Financial Bubble Detection Using GSADF and LSTM-RNN Model: Evidence from Emerging Markets
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".