{"id":"W3194755827","doi":"10.3390/jrfm14080386","title":"Optimizing Stock Market Returns during Global Pandemic Using Regression in the Context of Indian Stock Market","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Stock market; Stock exchange; Financial economics; Portfolio; Volatility (finance); Economics; Stock (firearms); Stock market bubble; Pandemic; Coronavirus disease 2019 (COVID-19); Econometrics; Context (archaeology); Finance; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.00226406,0.0007579933,0.001001699,0.0006443817,0.0002040472,0.001280557,0.0006829626,0.0008730695,0.0006707403],"category_scores_gemma":[0.007172697,0.0004035188,0.0006614767,0.0005730414,0.0003769955,0.0009747159,0.0005494988,0.00083507,0.0001237278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007829358,"about_ca_system_score_gemma":0.0006573503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01918597,"about_ca_topic_score_gemma":0.008832929,"domain_scores_codex":[0.9995897,0.0002051612,0.00002007585,0.00007163159,0.00003684617,0.0000765988],"domain_scores_gemma":[0.997142,0.002077054,0.0004068036,0.00007731475,0.0002017081,0.00009509604],"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":[0.00004325814,0.00004451347,0.01021523,0.00002491159,0.00006409891,0.0001072633,0.00002605265,0.9825704,0.0003200679,0.00141333,0.0001893069,0.004981592],"study_design_scores_gemma":[0.000001636809,0.00001531724,0.001216291,0.000001787869,0.00000715662,0.000007407487,0.000006756639,0.9984329,0.00005403829,0.0002328568,0.00002084857,0.000002970216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8952952,0.0007868869,0.1005885,0.0006906174,0.00002816816,0.00005015908,0.0001556443,0.0001880283,0.002216816],"genre_scores_gemma":[0.9942957,0.0002327082,0.004432202,0.00002699085,0.0000150022,0.00001363229,0.000147848,0.0000130567,0.0008228731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01918597,"threshold_uncertainty_score":0.03814858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03127706848559983,"score_gpt":0.2656742385558275,"score_spread":0.2343971700702277,"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."}}