼機 股票價格預測 CD算法

【摘要】：當今股票市場不僅為優(yōu)秀掛牌企業(yè)提供融資,同時讓一些有投資意識的股民提供資金出路。從而使得社會資源得到更好的配置和宏觀經濟得以調控,然而由于股市的不確定性,每個投資人對股市認知的異同性、技術分析的復雜性等因素,使得廣大股民投資的回報率達不到預期,有的甚至血本無歸。所以一直以來股票市場都被無論是政府、企業(yè)還是投資者所高度關注。股票價格趨勢的預測更是股票研究中的熱點。眾所周知,由于股市的波動具有極強的非線性、高噪聲等特點,所以對股票價格趨勢預測極其困難,傳統(tǒng)股票預測方法往往收效甚微。因此如何建立新的股票價格趨勢預測的模型來提高預測的準確度,從而幫助金融投資者有效規(guī)避風險,投資獲利最大化,具有重要的理論意義和應用價值。本文首先闡述了傳統(tǒng)股票預測方法,大體分為:基本分析法主要是從宏觀微觀經濟、相關公司的財務報表和現(xiàn)金流等信息角度,通過相對估值和折現(xiàn)估值等等,對該股票的內在價值進行估值。不足之處:信息不對等性及相關掛牌公司披露信息延時性、準確性等導致估值困難。大盤分析法主要是依據統(tǒng)計圖表,如K線圖,其形態(tài)可分為整理形態(tài)和趨向線等,根據對其特定的形態(tài)來判斷股市的未來動向。不足之處:此類分析方法繁多,且各個投資人判斷習慣不同,方法之間存在巨大差別。統(tǒng)計學分析法主要是采用最小二乘構建各種回歸,例如混合回歸模型、自回歸模型等進行股票價格趨勢預測,此類模型的預測預測準確率較前兩類預測方法要高。不足之處:這些回歸模型通常假設前提太多,且對非線性強的問題處理能力,而股票價格趨勢的預測問題影響因素眾多且非線性極強�；谌斯ど窠浘W絡的預測模型具有高度自組織、自調整和自學習的能力、是一個復雜度極高的非線性系統(tǒng),模型預測結果通常也要優(yōu)于上述傳統(tǒng)方法。不足之處:基于神經網絡的股票預測模型容易陷入局部最小值的問題,且多層神經網絡在對復雜事物的描述時,往往要增多隱含層的層數(shù),這樣會導致梯度擴散的問題,從而影響準確率。本文正是從基于人工神經網絡預測模型的缺點,如梯度擴散和局部最小值等問題,從而提出了采用受限的玻爾茲曼機模型構建基于深度學習的股票價格趨勢預測模型。深度學習是基于神經網絡基礎上發(fā)展而來,不僅繼承了神經網絡方法的優(yōu)點,而且很好的克服了神經網絡方法的不足之處。本文預測模型采用受限的玻爾茲曼機來構建深度置信網絡,學習方法是采用K步吉布斯采樣后,結合對比散度算法,來訓練整個深度置信網絡。最后利用收集的格力空調的股票價格信息來訓練本文預測模型并對本文模型預測準確率進行了檢驗。選用基于BP神經網絡的股票價格預測模型作為本文預測模型的對比模型,并用采用實例貴州茅臺和比亞迪的股票價格信息來檢驗兩個模型的預測準確率,實驗結果表明:基于深度學習的股票價格趨勢預測模型效果良好,且準確率要優(yōu)于BP神經網絡預測模型。本文創(chuàng)新點:(1)本文采用了基于受限的玻爾茲曼機構建深度置信網絡的股票價格趨勢預測模型,學習方法采用了經過K步的吉布斯采樣后的對比散度算法(CD算法)來訓練預測模型。最后給出實例驗證。(2)將本文預測模型與基于BP神經網絡股票價格趨勢預測模型的預測準確率進行了實例比較。
[Abstract]:The stock market not only provides financing for outstanding listed companies, while some investment minded investors to provide funds to make way. A better allocation of social resources and macroeconomic regulation to, however due to the uncertainty in the stock market, stock market investors on the similarities and differences of each cognitive complexity, factors such as technical analysis, making the stock investment the rate of return is not up to expectations, some even lose everything. So since the stock market has been highly concerned by both the government, enterprises and investors. The stock price trend forecast is a hot stock research. As everyone knows, because of the volatility of the stock market has a very strong nonlinear, high noise characteristics, so the stock price trend prediction is extremely difficult, the traditional stock forecasting methods often have little effect. So how to establish a new prediction model of stock price trend To improve the prediction accuracy, so as to help investors to avoid financial risk effectively, investment profit maximization, and has important theoretical significance and application value. This paper describes the traditional prediction methods of stock, can be divided into: basic analysis mainly from the macro and micro economy, financial statements and cash flow information related to the company's point of view. Through the relative valuation and valuation discount and so on, the intrinsic value of the stock valuation. Disadvantages: information asymmetry and listed company information disclosure delay, as a result of the valuation accuracy difficult. Large disk analysis method is mainly based on the statistical charts, such as the K map, its shape can be divided into the consolidation pattern and the trend line. According to the future trends of its specific form to determine the stock market. The shortcomings of such analysis methods are various, and each investor to judge different habits, there is a huge difference between the methods. The statistical analysis method is mainly constructed by least squares regression, such as mixed regression model, auto regression model to forecast the stock price trend forecast of this kind of model accuracy than prediction method to high. The first two shortcomings: the regression model is usually premise too much, and the ability to deal with the nonlinear problems. The factors affecting the prediction of stock price trend of large and highly nonlinear. The prediction model based on artificial neural network has a high degree of self-organization, self adjustment and self-learning ability, is a very complex nonlinear system, the prediction results are better than the traditional method. Disadvantages: neural network stock forecasting the model is easy to fall into the local minimum problem based on multilayer neural network and the complicated description of things, tend to increase the number of hidden layer, it will Lead to gradient diffusion problems, thus affecting the accuracy. This article is from the disadvantages of prediction model based on artificial neural network, such as the gradient diffusion and local minimum problem, and put forward the construction of deep learning of stock price trend forecast model based on the Boltzmann machine model is limited. Deep learning is developed based on neural network based on not only inherits the advantages of neural network method, and good overcomes the defects of the neural network method. In this paper, a prediction model based on Boltzmann machine limited to construct the deep belief network learning method is the use of K step of Gibbs sampling, comparing with the divergence algorithm, to train the entire depth of belief network. Finally the collection the GREE stock price information to train the prediction model and the prediction accuracy of this model is tested based on BP by God. The stock price prediction model as the prediction model comparison model, and by using examples Kweichow Moutai and BYD's stock price information to test the prediction accuracy of the model is two, the experimental results show that the deep learning of stock price trend forecast model based on good effect, and the accuracy is better than BP neural network prediction model. The innovation of this paper: (1) the prediction model of Boltzmann limited mechanism built deep belief networks of stock price trend based on learning method is adopted after comparing K step of Gibbs sampling after the divergence algorithm (CD algorithm) to train the prediction model. Finally, an instance is given. (2) the prediction model with the prediction of BP neural network prediction model of stock price trend based on the accuracy of the comparison.

【學位授予單位】：云南財經大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F832.51;TP18

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