Improved Accuracy of PSO and DE using Normalization: an Application to Stock Price Prediction

TL;DR

This paper enhances stock price prediction accuracy by applying normalization to improve PSO and DE algorithms, and introduces a DE-SVM hybrid model optimized for better parameter selection.

Contribution

It proposes a novel DE-SVM model for stock prediction and studies the impact of data normalization on algorithm accuracy.

Findings

Normalization improves prediction accuracy.

DE-SVM outperforms SVM and PSO-SVM.

Normalization significantly enhances model performance.

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 EvolutionSupport 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.