Stock Market Prediction Using Analytic Hierarchy Process and Support Vector Machine

Prediction of the stock market behavior has been a research topic for decades. Because it is a challenging subject both in terms of the choice of the prediction model and in terms of constructing the set of features that model will use for forecasting. In this thesis, a novel feature ranking and feature selection approach incorporation with weighted kernel least squares support vector machines (LS-SVMs) were used. We introduce the analytic hierarchy process (AHP) into the stock market and then evaluate criteria which provide the prediction model with relevant knowledge of the underlying processes of the studied stock market. The feature weights obtained by the AHP method are applied for feature ranking and selection and used with the LS-SVMs through a weighted kernel. The experimental results specify that the new model outperforms the benchmark models. Furthermore, the set of feature weights obtained by the new approach can also independently be incorporated into other kernel-based learners. Keywords: stock market prediction, analytic hierarchy process, support vector machine, least squares support vector machines, weighted kernel.