Financial Forecasting with BO-RL-GAN: A Dual Approach Combining Bayesian and Reinforcement Learning Techniques

This work proposes a new framework, BO-RL-GAN, which encapsulates the integration of Bayesian Optimization (BO) and Reinforcement Learning (RL) within the GAN structure designed for stock market prediction. Traditional approaches can be problematic, as they are associated with the inherent randomness or pro-nature of financial information inputs. To overcome these challenges, the BO-RL-GAN introduces two approaches: first, the hyperparameters of GANs are optimized properly; second, the training strategies of GAN s are adjusted appropriately by using reinforcement learning. The validity of the suggested model was analyzed with the help of historical data from the S&P 500, NASDAQ, and FTSE 100 indexes over ten years. This was measured by Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Directional Accuracy. The BO-RL-GAN achieved an RMSE of 0.068, better than the typical GAN (0.085), LSTM-based RNN (0.079), and ARIMA (0.091), and thus showed enhanced values of predicting outlook. Similarly it, the model had the lowest MAE equal to 0.053, while it was 0.067 for the standard GAN, 0.061 for the LSTM-based RNN and 0.070 for ARIMA, proving the ability of the model to minimize the errors of predictions. Furthermore, the BO-RL-GAN has a Directional accuracy of 78%, which is higher compared to classic GAN with 65%, and LSTM-based RNN with 70% so, proving that the concept of the BO-RL-GAN is effective in capturing the market trends. These findings assert the strength of the system, as occupied in this study, in terms of the appropriateness of the presented framework as a forecasted financial instrument.