Forecasting Mastercard Stock Price Using Singular Spectrum Analysis with Bootstrap Prediction Intervals

M. Fariz Fadillah Mardianto; Davin Firmansha; Dewi Rohayah

doi:10.31764/jtam.v10i4.39502

Forecasting Mastercard Stock Price Using Singular Spectrum Analysis with Bootstrap Prediction Intervals

M. Fariz Fadillah Mardianto, Davin Firmansha, Dewi Rohayah

Abstract

Stock price forecasting is essential for investment decision-making and financial risk management. However, the nonlinear and nonstationary characteristics of financial time series make accurate forecasting challenging. This quantitative study aims to forecast Mastercard Incorporated (MA) stock prices using Singular Spectrum Analysis (SSA) and bootstrap-based prediction intervals. Weekly closing price data from January 2017 to December 2025 were obtained from Investing.com. Automatic SSA was employed to automatically determine the optimal forecasting parameters using Mean Absolute Error (MAE) as the optimization criterion, while bootstrap resampling was used to estimate forecast uncertainty. The results indicate that the recurrent SSA approach outperformed vector SSA and achieved high forecasting accuracy with an out-of-sample MAPE of 3.60% and RMSE of 22.57. The optimal model was obtained with a window length of 104 and three reconstructed components, which explained 99.65% of the total variance. Furthermore, the bootstrap prediction intervals achieved a coverage probability of 98.94%, demonstrating reliable uncertainty quantification. The forecasting results suggest a continued upward trend in Mastercard stock prices over the forecast horizon, while the prediction intervals provide a realistic range of future price movements. These findings demonstrate that the integration of SSA and bootstrap prediction intervals offers an effective and interpretable framework for financial time series forecasting by combining automatic parameter optimization, accurate prediction, and uncertainty estimation.

Keywords

Singular Spectrum Analysis; Bootstrap Prediction Interval; Stock Price Forecasting; Financial Time Series; Uncertainty Quantification.

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DOI: https://doi.org/10.31764/jtam.v10i4.39502