{"id":"W3128019502","doi":"10.1016/j.irfa.2021.101657","title":"Same same but different – Stylized facts of CTA sub strategies","year":2021,"lang":"en","type":"article","venue":"International Review of Financial Analysis","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Universiteit Gent; European Commission; Queen's University; Central University of Finance and Economics; University of Technology Sydney; Queen's University Belfast","keywords":"Stylized fact; Contrarian; Volatility clustering; Volatility (finance); Economics; Econometrics; Cluster analysis; Financial market; Financial economics; Finance; Autoregressive conditional heteroskedasticity; Computer science; Artificial intelligence; Macroeconomics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004747488,0.0001469229,0.001052679,0.000246814,0.00002489413,0.00002900356,0.0003077828,0.00006384678,0.002036637],"category_scores_gemma":[0.001296925,0.0001477819,0.0006252851,0.0008701939,0.00005228413,0.000131441,0.00009770106,0.0001000049,0.00001124192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006829509,"about_ca_system_score_gemma":0.0001041242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004976828,"about_ca_topic_score_gemma":0.0005423142,"domain_scores_codex":[0.9981603,0.00003657938,0.001201586,0.0003293038,0.0001357537,0.0001364496],"domain_scores_gemma":[0.9981645,0.00007808916,0.0008406101,0.0003460884,0.0005223589,0.00004834703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00005790564,0.0006166847,0.4681859,0.002806238,0.002264871,0.00001351166,0.000105264,0.00005759103,0.0004287536,0.5115183,0.0004805457,0.01346445],"study_design_scores_gemma":[0.0005060358,0.00004666839,0.9145889,0.0009584002,0.0004570244,0.000001698565,0.00003881665,0.01895694,0.000593309,0.04910919,0.01436337,0.0003796432],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9560149,0.02393813,0.004879183,0.0008894114,0.0003465167,0.0001423679,0.001383191,0.000008630297,0.01239772],"genre_scores_gemma":[0.9632424,0.03536151,0.0002931787,0.000245239,0.00004416156,0.00001065967,0.0002606781,0.000006947758,0.0005352562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4624091,"threshold_uncertainty_score":0.9988756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02232577813923972,"score_gpt":0.2625352962888738,"score_spread":0.2402095181496341,"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."}}