{"id":"W2914318589","doi":"10.3390/jrfm12010031","title":"Statistical Arbitrage in Cryptocurrency Markets","year":2019,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cryptocurrency; Arbitrage; Statistical arbitrage; Transaction cost; Financial economics; Econometrics; Economics; Momentum (technical analysis); Database transaction; Index arbitrage; Algorithmic trading; Computer science; Finance; Risk arbitrage; Capital asset pricing model; Arbitrage pricing theory","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004535936,0.00006058015,0.0001378746,0.0001849268,0.00003333534,0.00002510816,0.0003040681,0.00004538527,0.00001088892],"category_scores_gemma":[0.00002501637,0.00005226011,0.000025221,0.0002172866,0.00002534621,0.00009784917,0.0001310249,0.0002763253,0.000008969285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001591072,"about_ca_system_score_gemma":0.00001703564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005268309,"about_ca_topic_score_gemma":0.000005502564,"domain_scores_codex":[0.9993612,0.00002905919,0.0002448701,0.0001251182,0.0001177681,0.0001219735],"domain_scores_gemma":[0.9996007,0.00004861611,0.0001249309,0.0001665688,0.0000252686,0.00003390884],"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":[0.00001100445,0.0000733761,0.01335593,0.00001392349,0.000002665762,0.00004168582,0.0001085602,0.000003837614,0.000001895307,0.5933525,0.000288919,0.3927457],"study_design_scores_gemma":[0.0007641603,0.0001157557,0.7013866,0.00003584401,0.000009219446,0.00002026767,0.00003504629,0.001713189,0.00001186065,0.2472919,0.04851096,0.0001052248],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3138147,0.0003438468,0.6842536,0.0002052506,0.0001763802,0.0001191764,0.000002117462,0.000009647566,0.001075267],"genre_scores_gemma":[0.9622199,0.0008798001,0.03680038,0.00005934727,0.00001906457,0.000003373761,1.576007e-7,0.000001807368,0.0000161803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6880307,"threshold_uncertainty_score":0.2131106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003400383336829689,"score_gpt":0.2084044527693975,"score_spread":0.2050040694325678,"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."}}