{"id":"W4389451201","doi":"10.1108/jcms-04-2023-0013","title":"Novel comparative methodology of hybrid support vector machine with meta-heuristic algorithms to develop an integrated candlestick technical analysis model","year":2023,"lang":"en","type":"article","venue":"Journal of Capital Markets Studies","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Support vector machine; Particle swarm optimization; Computer science; Machine learning; Artificial intelligence; Heuristic; Genetic algorithm; Data mining; Feature selection; Algorithm","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":[0.00114601,0.001175466,0.0009012501,0.00148974,0.0004343106,0.001429219,0.001509672,0.00104851,0.002286894],"category_scores_gemma":[0.001539661,0.0004349436,0.001269747,0.0007677184,0.0004056175,0.0008506779,0.0007801533,0.0007832402,0.0003610279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007709684,"about_ca_system_score_gemma":0.001385845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007521128,"about_ca_topic_score_gemma":0.003717916,"domain_scores_codex":[0.9994972,0.0001487932,0.00004035969,0.0000932937,0.0001589942,0.00006134651],"domain_scores_gemma":[0.9995146,0.0002093535,0.00006187451,0.00002736748,0.0001640705,0.00002280934],"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":[0.00003046714,0.00004846325,0.001141463,0.0001245363,0.00009001857,0.0001008655,0.0000658377,0.9252819,0.001240842,0.0100344,0.0005579652,0.06128308],"study_design_scores_gemma":[0.000002840062,0.00002108834,0.0000845773,0.000007476989,0.000009821364,0.0000107932,0.000007222976,0.9981765,0.0002456543,0.001036501,0.0003941173,0.000003425194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01251409,0.000619198,0.9811218,0.0001429026,0.00004741241,0.00009091726,0.00003621046,0.0003287921,0.00509866],"genre_scores_gemma":[0.7125821,0.0008655151,0.2813638,0.0001004243,0.00007988976,0.000560398,0.000186449,0.00007892394,0.004182465],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007521128,"threshold_uncertainty_score":0.01495469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4337199533043374,"score_gpt":0.4889003632460301,"score_spread":0.05518040994169277,"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."}}