Forecasting Financial Sector Stock Price and Loss Risk Using the ARIMAX and Value-at-Risk Methods - Amanda Aulia; Trimono Trimono; Kartika Maulida Hindrayani (Komunitas Dosen Indonesia, 2025)
Stock price volatility remains a persistent challenge in financial forecasting, as traditional ARIMA-based models often neglect the role of macroeconomic forces, leading to limited predictive robustness. Addressing this methodological gap, this study uniquely integrates the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model and the Value-at-Risk (VaR) framework to simultaneously predict stock prices and quantify investment risk. This dual approach advances prior forecasting literature by merging predictive modeling and risk assessment within a single analytical structure. Using daily data from PT Bank Central Asia Tbk (BBCA) and the USD/IDR and SGD/IDR exchange rates from January 2019 to September 2024, model identification through ACF, PACF, and the Akaike Information Criterion (AIC) identifies ARIMAX(0,1,1) as optimal. The model achieves a Mean Absolute Percentage Error (MAPE) of 2.19%, indicating very high predictive accuracy. Although forecasted movements appear smoother than observed fluctuations, the model effectively captures short-term market trends influenced by exchange rate dynamics. Historical simulation at a 95% confidence level estimates a daily Value-at-Risk (VaR) of 1.71%, implying a potential loss of approximately Rp17,144 per Rp1,000,000 invested. These results demonstrate that integrating ARIMAX with VaR not only enhances statistical precision but also provides practical value for investors and policymakers. The combined framework enables evidence-based decision-making, portfolio optimization, and risk mitigation in volatile capital markets, offering a replicable and data-driven model for financial forecasting under macroeconomic uncertainty.