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PREDICTING RETAIL INVESTOR BEHAVIOR USING DYNAMIC GRAPH NEURAL NETWORKS | ||
| Computational Methods for Differential Equations | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 21 آبان 1404 اصل مقاله (1.9 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22034/cmde.2025.67669.3236 | ||
| نویسندگان | ||
| Zahra Najafabadipour1؛ Jamal Valipour2؛ Majid Talebpoor1؛ Pourya Zareeihemat3؛ Hero Isavi* 4 | ||
| 1Department of Business Management, Faculty of Management, Islamic Azad University, Firoozkooh, Iran. | ||
| 2Department of Management, Financial Management, Azad University, Tehran, Iran. | ||
| 3Department of Business Management, Faculty of Management, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran. | ||
| 4Management Faculty, Islamic Azad University, Branch of Urmia, Urmia, Iran. | ||
| چکیده | ||
| Retail investors play a pivotal role in shaping financial market trends, yet accurately forecasting their behavior remains a complex challenge. Traditional models often fall short in capturing the temporal dynamics and evolving relationships inherent in investor behavior. In this paper, we introduce a novel frame- work based on Dynamic Graph Neural Networks (Dynamic GNNs) to predict retail investor actions with high accuracy and interpretability. Our approach constructs evolving graph representations of interactions between investors and assets over time, integrating both psychometric attributes (e.g., risk tolerance, decision-making tendencies) and sentiment signals derived from news and social media analysis. This fusion enables a comprehensive view of investor behavior in changing market contexts. We evaluate our model on a large-scale dataset of real-world retail investor transactions from brokerage platforms and compare its performance against a variety of benchmarks, including static GNNs, traditional machine learning models (XGBoost, Random Forest), and dynamic base- lines (e.g., RNNs, Temporal Graph Networks). Experimental results demonstrate that our Dynamic GNN framework achieves 12% higher accuracy, 15% improvement in precision, and 10% better recall over existing static GNN methods. Furthermore, it outperforms traditional dynamic methods by 8% in accuracy, thanks to its ability to capture fine-grained temporal patterns and incorporate rich investor-level features. However, scalability challenges arise when processing very large graphs, necessitating efficient sampling strategies. This research contributes to the advancement of behavioral finance by offering a robust, scalable, and interpretable method for modeling investor behavior. The proposed framework can support applications in algorithmic trading, risk management, and personalized financial advising, helping financial institutions better understand and serve retail investors | ||
| کلیدواژه‌ها | ||
| Key words and phrases. Retail Investors؛ Dynamic Graph Neural Networks؛ Investor Behavior Prediction؛ Sentiment Analysis؛ Behavioral Finance | ||
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آمار تعداد مشاهده مقاله: 1 تعداد دریافت فایل اصل مقاله: 1 |
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