On Cross-Paradigm Validation of Financial Time Series Models and Deep Learning Approaches

Thomas M. Menino Convention & Exhibition Center 

Time series modeling has long been used for forecasting financial data, with traditional financial time series models providing interpretable and theoretically grounded results. Recent advances in deep learning have introduced flexible, data-driven approaches that often achieve high predictive accuracy but differ fundamentally in estimation and validation procedures. This study addresses the challenge of comparing these two model paradigms under a mathematically consistent framework and proposes a unified evaluation methodology for meaningful comparison in stock price forecasting. The framework is applied to daily stock prices of major U.S. technology firms, including Apple, Amazon, Meta, Microsoft, Alphabet, and Tesla. Financial time series models such as ARIMA, GARCH, GJR-GARCH, and ARIMA–GARCH ensembles are evaluated alongside RNN, LSTM, and GRU models. Model performance is assessed under a common validation structure, ensuring that observed differences reflect underlying model dynamics rather than inconsistencies in transformation or evaluation design, while highlighting trade-offs between theoretical structure and predictive flexibility within a unified comparison framework.

Financial Time Series Models

Cross-Paradigm Validation

Forecast Evaluation

ARIMA–GARCH

Deep Learning

Stock Prices 

Business and Economic Statistics Section