Đang chuẩn bị liên kết để tải về tài liệu:
Stock price forecasting using support vector machines and improved particle swarm optimization

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. | Journal of Automation and Control Engineering, Vol. 1, No. 2, June 2013 Stock Price Forecasting using Support Vector Machines and Improved Particle Swarm Optimization M. Karazmodeh, S. Nasiri, and S. Majid Hashemi Eastern Mediterranean University /Banking and Finance, Famagusta, North Cyprus, Turkey Email: {mahdi.karazmudeh, sinanasiri.sbu, majid.hashemi.86}@gmail.com 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. Index Terms—Particle Swarm Optimization, Support Vector Machines, Stock Market forecasting, IPSOSVM, PSOSVM, Intelligent Algorithms I. INTRODUCTION The process of making assumptions of future Changes based on existing data is Forecasting. The more accurate the forecasting, the more it could be helpful to make decisions for future. Empowering the managers in all businesses to modify current situation in order to achieve the favorable results in future is the key use of forecasting. Forecasting stock price has always been a serious issue in financial fields. Stock market prediction is regarded as a challenging task in financial time-series forecasting because of the fact that stock market indices are essentially dynamic, nonlinear, complicated, nonparametric, and chaotic in nature [1]. Stock market forecasters focus on developing approaches to successfully forecast/predict index values or stock prices, aiming at high profits using well defined .