Manipulation and Financial Market Misconduct in Indonesia.

This article examines stock price volatility and potential manipulation in Indonesia's financial markets, focusing on stocks flagged for Unusual Market Activity (UMA) and benchmarked against the IDX80 and LQ45 indexes of the Indonesia Stock Exchange (IDX). Using machine learning models—Support Vector Machine (SVM) and Random Forest classifiers—alongside statistical analysis, the study identifies key financial and sectoral features, such as Insider Holdings and sector classification, that correlate with manipulation risk. Findings reveal that UMA stocks exhibit price movements largely aligned with broader market trends but show significantly higher volume volatility compared to the IDX80, suggesting irregular trading patterns. The Random Forest model outperformed Logistic Regression in detecting potential manipulation, highlighting the promise of advanced analytics for regulatory oversight and investor decision-making in emerging markets like Indonesia.