APPLICATION OF THE RANDOM FOREST ALGORITHM FOR ESTIMATING CONDITIONAL VALUE AT RISK (CVAR) ON THE STOCK PORTFOLIO OF INSURANCE COMPANIES IN INDONESIA
Abstract
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.
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H. Chaoqun, W. Shen, J. Huizhen, and L. Wei, “EVALUATING THE IMPACT OF UNCERTAINTY AND RISK ON THE OPERATIONAL EFFICIENCY OF CREDIT BUSINESS OF COMMERCIAL BANKS IN CHINA BASED ON DYNAMIC NETWORK DEA AND MALMQUIST INDEX MODEL,” Heliyon, vol. 10, no. 1, Jan. 2024, doi: https://doi.org/10.1016/j.heliyon.2023.e22850.
I. Qabrati, “RISK MANAGEMENT IN BANKING SECTOR: EMPIRICAL DATA FROM COMMERCIAL BANKS IN KOSOVO,” 2019. doi: https://doi.org/10.32936/PSSJ.V3I1.
S. Chitta and H. Soni, “THE IMPACT OF FINANCIAL RISK MANAGEMENT ON FIRM PERFORMANCE: A STUDY IN FINANCIAL MANAGEMENT PRACTICES,” Revista de Gestão e Secretariado (Management and Administrative Professional Review), vol. 14, no. 10, pp. 18095–18110, Oct. 2023, doi: https://doi.org/10.7769/gesec.v14i10.2736.
K. B. Ajeigbe and F. Ganda, “MANAGEMENT OF RISK OF CORPORATE DISTRESS AND FAILURE BEFORE, DURING AND AFTER THE COVID-19 PANDEMIC VIA CORPORATE GOVERNANCE PERFORMANCE,” International Journal of Innovative Research and Scientific Studies, vol. 6, no. 4, pp. 741–752, 2023, doi: https://doi.org/10.53894/ijirss.v6i4.1900.
J. Zhao, J. Ouenniche, and J. De Smedt, “SURVEY, CLASSIFICATION AND CRITICAL ANALYSIS OF THE LITERATURE ON CORPORATE BANKRUPTCY AND FINANCIAL DISTRESS PREDICTION,” Machine Learning with Applications, vol. 15, Mar. 2024, doi: https://doi.org/10.1016/j.mlwa.2024.100527.
W. R. Murhadi, F. R. Kencanasar, and B. S. Sutedjo, “THE INFLUENCE OF FINANCIAL LITERACY AND FINANCIAL INTEREST ON THE FINANCIAL RISK TOLERANCE OF INVESTOR IN INDONESIA,” Journal of Law and Sustainable Development, vol. 11, no. 2, pp. 01–16, 2023, doi: https://doi.org/10.55908/sdgs.v11i2.310.
D. Filipovic and A. Khalilzadeh, “MACHINE LEARNING FOR PREDICTING STOCK RETURN VOLATILITY,” Swiss Finance Institute, pp. 21–95, 2021, doi: https://doi.org/doi.org/10.2139/ssrn.3995529.
J. Jia-Ying, L. J. Zhou, W. Shen, L. Xiuli, J. Huizhen, and P. Wentsao, “PERFORMANCE EVALUATION AND ANALYSIS OF CHINESE INSURANCE COMPANIES UNDER UNCERTAIN FINANCIAL MARKETS,” Heliyon, vol. 9, no. 11, Nov. 2023, doi: https://doi.org/10.1016/j.heliyon.2023.e21487.
M. M. Karadağ, “ANALYZING THE TURKISH INSURANCE COMPANIES’ FINANCIAL PERFORMANCE TRADED ON BIST IMPLEMENTING THE CRITIC-BASED PIV METHOD,” Insurance Markets and Companies, vol. 15, no. 2, pp. 47–60, 2024, doi: https://doi.org/10.21511/ins.15(2).2024.05.
P. Capelli, F. Ielasi, and A. Russo, “FORECASTING VOLATILITY BY INTEGRATING FINANCIAL RISK WITH ENVIRONMENTAL, SOCIAL, AND GOVERNANCE RISK,” Corp Soc Responsib Environ Manag, vol. 28, no. 5, pp. 1483–1495, Sep. 2021, doi: https://doi.org/10.1002/csr.2180.
