Benchmarking Transformers and Baselines for Multi-Horizon Stock Return Prediction with Technical and Earnings Features
Bibliographic record
Abstract
We study daily multi-horizon stock return prediction on the Dow $30(h \in\{1,5,21\})$ using technical indicators and earnings features under a strict temporal split (train 2016-2019, val 2020, test 2021-2024). At h = 1, LSTM attains the lowest RMSE ($\mathbf{0. 0 1 6 2 2}$), narrowly ahead of GRU (0.01625), while Random Forest yields the highest DA $\boldsymbol{(} \mathbf{5 1. 9 \%} \boldsymbol{)}$. At $h=5$, Ridge achieves the best RMSE ($\mathbf{0. 0 3 6 5 1}$). At $h=21$, LSTM delivers both the lowest RMSE (0.05084) and highest DA (55.0%), edging TFT and GRU by $0.2-0.3 \%$ RMSE and 1 pp DA and outperforming tabular baselines by $\mathbf{3 1 - 3 8 \%}$ in RMSE. Adding earnings features increases DA by 1 pp at $h=21$, and DA rises to $\mathbf{6 1. 4 \%}$ within ±3-day earnings windows (vs. 53.9% otherwise). By regime, GRU performs best in the 2022 bear (RMSE 0.0636) and LSTM in the $2023-2024$ rally (RMSE 0.0475; DA 56.2%). Overall, simple sequence models match or surpass TFT on this constrained daily panel, with tabular methods competitive at shorter horizons.
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How this classification was reachedexpand
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".