{"id":"W4328096872","doi":"10.54691/bcpbm.v40i.4402","title":"Using the Nasdaq Index to Predict AAPL Price by Linear Regression Analysis","year":2023,"lang":"en","type":"article","venue":"BCP Business & Management","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Econometrics; Linear regression; Autocorrelation; Statistics; Index (typography); Residual; Regression analysis; Capitalization-weighted index; Simple linear regression; Stock market; Stock market index; Computer science; Mathematics; Algorithm","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":[0.001658493,0.0009082911,0.0004781165,0.002580683,0.0003040864,0.001172883,0.0005761011,0.0004637798,0.002481046],"category_scores_gemma":[0.005708507,0.0002156395,0.0008800799,0.002023235,0.0001839494,0.001371022,0.0004037996,0.0008864651,0.001608637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006072556,"about_ca_system_score_gemma":0.0008509921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01649533,"about_ca_topic_score_gemma":0.01080627,"domain_scores_codex":[0.9988964,0.0002308705,0.00009106909,0.000201142,0.0004913139,0.00008920958],"domain_scores_gemma":[0.9987977,0.0004290057,0.0002034582,0.00008298108,0.0004380499,0.00004878001],"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":[0.0003131482,0.0005372118,0.359175,0.0002563577,0.0005098894,0.0003859315,0.0002257538,0.1913505,0.01038042,0.007789337,0.01483128,0.414245],"study_design_scores_gemma":[0.00002164618,0.0001176287,0.04600805,0.00002004065,0.00005675491,0.0000736205,0.00007784251,0.9459194,0.003451259,0.001938267,0.002265366,0.00005005136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.492975,0.001326581,0.4861067,0.0009531078,0.0002715031,0.0003344144,0.002888679,0.004037959,0.01110604],"genre_scores_gemma":[0.9054621,0.0006782784,0.08522999,0.0001164328,0.0001124015,0.0001713727,0.002830135,0.0001238634,0.005275342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01649533,"threshold_uncertainty_score":0.03279859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1524720170329828,"score_gpt":0.43401476178566,"score_spread":0.2815427447526773,"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."}}