M. Mavronicolas and B. Monien, “CONDITIONAL VALUE-AT-RISK: STRUCTURE AND COMPLEXITY OF EQUILIBRIA,” Theor Comput Sci, vol. 807, pp. 266–283, Feb. 2020, doi: https://doi.org/10.1016/j.tcs.2019.09.015.
A. M. Hakam and A. K. Jaya, “ESTIMATING CONDITIONAL VALUE AT RISK IN NON-CYCLICAL SECTOR COMPANIES USING THE EXTREME VALUE THEORY APPROACH,” Jurnal Matematika, Statistika dan Komputasi, vol. 21, no. 1, pp. 159–175, Sep. 2024, doi: https://doi.org/10.20956/j.v21i1.35849.
M. Kishida and A. Cetinkaya, “RISK-AWARE LINEAR QUADRATIC CONTROL USING CONDITIONAL VALUE-AT-RISK,” IEEE Trans Automat Contr, vol. 68, no. 1, pp. 416–423, Jan. 2023, doi: https://doi.org/10.1109/TAC.2022.3142131.
M. S. Murugan and S. K. T, “LARGE-SCALE DATA-DRIVEN FINANCIAL RISK MANAGEMENT & ANALYSIS USING MACHINE LEARNING STRATEGIES,” Measurement: Sensors, vol. 27, Jun. 2023, doi: https://doi.org/10.1016/j.measen.2023.100756.
W. H. Saputra, D. D. Prastyo, and H. Kuswanto, “MACHINE LEARNING MODELING ON MIXED-FREQUENCY DATA FOR FINANCIAL GROWTH AT RISK,” Procedia Comput Sci, pp. 397–403, 2024, doi: https://doi.org/10.1016/j.procs.2024.03.020.
L. Chen, L. Sun, C. M. Chen, M. E. Wu, and J. M. T. Wu, “STOCK TRADING SYSTEM BASED ON MACHINE LEARNING AND KELLY CRITERION IN INTERNET OF THINGS,” Wirel Commun Mob Comput, vol. 2021, no. 1, 2021, doi: https://doi.org/10.1155/2021/7632052.
Mustafian, Mauliddin, and A. M. Abdal, “PENERAPAN VALUE-AT-RISK DAN CONDITIONAL-VALUE-AT-RISK DALAM PENGUKURAN RISIKO PORTOFOLIO OPTIMAL MENGGUNAKAN PENDEKATAN SIMULASI MONTE CARLO,” Jurnal Riset dan Aplikasi Matematika, vol. 08, no. 01, pp. 39–50, 2024, doi: https://doi.org/10.26740/jram.v8n1.p39-50 .
M. Kapse, V. Sharma, R. Vidhale, and V. Vellanki, “CUSTOMIZATION OF HEALTH INSURANCE PREMIUMS USING MACHINE LEARNING AND EXPLAINABLE AI,” International Journal of Information Management Data Insights, vol. 5, no. 1, Jun. 2025, doi: https://doi.org/10.1016/j.jjimei.2025.100328.
S. Rawat, A. Rawat, D. Kumar, and A. S. Sabitha, “APPLICATION OF MACHINE LEARNING AND DATA VISUALIZATION TECHNIQUES FOR DECISION SUPPORT IN THE INSURANCE SECTOR,” International Journal of Information Management Data Insights, vol. 1, no. 2, Nov. 2021, doi: https://doi.org/10.1016/j.jjimei.2021.100012.
Y. Demirdöğen and M. Çelik, “COMPARISON OF MACHINE LEARNING MODELS FOR VALUE AT RISK CALCULATIONS IN THE CHEMISTRY INDEX,” Journal of Financial Economics & Banking, vol. 4, no. 2, 2023, [Online]. Available: https://www.researchgate.net/publication/377086540
R. Sahai et al., “INSURANCE RISK PREDICTION USING MACHINE LEARNING,” Lecture Notes on Data Engineering and Communications Technologies, vol. 165, pp. 419–433, 2023, doi: https://doi.org/10.1007/978-981-99-0741-0_30.
