{"id":"W4407088921","doi":"10.3390/jrfm18020077","title":"The End of Mean-Variance? Tsallis Entropy Revolutionises Portfolio Optimisation in Cryptocurrencies","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cryptocurrency; Tsallis entropy; Portfolio; Econometrics; Variance (accounting); Mathematics; Entropy (arrow of time); Economics; Statistics; Financial economics; Computer science; Tsallis statistics; Physics; Thermodynamics; Accounting","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":[0.000810791,0.00008094705,0.0003355964,0.0002627038,0.0001346634,0.00005247836,0.0001456733,0.00003288695,0.00005142471],"category_scores_gemma":[0.00009656148,0.00006217815,0.0001261298,0.0003987609,0.00005630794,0.0001178575,0.00005988853,0.0000977277,0.000001853009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006525863,"about_ca_system_score_gemma":0.00001797854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005423645,"about_ca_topic_score_gemma":0.0003445339,"domain_scores_codex":[0.9988165,0.00002061724,0.0008685763,0.0001155228,0.00005468165,0.0001240855],"domain_scores_gemma":[0.9989635,0.00006273316,0.0007705094,0.0001208638,0.00006073817,0.00002170592],"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":[0.00008230106,0.00006638564,0.06149721,0.00005684351,0.00008671017,0.000005609231,0.0002983836,0.0005520149,0.000003301167,0.8625863,0.001357499,0.07340746],"study_design_scores_gemma":[0.0006797335,0.00007433626,0.4736691,0.000111393,0.00006024697,0.000001859646,0.0005446243,0.0007988733,0.000007974026,0.114213,0.4097341,0.0001047461],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6933319,0.09783954,0.1800423,0.001695073,0.002618575,0.0008184806,0.0001637625,0.00001473282,0.02347563],"genre_scores_gemma":[0.9792157,0.01927392,0.0009182134,0.00001921202,0.00007835226,0.000005180893,0.000001278489,0.00000245636,0.0004857078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7483733,"threshold_uncertainty_score":0.2535551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009480749633652261,"score_gpt":0.2038570167865222,"score_spread":0.1943762671528699,"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."}}