{"id":"W4283382592","doi":"10.3390/jrfm15020089","title":"Statistical Analysis Dow Jones Stock Index—Cumulative Return Gap and Finite Difference Method","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive model; Econometrics; Residual; Stock (firearms); Cumulative distribution function; Computer science; Distributed lag; Statistics; Mathematics; Probability density function; Engineering; Algorithm","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":[0.01067706,0.0001800937,0.000672963,0.001128682,0.0005114592,0.000176049,0.0004269385,0.00004358483,0.0002341516],"category_scores_gemma":[0.006061463,0.0001337043,0.0001706772,0.00171186,0.0001071376,0.0001343601,0.0006821593,0.0005172242,7.992799e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005688145,"about_ca_system_score_gemma":0.00003915397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006197099,"about_ca_topic_score_gemma":0.00004687108,"domain_scores_codex":[0.9951884,0.001672889,0.0009588046,0.0004011922,0.001530986,0.0002476895],"domain_scores_gemma":[0.9922584,0.006056907,0.001028889,0.0002715166,0.0002219211,0.0001623785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004259383,0.00005363708,0.3032091,0.00000659629,0.0001308995,0.0001158727,0.001056961,0.001932885,0.000002111921,0.002495387,0.0006526351,0.689918],"study_design_scores_gemma":[0.000598039,0.0003140878,0.8465346,0.000009382098,0.0005382852,0.00002956805,0.0007391575,0.02573627,0.000001443686,0.1154827,0.009865939,0.0001504824],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2326019,0.0002582876,0.7661235,0.00009233104,0.0003590733,0.0001241128,0.00006174953,0.000005166966,0.0003739001],"genre_scores_gemma":[0.8498215,0.0001867169,0.1491534,0.0001013801,0.00008660191,0.000009088571,0.000001196073,0.000008247439,0.0006318322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6897675,"threshold_uncertainty_score":0.7256575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07388838344407696,"score_gpt":0.3890496115220896,"score_spread":0.3151612280780127,"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."}}