Ability of Machine Learning Algorithms and Artificial Neural Networks in Predicting Accounting Profit Information Content Before Announcing

The aim of this research is to investigate the capability of artificial neural networks and machine learning algorithms, including Support Vector Machine and Random Forest, in predicting the information content of accounting profits before its announcement in accepted companies on the Tehran Stock Exchange during the period from 2015 to 2020. Daily data required for the research were collected using Rahnaward-e-Novin software, and a systematic random sampling method was used to select 88 companies. MATLAB was used for modeling artificial neural networks and machine learning algorithms, and Python code was employed to calculate abnormal returns in neural networks and machine learning algorithms. The information content of profits was measured through the test of the relationship between profits and abnormal returns, based on the model by Porti et al. (2018). The input variables for artificial neural networks and machine learning algorithms are technical indicators. Accuracy, precision, recall, and F-score metrics were used for performance evaluation. The results of predicting with three models of artificial neural networks, Support Vector Machine, and Random Forest showed that Support Vector Machine and Random Forest had higher accuracy than artificial neural networks in predicting buy, sell, and hold strategies, and only Support Vector Machine had the ability to predict the information content of profits among the three models.Originality / Value: Designing a predictive model for stock price movements in the next trading day using artificial neural networks, Support Vector Machine, and Random Forest as the main innovation of the research. The research findings can increase the speed of information dissemination to the market and attract it, which will reduce the impact of informational asymmetry and information-based trading and ultimately enhance market efficiency.