{"id":"W4297917825","doi":"10.1108/ajeb-11-2021-0131","title":"A novel approach for candlestick technical analysis using a combination of the support vector machine and particle swarm optimization","year":2022,"lang":"en","type":"article","venue":"Asian Journal of Economics and Banking","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Particle swarm optimization; Support vector machine; Artificial neural network; Computer science; Artificial intelligence; Data mining; Machine learning; Raw data","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":[0.0005793786,0.00069116,0.0007540624,0.00118947,0.0003373475,0.0008297336,0.0008485783,0.0006892975,0.001912507],"category_scores_gemma":[0.001072799,0.0003157592,0.0008596584,0.0008742731,0.0002369543,0.0009451858,0.0005666235,0.0006673237,0.0004932536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003838161,"about_ca_system_score_gemma":0.0007124681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002998687,"about_ca_topic_score_gemma":0.002438124,"domain_scores_codex":[0.9995472,0.00007020342,0.00003000137,0.0001003109,0.0002184482,0.00003378096],"domain_scores_gemma":[0.9996516,0.00009685923,0.00005099223,0.0000344044,0.0001465246,0.00001964488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001522246,0.0003046672,0.006699969,0.0002652529,0.0002223867,0.0002567949,0.0001060533,0.3193277,0.01530587,0.00927113,0.003028343,0.6450596],"study_design_scores_gemma":[0.000004929656,0.00005259754,0.0007390396,0.000005881021,0.00001328956,0.00003223679,0.0000113932,0.9963363,0.001142069,0.0008485244,0.0008060104,0.000007772831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01607987,0.0002121409,0.9809872,0.0001123961,0.00007690447,0.00007174112,0.00004174673,0.0004348902,0.001983105],"genre_scores_gemma":[0.5316088,0.000401356,0.4614765,0.0001073699,0.0001295982,0.0002071167,0.0002581878,0.00007078222,0.005740306],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002998687,"threshold_uncertainty_score":0.006397963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08931313242446408,"score_gpt":0.3357189917938698,"score_spread":0.2464058593694057,"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."}}