Forecasting Stock Exchange Index Using Particle Swarm Optimization Comparing to Traditional Models

The stock market is one of the most attractive investment choice from which a large amount of profit can be earned. This study presents a PSO-based methodology to deal with Stock market index prediction. The study showed superiority in applicability of the proposed approach by using Tehran Stock Exc...

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Autores principales:	Darush Damoori, Darush Farid, Morteza Ashhar
Formato:	article
Lenguaje:	FA
Publicado:	Shahid Bahonar University of Kerman 2011
Materias:	particle swarm optimization algorithm simple exponential smoothing hoelt-winters exponential smoothing auto regressive moving average auto regressive integrated moving average Accounting. Bookkeeping HF5601-5689
Acceso en línea:	https://doaj.org/article/eedd34e3472e4ac985e50ad35f1461d6
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spelling	oai:doaj.org-article:eedd34e3472e4ac985e50ad35f1461d62021-11-04T19:41:36ZForecasting Stock Exchange Index Using Particle Swarm Optimization Comparing to Traditional Models2008-89142476-292X10.22103/jak.2011.24https://doaj.org/article/eedd34e3472e4ac985e50ad35f1461d62011-08-01T00:00:00Zhttps://jak.uk.ac.ir/article_24_1b4935f376492e53a3157b5a00649074.pdfhttps://doaj.org/toc/2008-8914https://doaj.org/toc/2476-292XThe stock market is one of the most attractive investment choice from which a large amount of profit can be earned. This study presents a PSO-based methodology to deal with Stock market index prediction. The study showed superiority in applicability of the proposed approach by using Tehran Stock Exchange Index (TSEI) and comparing the outcomes with conventional method such as Simple Exponential Smoothing (SES), Hoelt-Winters Exponential Smoothing (HWES), Auto Regressive (AR), Moving Average (MA), Auto Regressive Integrated Moving Average (ARIMA). Experimental results clearly showed that PSO approach meaningfully outperforms all of the conventional method in terms of MAD, MSE, RMSE and MAPE. Additionally, evaluation statistics of the proposed approach significantly decrees variance of the errors compared to the conventional method.Darush DamooriDarush FaridMorteza AshharShahid Bahonar University of Kermanarticleparticle swarm optimization algorithmsimple exponential smoothinghoelt-winters exponential smoothingauto regressivemoving averageauto regressive integrated moving averageAccounting. BookkeepingHF5601-5689FAمجله دانش حسابداری, Vol 2, Iss 5, Pp 7-30 (2011)
institution	DOAJ
collection	DOAJ
language	FA
topic	particle swarm optimization algorithm simple exponential smoothing hoelt-winters exponential smoothing auto regressive moving average auto regressive integrated moving average Accounting. Bookkeeping HF5601-5689
spellingShingle	particle swarm optimization algorithm simple exponential smoothing hoelt-winters exponential smoothing auto regressive moving average auto regressive integrated moving average Accounting. Bookkeeping HF5601-5689 Darush Damoori Darush Farid Morteza Ashhar Forecasting Stock Exchange Index Using Particle Swarm Optimization Comparing to Traditional Models
description	The stock market is one of the most attractive investment choice from which a large amount of profit can be earned. This study presents a PSO-based methodology to deal with Stock market index prediction. The study showed superiority in applicability of the proposed approach by using Tehran Stock Exchange Index (TSEI) and comparing the outcomes with conventional method such as Simple Exponential Smoothing (SES), Hoelt-Winters Exponential Smoothing (HWES), Auto Regressive (AR), Moving Average (MA), Auto Regressive Integrated Moving Average (ARIMA). Experimental results clearly showed that PSO approach meaningfully outperforms all of the conventional method in terms of MAD, MSE, RMSE and MAPE. Additionally, evaluation statistics of the proposed approach significantly decrees variance of the errors compared to the conventional method.
format	article
author	Darush Damoori Darush Farid Morteza Ashhar
author_facet	Darush Damoori Darush Farid Morteza Ashhar
author_sort	Darush Damoori
title	Forecasting Stock Exchange Index Using Particle Swarm Optimization Comparing to Traditional Models
title_short	Forecasting Stock Exchange Index Using Particle Swarm Optimization Comparing to Traditional Models
title_full	Forecasting Stock Exchange Index Using Particle Swarm Optimization Comparing to Traditional Models
title_fullStr	Forecasting Stock Exchange Index Using Particle Swarm Optimization Comparing to Traditional Models
title_full_unstemmed	Forecasting Stock Exchange Index Using Particle Swarm Optimization Comparing to Traditional Models
title_sort	forecasting stock exchange index using particle swarm optimization comparing to traditional models
publisher	Shahid Bahonar University of Kerman
publishDate	2011
url	https://doaj.org/article/eedd34e3472e4ac985e50ad35f1461d6
work_keys_str_mv	AT darushdamoori forecastingstockexchangeindexusingparticleswarmoptimizationcomparingtotraditionalmodels AT darushfarid forecastingstockexchangeindexusingparticleswarmoptimizationcomparingtotraditionalmodels AT mortezaashhar forecastingstockexchangeindexusingparticleswarmoptimizationcomparingtotraditionalmodels
_version_	1718444685660258304

Forecasting Stock Exchange Index Using Particle Swarm Optimization Comparing to Traditional Models

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