Stock Market Index Prediction via Hybrid Inertia Factor PSO and Constriction Coefficient PSO

Morteza Ashhar () and Amin Rostami Motamed

International Journal of Academic Research in Accounting, Finance and Management Sciences, 2014, vol. 4, issue 3, 144-154

Abstract: Conventional statistical techniques for forecasting are constrained by the underlying seasonality, non-stationary and other factors. Increasingly over the past decade, Artificial intelligence (AI) methods including Artificial Neural network (ANN), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) etc. have been used successfully to perform predictions in financial markets and other areas. This study presents a hybrid inertia factor and constriction coefficient PSO-based methodology to deal with the Stock market index problem. We will demonstrate the superiority an applicability of the proposed approach by using Tehran Stock Exchange Index (TSEI) data and comparing the outcomes with other PSO methods such as: standard PSO, Inertia Factor PSO and Constriction Coefficient PSO. Experimental results clearly show that a hybrid PSO approach meaningfully outperforms all of the other PSO methods in terms of MAD, MSE, RMSE and MAPE Evaluation statistics also, the proposed approach can be considered as a suitable AI model for stock market index forecasting problem.

Keywords: Stock market forecasting; Hybrid PSO (hybrid intelligence model); Standard PSO; Inertia Factor PSO; Constriction Coefficient PSO (search for similar items in EconPapers)
Date: 2014
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hrmars.com/hrmars_papers/Article_15_Stock_M ... tion_via_Hybrid1.pdf (application/pdf)
http://hrmars.com/hrmars_papers/Article_15_Stock_M ... tion_via_Hybrid1.pdf (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hur:ijaraf:v:4:y:2014:i:3:p:144-154

Access Statistics for this article

More articles in International Journal of Academic Research in Accounting, Finance and Management Sciences from Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences
Bibliographic data for series maintained by Hassan Danial Aslam ().