{"id":"W4390014064","doi":"10.3390/jrfm17010004","title":"Reverse Stock Splits and Liquidity in ETFs","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Market liquidity; Univariate; Business; Stock (firearms); Stock exchange; Liquidity risk; Monetary economics; Econometrics; Multivariate statistics; Financial economics; Economics; Finance; Statistics; Mathematics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001589656,0.0002375619,0.0002753187,0.0008407063,0.0002848371,0.001685837,0.0003399343,0.0005785914,0.002592735],"category_scores_gemma":[0.01414997,0.00009766361,0.0002691431,0.0005431929,0.0008977533,0.002606758,0.001482718,0.0009459371,0.0002382767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003700252,"about_ca_system_score_gemma":0.0003007399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007147224,"about_ca_topic_score_gemma":0.0005497168,"domain_scores_codex":[0.9992008,0.000176428,0.0001123583,0.000107048,0.0002391861,0.0001643043],"domain_scores_gemma":[0.9842826,0.004957146,0.008565289,0.000615641,0.0006980176,0.0008813652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003316348,0.0006374195,0.9303142,0.0001091827,0.0002056828,0.00172442,0.001476498,0.005290581,0.006143673,0.009359516,0.0003177509,0.04110467],"study_design_scores_gemma":[0.0001657733,0.001610142,0.9505048,0.00008613981,0.000214343,0.001696663,0.002416519,0.00981154,0.007135172,0.02397697,0.002324827,0.00005694824],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985076,0.0002835036,0.0003278572,0.0000900189,0.000005375189,0.000004096782,0.00003720344,0.000003809051,0.0007405847],"genre_scores_gemma":[0.9996151,0.00005515588,0.00007421688,0.00001251288,0.00001636319,0.000002293351,0.00004114903,9.875931e-7,0.0001823037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002592735,"threshold_uncertainty_score":0.008673608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02323305364499471,"score_gpt":0.2138364590066578,"score_spread":0.1906034053616631,"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."}}