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Investigating the Explanatory Power of Accounting Information using Linear and Nonlinear Models in Predicting Economic Growth
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Vahid Bekhradi Nasab    , Ehsan Kamali * , Khadijeh Ebrahimi kahrizsangi |
| P.hD in Accounting, Assistant Professor Department of Accounting, Najaf Abad Branch, Islamic Azad University, Najaf Abad, Iran |
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Abstract: (860 Views) |
| The purpose of this study is to investigate the explanatory power of financial accounting information using linear and nonlinear models in predicting economic growth. The present study compares economic growth forecasts using Fama-Macbeth two-stage regression in the form of linear pattern and neural networks based on genetic algorithm and bird flight algorithm in the form of nonlinear pattern. The statistical population of this research includes all companies listed on the Tehran Stock Exchange. The period of research is from 2005 to 2019. The research method is based on a linear model based on combined data that predicts the rate of economic growth with the two-stage regression technique of Fama-Macbeth and the approach of total rolling time windows and Arima regression. Then, in a nonlinear model in the form of training data and test data in neural networks based on genetic algorithm and bird flight algorithm, a comparison is made with the linear pattern of two-stage Fama-Macbeth regression. Evidence showed that the Fama-Macbeth two-stage linear regression patterns have a higher explanatory power in predicting economic growth rate than the nonlinear patterns of neural networks based on genetic algorithm and bird flight algorithm and indicate the confirmation of financial accounting information in macro accounting theory. is. The results in specifying the three-factor model of Fama and French to the four-factor model proposed in the present study, increase the opportunity for future investment to the theory of Q and increase the close relationship with the future economy. The management factor positively predicts the future economy and the performance factor negatively predicts the future economy. |
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| Keywords: Fama-Macbeth Two-Stage Regression, Neural Network, Genetic Algorithm, Bird Flight Algorithm, Eeconomic Growth. |
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Full-Text [PDF 1231 kb]
(123 Downloads)
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notification: Research |
Subject:
Financial macroeconomics
Received: 2020/11/9 | Accepted: 2021/04/6 | Published: 2023/02/15
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