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Predicting stock output of Banks listed in Tehran Stock Exchange using financial ratios with neural network approach

Academic Journal of Accounting and Economic Researches

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Title Predicting stock output of Banks listed in Tehran Stock Exchange using financial ratios with neural network approach
 
Creator Mandegari, Parastoo
 
Description  Timely prediction of stock output can help managers and investors to make better decisions. Nowadays, the use of innovative approaches to artificial intelligence in modeling and financial forecasting has become commonplace.Banking industry, with 17 companies in Tehran Stock Exchange and OTC, is one of the groups paid attention by investors and especially risk averse people who except appropriate profit sharing and moderate rising slope of stock. This study aimed to investigate the prediction of stock output of Banks listed in the Tehran Stock Exchange using 7 financial ratios in the span of 2009 to 2015 with two approaches of Artificial Neural Networks (ANNS) and Support Victor Regression (SVR) as two techniques of artificial intelligence and data analysis and are used for prediction. Then, two methods were compared using the criteria for the function evaluation. The results of the study show that Support Victor Regression has better capability than the Artificial Neural Networks to predict stock output of Banks listed in Tehran Stock Exchange.
 
Publisher Academic Journal of Accounting and Economic Researches ISSN 2333-0783
 
Contributor —
 
Date 2016-07-05
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://worldofresearches.com/ojs-2.4.4-1/index.php/ajaer/article/view/281
 
Source Academic Journal of Accounting and Economic Researches ISSN 2333-0783; Vol 5, No 3 (2016): July 2016
2333-0783
2375-7493
 
Language eng
 
Relation http://worldofresearches.com/ojs-2.4.4-1/index.php/ajaer/article/view/281/215
 
Rights Copyright (c) 2016 Academic Journal of Accounting and Economic Researches ISSN 2333-0783
http://creativecommons.org/licenses/by-nc/4.0