I. Yunita, “EVALUASI KINERJA INVESTASI SAHAM SYARIAH MENGGUNAKAN INDEKS SHARPE, TREYNOR DAN JENSEN PERIODE 2021-2022,” Jurnal Ilmiah Ekonomi Islam, vol. 9, no. 1, pp. 435–442, 2023, doi: https://doi.org/10.29040/jiei.v9i1.7348.
Kanal F.A., Manurung T., and Prang J.D., “APPLICATION OF GARCH (GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY) MODEL IN CALCULATING BETA VALUE OF STOCK INDEX PEFINDO25,” Jurnal Ilmiah Sains, vol. 18, Oct. 2018, doi: https://doi.org/10.35799/jis.18.2.2018.19732.
M. Y. T. Irsan, E. Priscilla, and S. Siswanto, “COMPARISON OF VARIANCE COVARIANCE AND HISTORICAL SIMULATION METHODS TO CALCULATE VALUE AT RISK ON BANKING STOCK PORTFOLIO,” Jurnal Matematika, Statistika dan Komputasi, vol. 19, no. 1, pp. 241–250, Sep. 2022, doi: https://doi.org/10.20956/j.v19i1.21436.
H. Markowitz, “PORTFOLIO SELECTION,” J Finance, vol. 7, no. 1, pp. 77–91, Mar. 1952, doi: https://doi.org/10.2307/2975974.
R. N. R. Sa’diyah, R. Nooraeni, W. A. Sofa, and M. I. Falahuddin, “PORTFOLIO OPTIMIZATION USING THE MEAN-VARIANCE METHOD WITH A PROTOTYPE-BASED SEGMENTATION APPROACH,” Procedia Comput Sci, vol. 245, pp. 601–616, 2024, doi: https://doi.org/10.1016/j.procs.2024.10.287.
Y. Zhang, J. Tao, Z. Yin, and G. Wang, “IMPROVED LARGE COVARIANCE MATRIX ESTIMATION BASED ON EFFICIENT CONVEX COMBINATION AND ITS APPLICATION IN PORTFOLIO OPTIMIZATION,” Mathematics, vol. 10, no. 22, Nov. 2022, doi: https://doi.org/10.3390/math10224282.
D. Rosadi, E. P. Setiawan, M. Templ, and P. Filzmoser, “Robust covariance estimators for mean-variance portfolio optimization with transaction lots,” Operations Research Perspectives, vol. 7, Jan. 2020, doi: https://doi.org/10.1016/j.orp.2020.100154.
P. Lestari, H. Saladin, and Oktariansyah, “ANALISIS PENGETAHUAN INVESTASI, RISIKO INVESTASI DAN MODAL MINIMAL TERHADAP MINAT INVESTASI DI PASAR MODAL (STUDI KASUS MAHASISWA UNIVERSITAS PGRI PALEMBANG),” Wacana Ekonomi: Jurnal Ekonomi, Bisnis dan Akuntansi, vol. 23, no. 2, pp. 131–145, 2024, doi: https://doi.org/10.22225/we.23.2.2024.131-145.
F. Sugiarto, D. B. Wiranatakusuma, A. F. Adyun, R. Fadhil, R. F. M. Ginanjar, and J. F. Evianto, “ANALISIS PERILAKU INVESTOR DI PASAR MODAL SYARIAH DAN KONVENSIONAL: STUDI KASUS PADA SAHAM-SAHAM SEKTOR KEUANGAN,” Journal of Economics, Assets, and Evaluation, vol. 1, no. 3, pp. 1–11, 2024, doi: https://doi.org/10.47134/jeae.v1i3.163.
R. Tyrrell Rockafellar and S. Uryasev, “OPTIMIZATION OF CONDITIONAL VALUE-AT-RISK,” The Journal of Risk, vol. 2, no. 3, 2000.
E. Lazar, J. Pan, and S. Wang, “ON THE ESTIMATION OF VALUE-AT-RISK AND EXPECTED SHORTFALL AT EXTREME LEVELS,” Journal of Commodity Markets, vol. 34, Jun. 2024, doi: https://doi.org/10.1016/j.jcomm.2024.100391.
