APPLICATION OF THE RANDOM FOREST ALGORITHM FOR ESTIMATING CONDITIONAL VALUE AT RISK (CVAR) ON THE STOCK PORTFOLIO OF INSURANCE COMPANIES IN INDONESIA

Abstrak

This study aims to estimate Conditional Value at Risk (CVaR) for insurance company stock portfolios using a machine learning approach to improve the accuracy of financial risk measurement under extreme market conditions. The application of machine learning, particularly the Random Forest algorithm, is crucial for the Indonesian insurance sector, which faces increasing exposure to market volatility and uncertainty. The model predicts stock returns based on technical indicators such as moving averages, volatility, and lagged returns. The analysis uses historical data from ten insurance companies listed on the Indonesia Stock Exchange (IDX) for the period 2022–2025. To assess model performance, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Kupiec backtesting are employed. The model produces CVaR estimates of 1.65% and 1.94% at the 95% and 99% confidence levels, respectively. It also achieves a low MAE of 0.006701 and MSE of 0.000091, indicating high estimation accuracy. The Kupiec test results further confirm the statistical reliability of the CVaR estimates. This study contributes methodologically by highlighting the effectiveness of non-parametric ensemble learning in financial risk modeling. The findings offer practical implications for insurance firms and portfolio managers in adopting adaptive, data-driven risk mitigation strategies, especially in volatile market environments.