High-Frequency Financial Time Series Return Prediction Oriented Towards Transaction Costs: A Hierarchical Ensemble Learning and Regularized Meta-Learning Framework Incorporating Microstructural Features of Broussonetia Papyrifera
Bibliographic record
Abstract
This paper evaluates a transaction cost-aware return prediction framework for minute-level high-frequency CSI 300 stock index futures data from 2017 to 2025, comprising 518,873 minute bars. Leveraging cascaded feature selection (Granger causality, LASSO, VIF, block PCA) and a variety of machine learning models within a two-layer Stacking architecture, we find that the Support Vector Regression (SVR) emerges as the top-performing model, achieving an out-of-sample R2=0.982 mean absolute error = 0.1631, directional accuracy = 96.2% and an annualized Sharpe ratio = 10.0. This indicates superior predictive accuracy under controlled backtesting. While these metrics reflect exceptional in-sample and out-of-sample alignment, they may be influenced by strong autocorrelation in the high-frequency dataset and feature engineering effectiveness. Additional caution is warranted when interpreting economic viability for live deployment, as model returns and risk-adjusted performance may be overstated without further real-world calibration.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".