Enhancing Analytical Precision in Company Earnings Reports through Neurofuzzy System Development: A Comprehensive Investigation

Abstract

Te object of research is the fundamental and technical indicators of companies after the release of the earnings report. Tis study attempts to address the issue of understanding the impact of fundamental and technical analysis indicator dynamics on profts and loss news releases. Tis research provides an in-depth analysis of stock price forecasting models, focusing on the infuence of earning report seasons as catalysts for stock price growth. Te study explores the relationship between key fnancial indicators, including earnings per share (EPS), revenue, and the maximum price observed in the 52-week period of the previous year (MaxW52). A trading algorithm is developed based on the adaptive neurofuzzy inference system (ANFIS). Trough a comprehensive analysis of the neural network’s training sample, it is concluded that abnormally large negative indicators have a profound impact on traders’ emotional reactions. Tis results leads to a hypothesis for further research, suggesting that report indicators may be processed by computational algorithms, potentially including artifcial intelligence (AI). Consequently, the emergence of emotional trading robots managed by investment funds becomes a crucial area for investigation. Understanding the behavior of these algorithms enables proactive decision-making, allowing traders to leverage their knowledge and sell-purchased securities to these algorithms before their transactions occur. Te implications of this research shed light on the evolving landscape of trading strategies and the role of emotionality in fnancial markets.