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A Study on Short-term Prediction of Shanghai Composite Index Based on SVM

Author: LinLiMin
Tutor: WangXiaoYun
School: Hangzhou University of Electronic Science and Technology
Course: Management Science and Engineering
Keywords: Stock price prediction Support Vector Machine Technical Specifications Basic indicators
CLC: F830.91
Type: Master's thesis
Year: 2011
Downloads: 104
Quote: 0
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Abstract


The stock market is a complex nonlinear dynamic system, in order to ensure the interests of equity investors to grasp the law of the development of the stock market appears to be necessary in order to reduce the risk of stock investment, by predicting the stock market volatility. Many researchers at home and abroad through research, the establishment of effective securities price forecasting model. Time series model, ANN model, GA model according to its own characteristics, from different angles to solve various aspects of securities price forecasting, the prediction results of the theoretical and practical significance. However, these methods have their respective limitations, they are prone to over-learning in the forecasting process, due to learning, the problem of local minima and the curse of dimensionality. SVM model is better able to solve these problems, is widely applied to the field of securities forecast was, and achieved good results. First, the introduction of the background knowledge of the stock market as well as the existing stock market prediction, pointed out the inadequacies of the various forecasting methods. Then simple knowledge of statistical learning theory, the and final specific description of the support vector machine algorithm theoretical knowledge. Secondly, the support vector machine method for stock market prediction, support vector machine method for the basic flow of the stock market forecasts. Test process the Shanghai Composite Index as the object of study, the basic indicators as input variables, nine groups based on the closing price and trading volume of the different periods of design programs. The 9 comparative test, the test results indicators and to identify the most accurate program. Finally, to determine the validity of the time of the closing price and trading volume in the stock price prediction. Then, design support vector machine prediction model. Select the four categories of indicators as the input variable, group 2 programs designed according to the different stages of the process of data preprocessing. Program 10 correlation analysis after the rest of the technical indicators as input variables. Factor analysis, 10 input variables of the program to effectively reduce the dimension of the input variables to extract the principal components. Program 11 to the main component of factor analysis to extract, as the input variables. Finally, comparing the predicted results of the 11-group program. Concluded: SVM method to predict the course of the selection of input variables, the basic indicators than technical indicators to better improve the prediction accuracy; SVM method for forecasting process, basic indicators of the closing price and trading volume as a The input variables the effectiveness of three days.

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CLC: > Economic > Fiscal, monetary > Finance, banking > Finance, banking theory > Financial market > Securities market
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