Hybrid Models for Financial Forecasting: Combining GANs with Traditional Econometric Models

Adarsh Naidu

doi:10.71097/IJSAT.v12.i3.2821

Hybrid Models for Financial Forecasting: Combining GANs with Traditional Econometric Models

Author(s)	Adarsh Naidu
Country	United States
Abstract	Financial forecasting plays a crucial role in fintech applications, where accuracy is fundamental for effective decision-making in areas such as risk assessment and portfolio optimization. Conventional econometric models like ARIMA and GARCH, while offering robustness and interpretability, often struggle with the non-linear, noisy, and high-dimensional nature of financial datasets (Box et al., 2015; Engle, 1982). On the other hand, Generative Adversarial Networks (GANs), a subset of deep learning models, excel in generating synthetic data that reflects intricate real-world patterns (Goodfellow et al., 2014). This study explores hybrid methodologies that integrate GANs with traditional econometric models to improve forecasting precision. Two primary approaches are proposed: data augmentation, where synthetic data generated by GANs supplements training sets for models like ARIMA, and feature engineering, where GANs extract complex features for inclusion in econometric frameworks. Experiments conducted on real-world stock price datasets demonstrate that these hybrid models can decrease mean squared error (MSE) by up to 40% compared to standalone econometric models. Applications of this research extend to risk management, fraud detection, and algorithmic trading, offering enhanced resilience and scalability. This study highlights the transformative potential of hybrid models in fintech and suggests future research directions, such as advanced GAN architectures and real-time forecasting capabilities (Arjovsky et al., 2017).
Keywords	Generative Adversarial Networks, Financial Forecasting, Econometric Models, Hybrid Models, Data Augmentation, Feature Engineering, Risk Management, Portfolio Optimization, Forecasting Accuracy, Fintech Applications
Field	Engineering
Published In	Volume 12, Issue 3, July-September 2021
Published On	2021-09-07
Cite This	Hybrid Models for Financial Forecasting: Combining GANs with Traditional Econometric Models - Adarsh Naidu - IJSAT Volume 12, Issue 3, July-September 2021. DOI 10.71097/IJSAT.v12.i3.2821
DOI	https://doi.org/10.71097/IJSAT.v12.i3.2821
Short DOI	https://doi.org/g899fm

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Hybrid Models for Financial Forecasting: Combining GANs with Traditional Econometric Models

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