Stock Price Forecasting using Support Vector Machines and Improved Particle Swarm Optimization

Abstract

The present paper employs an Particle Swarm Optimization (PSO) Improved via Genetic Algorithm (IPSO) based on Support Vector Machines (SVM) for efficient prediction of various stock indices. The main difference between PSO and IPSO is shown in a graph. Different indicators from the technical analysis field of study are used as input features. To forecast the price of a stock, the correlation between stock prices of different companies has been used. It is in general observed that the proposed model is computationally more efficient, prediction wise more accurate and more robust against other researches done by standard PSOSVM based model. 