یکی از مهم ترین مسائلی که معمولا شرکت‌ها به منظور حداکثر کردن ثروت سهامداران در راستای اجرای برنامه‌های استراتژیک شرکت با آن مواجه می‌باشند، تأمین مالی است. در پژوهش حاضر تاثیر اجاره‌های عملیاتی، به عنوان مهم‌ترین و پرکاربردترین ابزار تأمین مالی خارج از ترازنامه بر ارزش افزوده اقتصادی و پایداری سود به عنوان معیارهای ارزیابی عملکرد واحد اقتصادی طی سال های 1388 الی 1394 مورد ارزیابی قرار گرفت. مجموعاً تعداد 70 شرکت، بعبارتی490 مشاهده(سال-شرکت) به عنوان نمونه انتخاب و آزمون فرضیه‌های پژوهش با استفاده از روش آزمون های تفاوت میانگین دو جامعه مستقل و رگرسیون چند متغیره با نوع داده های پولد انجام شده است. نتایج پژوهش نشان می دهد که تأمین مالی خارج از ترازنامه از طریق اجاره های عملیاتی بر ارزش افزوده اقتصادی شرکت ها بی‌تأثیر بوده ولی بر پایداری سود تأثیر مستقیم دارد. بدین معنی که هر اندازه در یک شرکت استفاده از اجاره‌های عملیاتی افزایش می یابد پایداری سود شرکت افزایش یافته و سود از کیفیت و استمرار پذیری بیشتری برخوردار است. پرونده مقاله

Cash 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. پرونده مقاله

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). پرونده مقاله