{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008846933,0.0005323483,0.001413948,0.001118361,0.0007234387,0.002033422,0.001048437,0.001453857,0.002270429],"category_scores_gemma":[0.03853291,0.0003854193,0.0007038665,0.001069198,0.002673618,0.004738991,0.001289701,0.001935145,0.0002095422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007558207,"about_ca_system_score_gemma":0.0009088957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00288217,"about_ca_topic_score_gemma":0.002201519,"domain_scores_codex":[0.9977466,0.001176535,0.0001356354,0.0004174444,0.0003221754,0.0002015434],"domain_scores_gemma":[0.9815161,0.01344461,0.002932117,0.001212434,0.000520833,0.0003738703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005068321,0.000213352,0.07058632,0.0002351777,0.0003428681,0.0006119325,0.0003202304,0.6586956,0.002248425,0.2097231,0.002929503,0.05358659],"study_design_scores_gemma":[0.00002786224,0.00009933847,0.007868203,0.00002175764,0.00001769697,0.00009311565,0.00004858363,0.8375245,0.0005460349,0.1531909,0.0005376879,0.00002435666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7617242,0.002870024,0.2266953,0.00381877,0.0000805125,0.00005492722,0.000329155,0.0003595953,0.004067496],"genre_scores_gemma":[0.9948267,0.0002061445,0.004186254,0.00009608528,0.000046161,0.00001624028,0.00009384041,0.00001807606,0.0005103679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008846933,"threshold_uncertainty_score":0.04678762,"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."}}