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Open Access Article
1 - The Evaluation of the Capability of the Regression & Neural Network Models in Predicting Future Cash Flows
Bahman Talebi Rasol Abdi Zohreh Hajiha Nader RezaeiCash flow and profit are two important indicators for measuring the performance of a business unit. The future prediction was always a necessity in everyday life, and one of the subjects in which “The Prediction” has a great importance is economical and fina MoreCash flow and profit are two important indicators for measuring the performance of a business unit. The future prediction was always a necessity in everyday life, and one of the subjects in which “The Prediction” has a great importance is economical and financial problems. The purpose of the present study is to predict future cash flows using regression and neural network models. Sub – separated variables of the accruals and operational cash flows were used to investigate this prediction. For this purpose, data of 137 accepted stock exchange companies in Tehran during 2009 to 2017 has been studied. In this study, Eviews9 software for regression model and Matlab13 software for Multi-Layer Artificial Neural Networks (MANN) with Error back propagation algorithm were used to test the hypotheses.The findings of the research show that both regression and neural network models within proposed variables in the present study have the capability of predicting future cash flows. Also, results of neural network models' processes show that a structure with 16 hidden neurons is the best model to predict future cash flows and this proposal neural network model compared with regression model in predicting future cash flows has a better and accurate function. Furthermore, in this study, it was noticed that accruals of assets compared with debt accrual and variables of operating cash flows with accrual components were more predictive for future cash flows. Manuscript profile -
Open Access Article
2 - Modelling Optimal Predicting Future Cash Flows Using New Data Mining Methods (A Combination of Artificial Intelligence Algorithms)
Bahman Talebi Rasoul Abdi Zohreh Hajiha Nader RezaeiThe 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 MoreThe 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). Manuscript profile -
Open Access Article
3 - Optimization of auditing strategies in companies listed on the Tehran Stock Exchange
Sajjad Azizi Barani Rasoul Abdi Nader Rezaei Asgar PakmaramAccounting processes and their related decisions play an important role in organizing business. If the organization does not have a comprehensive accounting system, managing it will face many problems such as liquidity deficit, tax penalties and so on. Therefore, optimi MoreAccounting processes and their related decisions play an important role in organizing business. If the organization does not have a comprehensive accounting system, managing it will face many problems such as liquidity deficit, tax penalties and so on. Therefore, optimizing accounting processes is essential for managing an organization and improves the company's performance and greater efficiency for business activities. On the other hand, estimates and subjective judgments in preparing financial statements have caused controversy among auditors and owners. In particular, in cases where the standards are not clear, the use of unavoidable judgment and mentality creates ambiguity in financial reporting. This is while the ultimate value of audit activity is in helping the user to determine the quality of the information received. On the other hand, the financial reporting crises of recent years have focused the attention of researchers and professional associations on increasing the reliability of audit reports and reducing negligence. Therefore, considering the importance of auditing and the quality of financial reporting, this study examines and analyzes auditing strategies and determines the optimal strategy to improve the quality of financial reporting. For this purpose, 120 companies active in the Tehran Stock Exchange, which have a legal and formal audit system and use external audit firms for auditing, are considered as a statistical sample based on the counting method, and the senior supervisors, managers and partners of this Companies have been questioned. Manuscript profile -
Open Access Article
4 - Comparing the Performance of Machine Learning Techniques in Detecting Financial Frauds
Jafar Nahari Aghdam Qala Jougha Nader Rezaei Yagoob Aghdam Mazraee, Rasol AbdiDetecting financial fraud is an important process in the activities of compa-nies. In the last decade, much attention has been paid to fraud detection techniques. Financial fraud is a problem with far-reaching implications for shareholders. Today, financial fraud in com MoreDetecting financial fraud is an important process in the activities of compa-nies. In the last decade, much attention has been paid to fraud detection techniques. Financial fraud is a problem with far-reaching implications for shareholders. Today, financial fraud in companies has become a big prob-lem. Companies and regulatory agencies must continuously develop their mechanisms to detect fraud. Machine learning and data mining techniques are currently commonly used to solve this problem. However, these tech-niques still need to be improved in terms of computational cost, memory cost, and dealing with big data that is becoming a feature of current financial transactions. In this research, machine learning techniques including logistic regression, neural network, and Bayesian linear regression were used to de-tect financial frauds in the Iranian stock market. According to the obtained results, the support vector machine model with radial kernel has the lowest RMSE and the highest accuracy criterion, and the support vector machine model with linear kernel and Bayesian linear regression has the highest RMSE and the lowest accuracy criterion for modeling the financial fraud of companies in they were Tehran stock market. Also, the models of artificial neural network model, Bayesian linear regression and support vector ma-chine model with linear kernel respectively had the lowest characteristic values and did not perform relatively well in detecting the existence of fi-nancial fraud in the companies present in the Tehran stock market. Manuscript profile -
Open Access Article
5 - Providing the optimal model for stock selection based on momentum, reverse and hybrid trading strategies
Seyed Saadat Hosseini Asgar Pakmaram Nader Rezaei Rasol AbdiMomentum strategy, despite its outstanding performance, offers different results at different time intervals. In this study, we aimed to provide an opti-mal model for stock selection based on momentum, reverse and hybrid trad-ing strategies using the data panel model. T MoreMomentum strategy, despite its outstanding performance, offers different results at different time intervals. In this study, we aimed to provide an opti-mal model for stock selection based on momentum, reverse and hybrid trad-ing strategies using the data panel model. The present research method was applied on the information of 180 companies in the period 2011 to 2021 was used to estimate the model. (Eviews12) software has been used to estimate the models. Based on the results of 8 time periods of 3, 6, 9, 12, 24, 36, 48 and 60 months based on different momentum and inverse strategies and a combination of loser, winner and loser-winner, winner-loser were analyzed. According to the data panel method, the studied strategies in small companies give more additional returns to investors than large companies. Also, based on the results of hybrid strategies, investors will receive more additional returns in the long run than simple momentum strategies. Manuscript profile
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