Enterprise Profit Forecast Model Based on Long Short-Term Memory Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
In the era of the rapid development of artificial intelligence, in order to improve the usefulness of accounting information, this paper uses Long Short-Term Memory (LSTM) neural network model and financial statement information to forecast the profit of listed companies, and compares with the results predicted by analysts. In the profit forecast task of enterprises from Shanghai and Shenzhen 300 (CSI 300), the average accuracy of LSTM model is 88.6%, which is 13.52% higher than the average accuracy of analysts' forecast. In the accuracy distribution, there is no thick tail phenomenon in the results of LSTM model, and its kurtosis is significantly higher than that of analysts' forecast, and the variance is significantly lower than that of analysts' forecast. It reveals the practical significance of the application of artificial intelligence model in financial forecasting.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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