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:: Volume 1, Issue 2 (modern theories of accounting,winer2012,vol1,no.2 2011) ::
3 2011, 1(2): 83-104 Back to browse issues page
Predicting Stock Return Using Accounting Variables with Linear Regression Approach and Artificial Neural Network
Seyyed Hossein Sajadi , Sohrab Osta , Roohollah Gheitasi
Abstract:   (1715 Views)
This study Predicting the stock return by two models of linear regression and Artificial neural networks using accounting variables in Tehran Stock Exchange. Therefore, we developed a hypothesis using 140 companies` data during 1389-1384. In this study, we`ve used Pooled regression model and Ordinary Least Squares (OLS) method to investigate the linear relationship and usefulness of linear relation. We used the multilayer perceptron (MLP) based neural network by the algorithm of Back-propagation neural networks to investigate the nonlinear relationships and usefulness of these relations.  We used the Adjusted R-squared, RMSE) root-mean-square error), Mean absolute error and NMSE) normal mean sum of squares of errors) to evaluate the results .The results confirm these two models in Predicting stock return, but the neural network model is the better. In other words, using accounting variables neural network could reduce forecasting errors in comparison with linear regression
Keywords: artificial neural networks, linear regression, stock return, MLP
Full-Text [PDF 2879 kb]   (113 Downloads)    
Type of Study: Research | Subject: General
Received: 2011/10/9 | Accepted: 2011/12/14 | Published: 2011/12/22
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Sajadi S H, Osta S, Gheitasi R. Predicting Stock Return Using Accounting Variables with Linear Regression Approach and Artificial Neural Network. 3 2011; 1 (2) :83-104
URL: http://mta.raja.ac.ir/article-1-41-en.html


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Volume 1, Issue 2 (modern theories of accounting,winer2012,vol1,no.2 2011) Back to browse issues page
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