Multi-objective optimization with an evolutionary artificial neural network for financial forecasting
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we attempt to make accurate predictions of the movement of the stock market with the aid of an evolutionary artificial neural network (EANN). To facilitate this objective we constructed an EANN for multi-objective optimization (MOO) that was trained with macro-economic data and its effect on market performance. Experiments were conducted with EANNs that updated connection weights through genetic operators (crossover and mutation) and/or with the aid of back-propagation. The results showed that the optimal performance was achieved under natural complexification of the EANN and that back-propagation tended to over fit the data. The results also suggested that EANNs trained with multi-objectives were more robust than that of a single optimization approach. The MOO approach produced superior investment returns during training and testing over a single objective optimization (SOO).
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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