{"id":"W1982954151","doi":"10.1016/j.csda.2007.08.016","title":"Testing the random walk hypothesis through robust estimation of correlation","year":2007,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Heteroscedasticity; Autocorrelation; Econometrics; Mathematics; Random walk; Statistics; Estimator; Random walk hypothesis; Pearson product-moment correlation coefficient; Outlier; Stock market","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":[0.01772158,0.0009219833,0.00219894,0.00191292,0.0008096783,0.002871443,0.002620868,0.002309065,0.002348145],"category_scores_gemma":[0.1556219,0.0009637002,0.001778359,0.001483998,0.002444275,0.00447996,0.001963336,0.00286609,0.0006078175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006306523,"about_ca_system_score_gemma":0.002080978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001471024,"about_ca_topic_score_gemma":0.0008521489,"domain_scores_codex":[0.9850982,0.008873085,0.0007446512,0.002961202,0.001770026,0.0005527915],"domain_scores_gemma":[0.7527824,0.2169172,0.009782382,0.0157758,0.003537235,0.001204947],"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":[0.001447112,0.0009277932,0.0512374,0.0004156701,0.002589137,0.001165133,0.0003961766,0.4645887,0.006162411,0.3254295,0.004621013,0.14102],"study_design_scores_gemma":[0.0001246737,0.0001388192,0.003079468,0.00002323136,0.00007184632,0.0001273248,0.00003798748,0.8967425,0.00156665,0.09766053,0.0003916367,0.00003535026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1165126,0.0001831146,0.8812161,0.0005019014,0.00006233591,0.0000509573,0.0001392497,0.0004394409,0.0008942015],"genre_scores_gemma":[0.8803409,0.000182111,0.1175221,0.0002317017,0.0001579999,0.0001415223,0.0006595114,0.000125371,0.0006386857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01772158,"threshold_uncertainty_score":0.09372181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1400539877214131,"score_gpt":0.2872192153077154,"score_spread":0.1471652275863024,"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."}}