Previous research shows strong evidence that traditional regression- based predictive models face significant challenges in predictability tests due to uncertain models and unstable parameters. Recent studies introduce new, stable strategies to overcome these problems. Support Vector Clustering is a relatively new learning algorithm that has the desirable characteristics of the control of the decision function, the use of the kernel method, and the sparsity of the solution. In this paper, we present a theoretical and empirical framework to apply the Support Vector Machines strategy to predict the stock market. There are many factors like macro and microeconomic events that may influence the stock trend. For predicting the stock performance, Support Vector Machine is used to analyze the relationship between these factors. Our results suggest that support vector clustering is a powerful predictive tool for stock predictions in the financial market.
Stock prediction, predictive models, predictive algorithms and training data
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