Profiling investor behavior in the Malaysian derivatives market using K-means clustering.

ABSTRACT: This study investigates the trading behaviors of Malaysian derivatives traders using a comprehensive dataset from Bursa Malaysia with K-means clustering, representing one of the first AI applications to derivatives market segmentation. The analysis encompassed over 11 million trade records for FCPO and FKLI derivatives from January to December 2022. Six key features were engineered to segment derivative traders: Total Number of Trades, Total Traded Amount, Overall Realized Profit, Average ROI, Maximum Account Vintage (trader experience in years), and Median Holding Days (typical position duration). Inverse Hyperbolic Sine transformation was applied to address extreme outliers, ensuring robust feature scaling. K-means clustering identified five distinct profiles: "High-Frequency, High-Risk

SUBMITTER: Tan EHL 

PROVIDER: S-EPMC12484065 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature