Abstract
Data Mining is being actively applied to stock market since 1980s. It has been used to predict stock prices, stock indexes, for portfolio management, trend detection and for developing recommender systems. The various algorithms which have been used for the same include ANN, SVM, ARIMA, GARCH etc. Different hybrid models have been developed by combining these algorithms with other algorithms like roughest, fuzzy logic, GA, PSO, DE, ACO etc. to improve the efficiency. This paper proposes DE-SVM model (Differential Evolution- Support vector Machine) for stock price prediction. DE has been used to select best free parameters combination for SVM to improve results. The paper also compares the results of prediction with the outputs of SVM alone and PSO-SVM model (Particle Swarm Optimization). The effect of normalization of data on the accuracy of prediction has also been studied.
Highlights
Stock Market prediction is an attractive field for research due to its commercial applications and the attractive benefits it offers
This paper proposes a hybrid of DE-SVM (Differential Evolution-Support Vector Machines)
The performance of SVM can be significantly affected by choice of its free parameters of cost (C), insensitive loss function (ε) and kernel parameter (γ)
Summary
Stock Market prediction is an attractive field for research due to its commercial applications and the attractive benefits it offers. It follows stochastic, non-parametric and nonlinear behavior. This paper proposes a hybrid of DE-SVM (Differential Evolution-Support Vector Machines). DE-SVM has already been used by Zhonghai Chen et al [6] for air conditioning load prediction, Yong Sun et al [7] for gas load prediction, Jośe Garćıa-Nieto et al [8] for feature selection, Shu Jun et al [9] for rainstorm forecasting and for studying the lithology identification method from well logs by Jiang An-nan et al [10]. The effect of normalization on datasets has been studied
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