Robust Deep Learning Models for Predicting the Trend of Stock Market Prices During Market Crash Periods
Bibliographic record
Abstract
Investing in the stock market involves both opportunities and risks. However, the stock market may experience some fluctuations, which most investors regard as serious threats -- particularly when prices fall sharply due to external circumstances. At the beginning of 2020, as a consequence of the COVID-19 pandemic, equity market indices fell sharply. During this time, many investors suffered significant losses. Despite substantial research in stock market forecasting and the development of various efficient models, existing methods fall short in proposing sustainable and stable models during a financial crisis. To address this research gap, we propose two novel deep learning models: a Convolutional Neural Network (CNN)-based ensemble model, namely GAFECNN-Stacking and a stacking ensemble of enhanced WGANs-based model, namely GAFEWGAN, that maintains a high level of resilience to the stock market crash. The GAF-ECNN Stacking and GAF-EWGAN models achieved an average of 13.26% and 16.49% annual returns over 20 selected stocks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".