Forecasting stock market anomalies in emerging markets: An OPTUNA-optimized isolation forest and K-means approach

Forecasting financial anomalies in emerging markets is critical for informed investment and risk management. This study proposes a novel machine learning framework that integrates an OPTUNA-optimized Isolation Forest algorithm with K-Means clustering to detect and classify stock market anomalies in Iran Khodro, one of Iran’s largest automotive firms. Leveraging daily stock data from 2001 to 2022, the model enhances anomaly detection accuracy by tuning hyperparameters through Bayesian optimization, significantly reducing false positives compared to standard implementations. The K-Means clustering algorithm further segments the detected anomalies into meaningful behavioral categories based on price and trading volume dynamics. Results reveal distinct periods of market disruption aligned with major political and economic events, including sanctions, currency volatility, and the COVID-19 pandemic. This hybrid approach demonstrates a robust, efficient, and interpretable method for forecasting abnormal market behavior in high-volatility, low-transparency environments. The framework holds promise for broader application in forecasting stock anomalies across other emerging financial markets.