Investor Sentiment, Audit Quality, and Stock Returns: An AI-Driven Approach
The objective of the present research is to investigate the impact of investor sentiment on stock returns, emphasizing the moderating role of audit quality, using artificial intelligence methods within the period of 2012 to 2024. In this research, financial data of manufacturing companies listed on...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Mashhad: Behzad Hassannezhad Kashani
2025-06-01
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| Series: | International Journal of Management, Accounting and Economics |
| Subjects: | |
| Online Access: | https://www.ijmae.com/article_222106_d5e6be15fe22d55c2b929889dc58302c.pdf |
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| Summary: | The objective of the present research is to investigate the impact of investor sentiment on stock returns, emphasizing the moderating role of audit quality, using artificial intelligence methods within the period of 2012 to 2024. In this research, financial data of manufacturing companies listed on the Tehran Stock Exchange were collected and analyzed using six machine learning models, including Artificial Neural Networks (ANN), Multilayer Perceptrons (MLP), Decision Tree, Random Forest, Cerebellar Model Articulation Controller (CMAC) neural networks, and Gradient Boosting methods. The variables under investigation included investor sentiment (SENT), audit quality (AQ), the interaction variable (SENT × AQ), and Fama-French market risk factors. Multiple performance metrics and cross-validation methods were used to evaluate the models. The results demonstrate that investor sentiment is the most significant predictor of stock returns across all AI models, with importance levels ranging from 28.3% to 33.8%, particularly achieving 33.8% in Gradient Boosting and 31.5% in Random Forest models. Audit quality emerged as the second most critical variable with importance levels of 23.5% to 26.4% across different models. The interaction variable (SENT × AQ) showed substantial moderating effects with importance ranging from 18.6% to 25.4%, reaching 24.7% in Decision Tree models, providing strong statistical evidence for the moderating role of audit quality. Gradient Boosting methods achieved superior performance with R² = 0.911 and prediction accuracy of 93.8%, followed by Random Forest with R² = 0.892. Cross-validation results (5-fold, 10-fold, and time-series) confirmed model stability and generalizability. |
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| ISSN: | 2383-2126 |