{"id":"W2098806386","doi":"10.1002/for.986","title":"Non‐linear, non‐parametric, non‐fundamental exchange rate forecasting","year":2006,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada; Lakehead University","funders":"University of British Columbia; Lakehead University","keywords":"Random walk; Mean squared error; Linear model; Exchange rate; Parametric statistics; Mathematics; Econometrics; Statistics; Computer science; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.02667341,0.0006524907,0.001481877,0.002433702,0.0007098903,0.000721356,0.001842867,0.0002912585,0.0005062389],"category_scores_gemma":[0.02098538,0.0005072031,0.0009304675,0.004173057,0.000232982,0.001400331,0.0005711314,0.00118745,0.00009783766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003947689,"about_ca_system_score_gemma":0.0003362256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001359122,"about_ca_topic_score_gemma":0.00006148523,"domain_scores_codex":[0.9901366,0.0005997885,0.003995101,0.0008328951,0.003128791,0.001306779],"domain_scores_gemma":[0.9829635,0.008981432,0.005091217,0.0007471831,0.001739373,0.0004773199],"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":[0.001061748,0.0005811527,0.1774347,0.0001540902,0.0002401697,0.001793469,0.001770049,0.03327526,0.008517159,0.00004038524,0.04559403,0.7295378],"study_design_scores_gemma":[0.00496105,0.001729014,0.06294987,0.0009344164,0.0002388367,0.005227418,0.001734445,0.8886695,0.008722804,0.01051473,0.01298265,0.001335267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8722522,0.000267112,0.1016999,0.0002150826,0.003093243,0.0004248553,0.00001764686,0.00004053879,0.02198941],"genre_scores_gemma":[0.8496812,0.000007799463,0.1448139,0.0001516654,0.002668124,0.00001019538,0.000003699902,0.00009255012,0.002570919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8553942,"threshold_uncertainty_score":0.999738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1667260819266446,"score_gpt":0.3864074929030265,"score_spread":0.2196814109763819,"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."}}