{"id":"W7127599319","doi":"10.62517/jse.202511612","title":"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","year":2025,"lang":"","type":"article","venue":"Journal of statistics and economics.","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Feature selection; Sharpe ratio; Autocorrelation; Feature (linguistics); Time series; Ensemble learning; Ensemble forecasting; Lasso (programming language); Absolute return; Support vector machine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001077192,0.00068262,0.0007092712,0.0009058042,0.0002615963,0.0006950738,0.000796138,0.0005637645,0.0006398384],"category_scores_gemma":[0.00161534,0.0002257609,0.0008494637,0.0005341959,0.0001633348,0.0007968518,0.0004473883,0.0007987695,0.0002582215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002972494,"about_ca_system_score_gemma":0.0004227093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005856498,"about_ca_topic_score_gemma":0.008192558,"domain_scores_codex":[0.9997836,0.00006335537,0.00001220749,0.00007016465,0.00003607495,0.00003468556],"domain_scores_gemma":[0.9996188,0.0001625225,0.00005027021,0.00005097836,0.00009192833,0.00002557014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001828126,0.000243984,0.02757754,0.0000630083,0.0002780625,0.0001674782,0.00008493195,0.7757283,0.004892303,0.001778923,0.002051566,0.1869511],"study_design_scores_gemma":[0.00000185188,0.00002147471,0.001869019,0.00000400629,0.00001619667,0.00000925954,0.000009302156,0.9969928,0.0003133796,0.0005943183,0.0001636703,0.000004619258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6009938,0.001195485,0.3924519,0.0006912539,0.00009471618,0.00004645089,0.0008626659,0.001324021,0.002339678],"genre_scores_gemma":[0.9589227,0.0002016817,0.03872721,0.00006412448,0.00005388145,0.00003318388,0.0009682096,0.00003905545,0.0009899967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005856498,"threshold_uncertainty_score":0.01164478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617331615410305,"score_gpt":0.2857674534026071,"score_spread":0.269594137248504,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}