[Paper Review] Integrating Generative AI into Financial Market Prediction for Improved Decision Making

Chang Che, Zengyi Huang+3 more|arXiv (Cornell University)|Apr 4, 2024

Stock Market Forecasting Methods4 citations

TL;DR

This paper proposes a novel framework integrating conditional generative adversarial networks (cGAN) with time series analysis to enhance financial market prediction accuracy. By modeling complex market dynamics, the cGAN approach achieves high predictive fidelity, minimizing deviation from actual market performance.

ABSTRACT

This study provides an in-depth analysis of the model architecture and key technologies of generative artificial intelligence, combined with specific application cases, and uses conditional generative adversarial networks ( cGAN ) and time series analysis methods to simulate and predict dynamic changes in financial markets. The research results show that the cGAN model can effectively capture the complexity of financial market data, and the deviation between the prediction results and the actual market performance is minimal, showing a high degree of accuracy.

Motivation & Objective

• Address the challenge of accurately predicting dynamic financial market movements using advanced AI techniques.
• Overcome limitations of traditional models in capturing non-linear and volatile market behaviors.
• Integrate generative AI with time series analysis to model complex market data more effectively.
• Improve decision-making in financial markets by reducing prediction error and increasing model robustness.
• Demonstrate the practical viability of cGANs in real-world financial forecasting applications.

Proposed method

• Employ conditional generative adversarial networks (cGAN) to model conditional distributions of financial time series data.
• Train the cGAN on historical market data to generate realistic future price paths conditioned on specific market states.
• Integrate time series analysis techniques to refine and validate the generated market trajectories.
• Use adversarial training to improve the generator's ability to produce high-fidelity market simulations.
• Apply conditional inputs (e.g., volatility, volume, technical indicators) to guide the generation process and improve prediction relevance.
• Optimize the model using loss functions that minimize reconstruction error and improve alignment with actual market trends.

Experimental results

Research questions

• RQ1 Can cGANs effectively model the complex, non-linear dynamics of financial time series data?
• RQ2 How does the integration of cGANs with time series analysis improve prediction accuracy compared to conventional models?
• RQ3 To what extent can conditional inputs enhance the fidelity and relevance of generated market scenarios?
• RQ4 What is the deviation between cGAN-generated predictions and actual market performance in real-world conditions?
• RQ5 How robust is the proposed framework under varying market regimes and volatility levels?

Key findings

• The cGAN model effectively captures the complexity of financial market data, demonstrating strong adaptability to dynamic market conditions.
• Prediction results show minimal deviation from actual market performance, indicating high accuracy and reliability.
• The integration of cGANs with time series analysis significantly improves the fidelity of simulated market trajectories.
• Conditional inputs enhance the model's ability to generate contextually relevant and realistic market scenarios.
• The framework outperforms baseline models in terms of predictive accuracy and robustness across diverse market environments.
• The model maintains low error rates even during periods of high market volatility, confirming its resilience and practical utility.