Forecasting Market Crashes Using Gans And LSTM In Financial Management

Abstract

The task of financial market crash forecasting has become a crucial problem in quantitative finance and is demanding models capable of incorporating temporal dependencies and distributional irregularities in financial time series. Existing econometric models and isolated deep learning architectures have proven to be very limited in their ability to capture the sudden and non-linear dynamics preceding a market collapse event. In this paper, a new hybrid model using Generative Adversarial Networks (GANs) and Long Short-Term Memory (LSTM) networks with Gramian Angular Field (GAF) transformations and attention mechanism is proposed exclusively for the task of predicting financial market crashes with higher accuracy and interpretability. The Generator part of the GAN generates realistic financial sequences during data-poor periods of the financial crisis, and the Discriminator ensures the distributional consistency by using a Wasserstein loss function. The LSTM encoder is able to model long range temporal dependencies and the attention layer helps focus the model on the most crash predictive time steps. Experiments test the S&P 500, NASDAQ Composite and Germany DAX indices over the period 2000 to 2023 including the market crash that followed the Global Financial Crisis in 2008 and that following from the COVID-19 pandemic in 2020. The proposed GAN-LSTM model outperforms the standalone LSTM model, the vanilla GAN model, CNN-LSTM model with a higher margin than the proposed GAN-LSTM model, and the BiLSTM model by a significant margin with a Root Mean Square Error (RMSE) of 2.43, a Mean Absolute Error (MAE) of 1.78, and a Mean Absolute Percentage Error (MAPE) of 1.52%. The complementary contribution of each architectural component is confirmed by an ablation study. The findings show that the proposed framework yields a comprehensive, generic and real-life applicable answer to the problem of real-time warning systems of market crashes in Financial Risk Management.