Transforming Financial Market Prediction Through Generative AI-Powered Data Management

Predicting financial markets remains essential for decision-making in trading and investment. However, traditional models often fall short due to low-quality data, market volatility, and the complexity of global economic trends. Generative AI offers a transformative approach by improving data management processes, enhancing prediction accuracy, and overcoming the limitations of current forecasting models. This research explores the potential of Generative AI to enrich financial data through techniques like data augmentation, synthetic data generation, and anomaly detection. Specifically, the study investigates how integrating Generative AI into data preprocessing can enhance predictive modeling using Long Short-Term Memory (LSTM) networks for time series forecasting, particularly in stock prices. The research also explores feature engineering techniques such as moving averages, Relative Strength Index (RSI), and volatility metrics, which are crucial for capturing market trends. Generative Adversarial Networks (GANs) are employed to create additional realistic trading scenarios, boosting model robustness. Implemented using Python, the findings reveal that the combined LSTM and GAN model improved prediction accuracy over traditional methods, achieving an RMSE of 0.096 and R-squared (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{\mathbf{2}}$</tex>) <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0. 8 5}$</tex> in stock price forecasts. The incorporation of synthetic data with traditional financial metrics leads to an improvement in the model's ability to predict short-term market trends. These findings suggest that the use of Generative AI can provide a more resilient and adaptive approach to financial market prediction, opening new avenues for research in the domain. Future research could focus on expanding the scope to include multi-market scenarios and incorporating real-time data streams.