{"id":"W4417237146","doi":"10.1007/s10614-025-11168-9","title":"Technical Analysis with Machine Learning Classification Algorithms: Can it Still ‘Beat’ the Buy-and-hold Strategy?","year":2025,"lang":"en","type":"article","venue":"Computational Economics","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Technical analysis; Sharpe ratio; Trading strategy; Trend following; Pairs trade; Statistical arbitrage; Profitability index; Algorithmic trading; Financial market; Equity (law)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02715723,0.002038253,0.002550563,0.003496678,0.002106571,0.009030758,0.003665784,0.005855707,0.01022909],"category_scores_gemma":[0.1059644,0.0006789701,0.001685537,0.002352753,0.00993209,0.0280468,0.003653118,0.01056947,0.003727787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002409477,"about_ca_system_score_gemma":0.003399702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002120329,"about_ca_topic_score_gemma":0.001759344,"domain_scores_codex":[0.9909433,0.00371174,0.0004977002,0.00109403,0.00326195,0.0004914357],"domain_scores_gemma":[0.9402829,0.04000121,0.00387457,0.007676549,0.006769426,0.001395391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001203173,0.0002063137,0.005406073,0.0003512725,0.0002633741,0.000124587,0.0002655403,0.01031702,0.0006404769,0.7916483,0.03023448,0.1604223],"study_design_scores_gemma":[0.00001812065,0.00004908282,0.0007658086,0.0001308228,0.00002431988,0.00005400609,0.0001102394,0.04442728,0.0003738108,0.9467399,0.00727386,0.000032582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03275117,0.01056284,0.7240982,0.1808805,0.005957177,0.0001285827,0.0003080572,0.0008537573,0.04445984],"genre_scores_gemma":[0.6669195,0.01104444,0.2519099,0.02831198,0.01466069,0.0003525447,0.0006128932,0.0009228316,0.02526531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02715723,"threshold_uncertainty_score":0.1436229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1093213188330786,"score_gpt":0.3837547398222899,"score_spread":0.2744334209892113,"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."}}