S. Emmer, M. Kratz, and D. Tasche, “WHAT IS THE BEST RISK MEASURE IN PRACTICE? A COMPARISON OF STANDARD MEASURES,” Dec. 2013. [Online]. Available: https://essec.hal.science/hal-00921283v1
P. García-Risueño, “HISTORICAL SIMULATION SYSTEMATICALLY UNDERESTIMATES THE EXPECTED SHORTFALL,” Journal of Risk and Financial Management, vol. 18, no. 1, Jan. 2025, doi: https://doi.org/10.3390/jrfm18010034.
J. Zhang, “IMPACT OF AN IMPROVED RANDOM FOREST-BASED FINANCIAL MANAGEMENT MODEL ON THE EFFECTIVENESS OF CORPORATE SUSTAINABILITY DECISIONS,” Systems and Soft Computing, vol. 6, Dec. 2024, doi: https://doi.org/10.1016/j.sasc.2024.200102.
M. Schonlau and R. Y. Zou, “THE RANDOM FOREST ALGORITHM FOR STATISTICAL LEARNING,” Stata J, vol. 20, no. 1, pp. 3–29, Mar. 2020, doi: https://doi.org/10.1177/1536867X20909688.
T. Mahmood, M. Usman, and C. Conrad, “SELECTING RELEVANT FEATURES FOR RANDOM FOREST-BASED CROP TYPE CLASSIFICATIONS BY SPATIAL ASSESSMENTS OF BACKWARD FEATURE REDUCTION,” PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol. 93, no. 2, pp. 173–196, Apr. 2025, doi: https://doi.org/10.1007/s41064-024-00329-4.
P. Sadorsky, “A RANDOM FORESTS APPROACH TO PREDICTING CLEAN ENERGY STOCK PRICES,” Journal of Risk and Financial Management, vol. 14, no. 2, Feb. 2021, doi: https://doi.org/10.3390/jrfm14020048.
L. K. David, J. Wang, I. I. Cisse, and V. Angel, “MACHINE LEARNING ALGORITHMS FOR FINANCIAL RISK PREDICTION: A PERFORMANCE COMPARISON,” International Journal of Accounting Research, vol. 9, no. 2, pp. 49–55, 2024, [Online]. Available: www.j.arabianjbmr.com
L. Breiman, “RANDOM FORESTS,” Mach Learn, vol. 45, no. 1, pp. 5–32, 2001, doi: https://doi.org/https://doi.org/10.1023/A:1010933404324.
A. Nõu, D. Lapitskaya, M. H. Eratalay, and R. Sharma, “PREDICTING STOCK RETURN AND VOLATILITY WITH MACHINE LEARNING AND ECONOMETRIC MODELS: A COMPARATIVE CASE STUDY OF THE BALTIC STOCK MARKET,” Nov. 2021, doi: https://doi.org/https://dx.doi.org/10.2139/ssrn.3974770.
P. E. Astuti and T. Gunarsih, “VALUE-AT-RISK ANALYSIS IN RISK MEASUREMENT AND FORMATION OF OPTIMAL PORTFOLIO IN BANKING SHARE,” JBTI : Jurnal Bisnis : Teori dan Implementasi, vol. 12, no. 2, pp. 103–114, Sep. 2021, doi: https://doi.org/10.18196/jbti.v12i2.12263.
L. Yizheng, “PORTFOLIO RISK MANAGEMENT MODEL BASED ON MACHINE LEARNING,” Financial Engineering and Risk Management, vol. 6, no. 9, pp. 70–76, 2023, doi: https://doi.org/10.23977/ferm.2023.060910.
E. S. Nugraha, A. Olivia, F. N. F. Sudding, and K. E. Lestari, “ESTIMATION OF VALUE AT RISK FOR GENERAL INSURANCE COMPANY STOCKS USING THE GARCH MODEL,” Barekeng, vol. 19, no. 2, pp. 1071–1082, Jun. 2025, doi: https://doi.org/10.30598/barekengvol19iss2pp1071-1082.
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