Comparing the Efficiency of Statistical Models and Machine-Learning Models and Choosing the Optimal Model for Predicting Net Profit and Operating Cash Flows

• Published in: Mudīrriyat-i dārāyī va ta̓mīn-i mālī 2023-06, Vol.11 (2), p.53-74
• Main Authors: Sajjad Mirzaei, Ali Ashtab, Akbar Zavari Rezaei
• Format: Article
• Language: per
• Subjects: classification; data mining; machine learning; net profit forecasting; operating cash flow forecasting
• ISSN: 2383-1189
• DOI: 10.22108/amf.2023.136720.1784

The present study compared the predictive performance of machine-learning models and statistical models for forecasting profit and operational cash flow by using a combination of accrual and cash variables. The research method encompassed 3 main stages: data set and variable selection, modeling, and estimation. The study focused on companies listed on the Tehran Stock Exchange (TSE), analyzing data from 184 companies over the period of 2012-2021. The findings indicated that accrual variables exhibited greater explanatory power than cash variables in predicting net profit and future operating cash flow. Furthermore, the comparison of machine-learning and statistical models for forecasting net profit and future operating cash flow revealed that the artificial intelligence approach exhibited superior capability. Specifically, symbolic regression among the machine-learning models and the probit model among the statistical models demonstrated higher performance. Additionally, the results indicated that certain statistical models outperformed some machine-learning models while, on average, machine-learning models outperformed statistical models.Keywords: Classification, Data Mining, Machine Learning, Net Profit Forecasting, Operating Cash Flow Forecasting. IntroductionIn the current intensely competitive business environment, precise prediction of financial outcomes has emerged as a pivotal element in organizational triumph. Projecting crucial financial indicators, such as net profit and operating cash flows, equips businesses with the insight needed to make well-informed choices regarding investment strategies, resource distribution, and comprehensive financial strategizing. The capacity to anticipate future financial performance enables organizations to streamline operations and mitigate risks. Consequently, there is an escalating need for effective forecasting models.This study had two primary objectives: firstly, assessing the predictive capability of accrual and cash variables for forecasting profit and future cash flows and secondly, comparing the efficacy of statistical models and machine-learning models in predicting net profit and operating cash flows. Statistical models seek to scrutinize historical data patterns and underlying relationships to anticipate future financial outcomes. Conversely, machine-learning models have emerged as a potent alternative, employing advanced computational techniques to glean insights from data and make predictions withou