Article
Version 1
This version is not peer-reviewed
Deep Learning Model-Driven Financial Risk Prediction and Analysis
Version 1
: Received: 27 June 2024 / Approved: 28 June 2024 / Online: 29 June 2024 (05:57:56 CEST)
How to cite: Yang, T.; Li, A.; Xu, J.; Su, G.; Wang, J. Deep Learning Model-Driven Financial Risk Prediction and Analysis. Preprints 2024, 2024062069. https://doi.org/10.20944/preprints202406.2069.v1 Yang, T.; Li, A.; Xu, J.; Su, G.; Wang, J. Deep Learning Model-Driven Financial Risk Prediction and Analysis. Preprints 2024, 2024062069. https://doi.org/10.20944/preprints202406.2069.v1
Abstract
The integration of deep learning models into financial risk prediction and analysis has significantly transformed traditional approaches. While conventional quantitative methods often rely on simplistic metrics like maximum drawdown, the advent of deep learning necessitates a more nuanced evaluation, emphasizing the model's generalization ability, especially during market crises such as stock market crashes. This paper explores the critical aspects of evaluating deep learning models' risk control capabilities in finance, underscoring the importance of understanding both statistical metrics and generalization abilities, particularly in adverse market conditions. By examining deep learning models' performance in scenarios like stock market crashes and highlighting the significance of cross-validation techniques, this study aims to offer practitioners insights into constructing robust risk management systems. It advocates for a comprehensive approach integrating quantitative analysis with macroeconomic factors to enhance financial risk prediction and analysis in volatile markets. The experimentation reveals that different deep generative models excel in various aspects of financial time series analysis, with generative adversarial networks (GANs) demonstrating superior performance in predicting Value at Risk (VaR) and variational autoencoders (VAEs) excelling in return rate prediction. Moreover, integrating multiple models further enhances predictive performance, leveraging the strengths of each model to compensate for individual weaknesses. Overall, this paper underscores the potential and significance of deep generative models in financial time series analysis, offering a roadmap for improved risk management and decision-making in financial markets.
Keywords
Deep learning models; Financial risk prediction; Generative adversarial networks (GANs); Value at Risk (VaR); Model integration
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
