Proactive Market Crash Prediction: Investigating GNN-LSTM Networks for Early Detection in Stock Markets

This study examines the combination of graph-based representations with sequential modeling approaches to improve early detection mechanisms, with a focus on the possibility of GNN-LSTM networks for proactive market crash prediction in stock markets. The purpose of this research is to determine if Graph Neural Networks (GNNs) combined with Long Short-Term Memory (LSTM) networks can effectively anticipate market crashes in advance in stock markets. The necessity for strong early detection techniques is underscored by the substantial dangers that market collapses represent to investors and financial stability. The stock market is represented as a graph using GNNs, with each stock being a node connected to other nodes depending on interactions like co-movements or correlation. GNNs learn embeddings for every node through iterative updates, capturing the inherent characteristics of the stocks as well as their interactions with one another within the market. In the meanwhile, temporal relationships and sequential patterns in stock market data are modeled using LSTM networks. Because LSTMs are so good at capturing the many temporal patterns and trends found in time-series data, they are a good choice for predicting future moves in the market. Combining GNNs with LSTMs provides a complementary method for anticipating market crashes in advance. The study employs historical stock market data to train and evaluate the GNN-LSTM model, utilizing appropriate evaluation metrics to assess its performance. The proposed method is implemented in Python software and has an accuracy of about 99.12% which is 1.49% higher than other existing methods like Conv-LSTM, Convolutional Neural Network (CNN)-LSTM, and CNN-GRU (Gated Recurrent Unit). The findings of this research have implications for investors, financial institutions, and policymakers, providing insights into proactive risk management strategies and decision-making processes in response to evolving market conditions. Overall, the investigation contributes to the advancement of predictive modeling techniques for early detection in stock markets, enhancing financial stability and investor confidence.