{"id":"W3121271996","doi":"10.1016/j.intfin.2016.07.007","title":"Less is more: Testing financial integration using identification-robust asset pricing models","year":2016,"lang":"en","type":"article","venue":"Journal of International Financial Markets Institutions and Money","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; Université Laval","funders":"","keywords":"Spurious relationship; Identification (biology); Inference; Financial integration; Capital asset pricing model; Econometrics; Specification; Sample (material); Exploit; Constraint (computer-aided design); Financial asset; Computer science; Financial market; Economics; Finance; Machine learning; Engineering; Artificial intelligence","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":[0.01288037,0.001472652,0.001322759,0.001340869,0.0008780178,0.002801674,0.001824459,0.001951281,0.004758828],"category_scores_gemma":[0.07769035,0.0005103778,0.001216903,0.001387406,0.001307272,0.00471695,0.002757023,0.002049875,0.0007208923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005854939,"about_ca_system_score_gemma":0.001193649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004679823,"about_ca_topic_score_gemma":0.002822436,"domain_scores_codex":[0.9929699,0.004436933,0.0003582467,0.001152733,0.0005612861,0.0005208538],"domain_scores_gemma":[0.9049711,0.07722664,0.009255434,0.004379807,0.001966345,0.002200693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004446485,0.005963882,0.7301829,0.000174158,0.003334859,0.0009063803,0.001362529,0.1220544,0.001556168,0.0295708,0.00371841,0.09672913],"study_design_scores_gemma":[0.0009152932,0.001965161,0.1412839,0.00007022005,0.0008117157,0.0003110878,0.00246544,0.791076,0.001986243,0.05781623,0.00119198,0.0001067836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884529,0.0001009231,0.009606189,0.0005510003,0.00003176559,0.00002474086,0.0001163315,0.00005849204,0.001057611],"genre_scores_gemma":[0.9970656,0.00003413725,0.002413642,0.00005121129,0.00002353854,0.00001538431,0.0002402954,0.000009195209,0.0001470709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01288037,"threshold_uncertainty_score":0.06811875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1133216403021835,"score_gpt":0.2738811304786767,"score_spread":0.1605594901764932,"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."}}