Financial Bubble Detection Using GSADF and LSTM-RNN Model: Evidence from Emerging Markets

Abstract

Forecasting financial bubbles is a crucial task in financial economics due to the disruptive impact of asset price collapses on markets and economic stability. This study proposes a novel approach to bubble prediction by integrating the PSY (Phillips, Shi, and Yu) procedure for bubble detection with Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), a machine learning technique well-suited for modeling nonlinear time-series patterns. Using weekly data from the Vietnamese stock market covering the period from 2015 to 2025, we construct a binary dependent variable indicating the presence of bubble episodes based on the GSADF test. Key macro-financial variables, including returns, volatility, and geopolitical risk, are employed as predictors. The LSTM-RNN model is trained and validated using a time-split approach (2015–2019 for training, 2020–2022 for validation, and 2023–2025 for testing), ensuring robustness and preventing overfitting. Out-of-sample results demonstrate that the LSTM-RNN achieves a high accuracy of over 81% and significantly outperforms a random walk benchmark. Our findings highlight the critical role of macroeconomic uncertainty, especially geopolitical risk, in driving bubble dynamics. This research contributes to the literature by offering an early warning framework that combines econometric detection with advanced machine learning, supporting better decision-making for investors and financial regulators in emerging markets.