{"id":"W2262015899","doi":"10.2139/ssrn.2256217","title":"Convert Arbitrage, Happy Meals, and Insider Trading","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Arbitrage; Insider trading; Business; Index arbitrage; Financial economics; Pairs trade; Algorithmic trading; Risk arbitrage; Monetary economics; Insider; Statistical arbitrage; Economics; Alternative trading system; Finance; Political science; Capital asset pricing model; Law","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":[0.001227687,0.0002529812,0.000550998,0.0005166873,0.0008357438,0.003647508,0.0005054558,0.001409892,0.01846591],"category_scores_gemma":[0.0109562,0.0002176834,0.0003451418,0.0004294735,0.001517192,0.003272692,0.001399891,0.001751916,0.0008505001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004385068,"about_ca_system_score_gemma":0.0002175133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001047223,"about_ca_topic_score_gemma":0.001214727,"domain_scores_codex":[0.9995996,0.0001405694,0.00002444422,0.00007902575,0.0000707383,0.00008552636],"domain_scores_gemma":[0.9959128,0.001941888,0.001247877,0.0003043528,0.0001219343,0.0004710215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.007159228,0.001839702,0.1681107,0.0003707742,0.0003271072,0.005848398,0.004295651,0.01305164,0.005592867,0.6176726,0.01435173,0.1613796],"study_design_scores_gemma":[0.0003641102,0.001000593,0.1673062,0.0001431003,0.0002347142,0.00350743,0.005422854,0.04196162,0.001958276,0.7602156,0.01770904,0.000176458],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9367204,0.002118513,0.004687085,0.001790596,0.00009946395,0.00001653305,0.000110409,0.00007585734,0.05438106],"genre_scores_gemma":[0.9940837,0.0001839728,0.000286904,0.00004931594,0.00005992287,0.000002119086,0.00003518796,0.000009352395,0.0052896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01846591,"threshold_uncertainty_score":0.06177461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0162932155483815,"score_gpt":0.1929267366971603,"score_spread":0.1766335211487788,"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."}}