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The Analysis And Application Of Stock Price Forecasting Based On Support Vector Regression

Posted on:2008-04-27Degree:MasterType:Thesis
Country:ChinaCandidate:Z G ShenFull Text:PDF
GTID:2189360215995589Subject:Computer application technology
Abstract/Summary:PDF Full Text Request
Stock investment has become an important part of people's daily life. Forecasting the stock price has been a concern problem. Stock market is a complicated non-linear dynamic system. It is very difficult to open out its inherent rules using traditional timing prediction technique. To improve the analysis of the pattern of the stock market price, based on the theory of support vector machines, we put forward a new method for forecasting stock price.Because Support Vector Machine uses the structural risk minimization principle, the risk is only influenced with the number of input samples, but has nothing to do with the input dimension. Support Vector Machine avoids "dimension disaster" and overcomes some shortcomings, such as the slow convergence of traditional neural networks, local minimum value and so on. So, Support Vector Machine has good generalization ability. Based on this method, we develop a prototype system of support vector regression.We use this system to predict the stock price. The result is generally satisfactory. We also discuss the different kernel functions and the different parameters to impact the results of forecasts. So based on Support Vector Machines for predicting the stock price have important reference value.
Keywords/Search Tags:Support Vector Regression, kernel function, forecast stock price, parameter selection
PDF Full Text Request
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