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AI-driven anomaly detection reveals significant market irregularities and local volatility in Bitcoin returns.
This study introduces a novel AI-powered Business Intelligence Dashboard System (AIBIDS) designed to detect and visualize calendar-based anomalies in cryptocurrency returns. Focusing on Bitcoin as a case study, the system integrates unsupervised machine learning algorithms to identify periods of abnormal market behavior across multiple temporal resolutions. The proposed system leverages a star-schema OLAP data warehouse, enabling real-time anomaly detection, dynamic visualization, and drill-down exploration of market irregularities. Empirical results confirm the presence of pronounced calendar effects in Bitcoin returns, such as heightened anomalies during Q1 and Q4, and reveal model-specific sensitivities to local versus global volatility. Our novel platform offers a practical, scalable innovation for investors, analysts, and regulators seeking to monitor cryptocurrency markets more effectively, and contributes to the emerging FinTech literature on AI-driven anomaly detection and behavioral market dynamics.
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Alberg et al. (2025) studied this question.
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