Modelling Optimal Predicting Future Cash Flows Using New Data Mining Methods (A Combination of Artificial Intelligence Algorithms)

الموضوعات :

Bahman Talebi ¹ (Department of Accounting, Bonab Branch, Islamic Azad University, Bonab, Iran)
Rasoul Abdi ² (Department of Accounting, Bonab Branch, Islamic Azad University, Bonab, Iran)
Zohreh Hajiha ³ (Department of Accounting, East Tehran Branch, Islamic Azad University, Tehran Iran)
Nader Rezaei ⁴ (Department of Accounting, Bonab Branch, Islamic Azad University, Bonab, Iran)

تاريخ الإرسال : 18 الإثنين , صفر, 1442 تاريخ التأكيد : 11 الأحد , جمادى الثانية, 1442 تاريخ الإصدار : 13 السبت , ذو الحجة, 1444

الکلمات المفتاحية: Genetic Algorithm, Particle swarm Algorithm, Future Cash Flows, Neural Network Model,

ملخص المقالة :

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 137 companies listed in Tehran Stock Exchange during (2009-2017) 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 15 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).

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