Modelling Optimal Predicting Future Cash Flows Using New Data Mining Methods (A Combination of Artificial Intelligence Algorithms)
Publish Year: 1402
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_AMFA-8-3_016
تاریخ نمایه سازی: 19 تیر 1402
Abstract:
The purpose of this study was to present an optimal model Predicting Future Cash Flows optimized neural network with genetic (ANN+GA) and particle swarm algorithms (ANN+PSO). In this study, due to the nonlinear relationship among accounting information, we have tried to predict future cash flows by combining artificial intelligence algorithms. Variables of accruals components and operating cash flows were employed to investigate this prediction; therefore, the data of ۱۳۷ companies listed in Tehran Stock Exchange during (۲۰۰۹-۲۰۱۷) were analysed. The results of this study showed that both neural network models optimized by genetic and particle swarm algorithms with all variables presented in this study (with ۱۵ predictor variables) are able to provide an optimal model Predicting Future Cash Flows. The results of fitting models also showed that neural network optimized with particle swarm algorithm (ANN+PSO) has lower error coefficient (better efficiency and higher prediction accuracy) than neural network optimized with ge-netic algorithms (ANN+GA).
Authors
bahman talebi
Ph.D. Student of Accounting, Bonab Branch, Islamic Azad University, Bonab, Iran.
Rasoul Abdi
Department of Accounting, Bonab Branch, Islamic Azad University, Bonab Iran.
Zohreh Hajiha
Department Of Accounting, East Tehran Branch, Islamic Azad University, Tehran, Iran
nader rezaei
Department of Accounting and Finance, Humanities Faculty, Bonab Branch of Islamic Azad University, Bonab, Iran
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