Authors : Sanidhya Vishal Sharma; Swati Joshi

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/27t49rac

Scribd : https://tinyurl.com/3afvj2hx

DOI : https://doi.org/10.38124/ijisrt/25nov813

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Abstract : Behavioral finance has emerged as a critical framework for understanding market dynamics beyond traditional rational agent models. This research presents a comprehensive multimodal approach to behavioral finance analysis, integrating market data, macroeconomic indicators, news sentiment, cryptocurrency metrics, Web3 analytics, GitHub development activity, and social sentiment to test five advanced hypotheses regarding behavioral pattern identification and market anomaly detection. The study employs an ultra-comprehensive data pipeline processing 30,400 samples across seven distinct data sources, generating 91 engineered features representing behavioral biases, investment patterns, and market psychology. Advanced machine learning techniques including Principal Component Analysis, t-Distributed Stochastic Neighbor Embedding, Variational Autoencoders, K-Means, Hierarchical Clustering, DBSCAN, Isolation Forest, One-Class SVM, and Elliptic Envelope are applied to identify behavioral structures and detect anomalies. Statistical validation through chi-square tests, ANOVA, Granger causality analysis, and lagged correlation studies demonstrates that three of five hypotheses (60%) achieve statistical significance at p < 0.05. Key findings reveal that behavioral structures exist and correspond to canonical biases (chi-square = 3406.780, p < 0.001), cluster assignments maintain moderate stability across market regimes (Jaccard similarity = 0.300), and sentiment and macroeconomic factors exhibit 65 significant causal relationships with behavioral patterns. However, multimodal data integration does not uniformly improve clustering quality (Silhouette score decrease of 0.116), and cluster-conditioned anomaly detection fails to outperform global methods (F1-score decrease of 0.017). These findings contribute to behavioral finance theory while providing practical applications for investment management, fraud detection, and regulatory compliance.

Keywords : Behavioral Finance, Multimodal Data Integration, Machine Learning, Hypothesis Testing, Anomaly Detection, Market Psychology

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