{"id":"W3199324694","doi":"10.32920/ryerson.14645169.v1","title":"Stock market trend prediction using regression errors","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Econometrics; Stock (firearms); Portfolio; Regression; Stock market; Regression analysis; Factor analysis; Economics; Computer science; Financial economics; Statistics; Mathematics; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01281338,0.0005866751,0.001016361,0.0009995144,0.0003290532,0.0009626019,0.001542292,0.0008299391,0.01317883],"category_scores_gemma":[0.01755908,0.0004205324,0.0006089133,0.001293843,0.0001454473,0.0003351975,0.003661951,0.001190259,0.00002549739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000295869,"about_ca_system_score_gemma":0.0005160316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001416827,"about_ca_topic_score_gemma":0.0001383728,"domain_scores_codex":[0.9892014,0.003058124,0.001707494,0.002124948,0.003362994,0.0005450477],"domain_scores_gemma":[0.9916738,0.003618749,0.001196542,0.002617846,0.0006005829,0.0002924315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003791069,0.0002221141,0.05328175,0.0001359142,0.0002054503,0.0001439426,0.001557819,0.00879625,0.001563363,0.00004706525,0.2121508,0.7215164],"study_design_scores_gemma":[0.0007610954,0.000105414,0.1163633,0.001451646,0.000251008,0.000248388,0.002486293,0.8404355,0.001120746,0.02015899,0.01531347,0.001304172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5783828,0.0005511572,0.2563052,0.0003179924,0.01225967,0.0007132561,0.0001255061,0.0003996445,0.1509448],"genre_scores_gemma":[0.300935,0.00005696224,0.6255506,0.0001974715,0.001205649,0.00007438356,0.000107025,0.0001470157,0.07172591],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8316393,"threshold_uncertainty_score":0.9998246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2668800983293407,"score_gpt":0.4580061281099976,"score_spread":0.1911260297806568,"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."}}