本文關(guān)鍵詞：決策樹及神經(jīng)網(wǎng)絡算法在股票分類預測中的應用　出處：《杭州電子科技大學》2014年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 數(shù)據(jù)挖掘 決策樹 神經(jīng)網(wǎng)絡 股票市場

【摘要】：股票市場作為市場經(jīng)濟的“晴雨表”反映著我國經(jīng)濟的總體狀況，在我國經(jīng)濟發(fā)展中起著重要的作用。隨著股票市場的發(fā)展，越來越多的人選擇投資股票。為了可以準確的選擇優(yōu)秀的上市公司進行投資，從中獲取可觀的收益，這就需要對股票市場上不同的上市公司的綜合經(jīng)營績效進行準確的分析預測。然而股票市場數(shù)據(jù)量龐大，是一個非常復雜的系統(tǒng)，利用傳統(tǒng)的方法很難對它做出準確的預測。數(shù)據(jù)挖掘技術(shù)是一個從海量的雜亂無章的數(shù)據(jù)中提取出隱含和潛在的對決策有價值的信息和模式的過程，它可以處理股票市場上規(guī)模巨大、繁瑣、雜亂無章的數(shù)據(jù)。本文利用數(shù)據(jù)挖掘技術(shù)中的C5.0決策樹、BP神經(jīng)網(wǎng)絡和RBF神經(jīng)網(wǎng)絡三種分類算法以上市公司的綜合經(jīng)營績效等級為分類標準進行分類預測。 本文選取2012年A股市場上200個上市公司為樣本，其中50個為A股市場上綜合績效最優(yōu)的股票，50個為綜合績效最差的股票，另外100個為隨機選取的綜合績效一般的股票，其中50個為上證股票，50個為深證股票。以股票的綜合績效等級為輸出變量，選取七大類14個有代表性的財務指標作為輸入變量，運用SPSS Clementine軟件，利用C5.0決策樹、BP神經(jīng)網(wǎng)絡和RBF神經(jīng)網(wǎng)絡三種分類算法分別建立分類預測模型。在建立模型時，隨機選取樣本集中80%的數(shù)據(jù)作為訓練樣本，用于模型的建立；選取樣本集中其余20%的數(shù)據(jù)作為測試樣本，用于模型有效性的檢測。模型建立之后，，對三種分類方法的預測準確率進行比較可知， C5.0決策樹算法得到的對測試樣本集的預測準確率最高，運用C5.0決策樹更具有參考意義。由三種分類方法給出的重要變量可知，每股收益增長率對上市公司的綜合經(jīng)營績效影響最大，現(xiàn)金流動負債比和流動比率對上市公司的綜合經(jīng)營績效影響也較大。利用三種分類預測模型對上市公司的綜合經(jīng)營績效進行分析，找出優(yōu)秀的上市公司財務指標所共有的特征，為投資者在股票的投資決策上提供幫助。
[Abstract]:The stock market as a market economy "barometer" reflects the overall situation of the economy of our country, plays an important role in the economic development of our country. With the development of the stock market, more and more people choose to invest in stocks. In order to accurately select the best listed companies to invest in, get considerable profit from this. You need on the stock market on different listed companies comprehensive performance analysis accurately predict the stock market. However, the huge amount of data, is a very complex system using traditional methods, it is very difficult to make accurate predictions. Data mining technology is an out of order from the mass data extraction process the implicit and potentially valuable information for decision-making and mode, it can be a huge scale, the stock market is complicated, out of order data. By using data mining technology. The three classification algorithms of C5.0 decision tree, BP neural network and RBF neural network are classified and forecasted according to the classification standard of listed companies' comprehensive management performance.
This paper selects 200 listed companies in 2012 A stock market as samples, of which 50 are A stock market performance optimal stock, 50 is the worst performance of the stock, the other 100 were randomly selected for the comprehensive performance of common shares, 50 of which the Shanghai stock, 50 for the Shenzhen stock. In order to grade comprehensive performance stock as the output variables, selected seven categories 14 representative financial index as input variables, the use of SPSS Clementine software, using C5.0 decision tree classification, prediction models are established BP neural network and RBF neural network three classification algorithms. In the models, randomly selected sample of 80% data as the training samples, used for model establishment; the remaining 20% of the sample concentration data as test samples for the detection of the effectiveness of the model. After the model, the prediction of three classification accuracy ratio Is the prediction of the test set of C5.0 decision tree algorithm is the highest accuracy, using C5.0 decision tree has the reference significance. It presents three important variables by the classification method, the greatest impact on the growth rate of earnings per share of listed companies comprehensive operating performance, influence of cash flow debt ratio and liquidity ratio of the performance of listed companies is also larger. On the listed company's comprehensive performance is analyzed by using the three classification prediction model, find out the financial index of listed company's outstanding common characteristics, help provide investors in the investment decision-making stock.

【學位授予單位】：杭州電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F832.51;TP183

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