Forecasting Stock Market Crashes using Deep Learning

This paper addresses the important issue of predicting stock market crashes, which can cause major problems due to large drops and long recovery times. We present a new method that uses a mix of machine learning and deep learning to improve prediction accuracy and provide useful insights in financial forecasting. Our approach includes Support Vector Machines (SVMs), Deep Reinforcement Learning (DRL), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Deep Deterministic Policy Gradient (DDPG) models. An important part of our work is the development of a DRL model specifically designed to be more resilient during market downturns. This model can help reduce financial losses during crises and also provide significant returns for investors. The experimental results strongly support the robustness of our DRL model in unpredictable market conditions. Performance checks, which use R2-Score, Mean Squared Error (MSE), Mean Absolute Error (MAE), and Receiver Operating Characteristic (ROC) curves, confirm that our methods outperform models like Graph Neural Networks (GNNs) and Transformers. This DRL model achieves a high true positive rate for detecting crash signals while keeping the false-alarm rate low. Its strength comes from the reinforcement learning agent’s ability to learn decision-making strategies that take misclassification costs into account within a simulated trading environment.