Enhancing Financial Predictive Modeling with Synthetic Data Using Generative Approach

Financial predictive modeling plays a crucial role in decision-making, risk management, and strategic planning within financial markets and financial institutions. Ensuring the veracity and accuracy of synthetic data is a major challenge when it comes to developing forecasting models. Otherwise, inaccurate model predictions and flawed decisions are likely to result if the artificial data created does not look like real-world financial patterns. A research study has tended to apply generative techniques on artificial information to determine the potential for influencing financial forecasting models through content selection and improved model accuracy. This study critically examines RBMs and Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to create synthetic data that mimics the intricate behaviors of financial datasets like market volatility, price couplings, and time lags to perfection. Furthermore, this research introduces the use of Kullback-Leibler Divergence (KL-Divergence) as a measure to evaluate that how distant the synthetic data is from the real financial data. The operative nature of KL-Divergence allows one to ascertain how well the synthetic data can emulate the true underpinning distribution of actual finance data. Results indicate that Real and Fake achieved a skewed distribution peaking at 25 with density coverage fluctuating from − 0.50 to 1.25 using Python software. The results reveal that the integration of synthetic data generated reporting by R.B.M. and other generative models into training datasets can substantially improve model performance, even under market conditions that tend to flip-flop or show rarity. Posted literature-On future research is dealing with integration between advanced reinforcement learning techniques and generative models to derive the finest possible artificial data pools for adaptability in financial forecasting.