A Robust Deep Learning Model for Predicting the Trend of Stock Market Prices During Market Crash Periods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The stock market is one of the most important investment opportunities for small and large investors. Stock market fluctuations provide opportunities and risks for investors. However, some fluctuations are considered as enormous threats for most investors; significantly when the stock market has fallen sharply due to external factors and does not reach its previous point in a long time. For example, at the beginning of 2020, financial market indices, especially the stock market, fell sharply due to the COVID-19 pandemic, and for a long time, the indices did not grow significantly. Many investors suffered huge losses during this period. Although much research has been done in stock market forecasting and very efficient models have been proposed so far, no special effort has been made to build a model resistant to the collapse of financial markets. We propose a Convolutional Neural Network (CNN)-based ensemble model that is highly resilient to the stock market crash, especially at the beginning of the COVID-19 period. The proposed model not only avoids losing money in financial crises but can bring significant returns to investors. Experimental results show that the ensemble CNN models using Gramian Angular Fields (GAF) has greatly improved the resistance of the model in critical market conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it