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All Journal Economic: Journal Economic and Business
Ardicha Appu Sianturi
Universitas Negeri Medan
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Integrating garch, markowitz mean–variance, and lstm for risk, volatility, and portfolio analysis of four major Indonesian banking stocks 2015 – 2024 Risca Octaviyani Hutapea; Gizka Triyunita Sinaga; Ardicha Appu Sianturi
Economic: Journal Economic and Business Vol. 5 No. 1 (2026): ECONOMIC: Journal Economic and Business
Publisher : Lembaga Riset Mutiara Akbar (LARISMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/ejeb.v5i1.1401

Abstract

This study examines the risk structure, volatility behavior, and optimal portfolio construction of four major banking stocks in Indonesia BBCA, BBRI, BMRI, and BBNI during the 2015–2024 period through the integration of the GARCH(1,1) model, Markowitz Mean–Variance optimization, and Long Short-Term Memory (LSTM)-based forecasting. Daily closing price data is transformed into log returns and tested for stationarity before further analysis. The GARCH estimation results indicate persistent high volatility across all stocks (?+? close to 1), with BBNI and BBRI the most responsive to market shocks, while BBCA remains the most stable stock. Markowitz optimization produces a Minimum Variance portfolio dominated by BBCA, while the Maximum Sharpe portfolio allocates funds entirely to BBCA due to its superior return efficiency. The LSTM is able to represent price trends well, as evidenced by low prediction error values for BBRI and BBNI and accuracy between 58–61 percent. The integration provides a comprehensive analytical framework for understanding changing market risk dynamics and supporting adaptive investment decision-making in the Indonesian banking sector. These findings confirm that the hybrid approach can improve risk mapping while maximizing portfolio performance through a combination of historical information, dynamic volatility, and price trend predictions.
Integration of survival analysis in predicting customer churn risk to optimize life insurance redemption value formulation Fachriz Effendy K; Risca Octaviyani Hutapea; Ardicha Appu Sianturi
Economic: Journal Economic and Business Vol. 5 No. 2 (2026): ECONOMIC: Journal Economic and Business
Publisher : Lembaga Riset Mutiara Akbar (LARISMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/ejeb.v5i2.1580

Abstract

The risk of voluntary policy surrender is a major threat to the liquidity and stability of life insurance companies' premium reserves. Conventional actuarial valuations, which assume a static depreciation rate, often fail to accurately mitigate this risk. This study aims to integrate a predictive analytics approach into actuarial mathematics to create a dynamic Surrender Value formula. Using 10,000 historical observations of financial customers as a proxy, the analysis was conducted using the Kaplan-Meier estimator and Cox Proportional Hazard (Cox PH) regression. The Kaplan-Meier estimation results show that the probability of policy survival experiences an exponential decay from 95.8% in the first year to 80.2% in the fifth year. Cox PH modeling confirms that entry age (hazard ratio = 1.048) and female gender (hazard ratio = 1.535) significantly increase the surrender risk, while active customer interaction (hazard ratio = 0.589) acts as a protective factor. The resulting cumulative individual hazard probabilities are then integrated as weighting constants into the surrender charge formula. This integration produces penalty recommendations that adapt to each policyholder's risk profile, providing more proportional and equitable liquidity protection for insurance company operations.
Co-Authors Fachriz Effendy K Gizka Triyunita Sinaga Risca Octaviyani Hutapea