{"id":"W4396797506","doi":"10.1007/s10614-024-10617-1","title":"Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning","year":2024,"lang":"en","type":"article","venue":"Computational Economics","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Exchange rate; Econometrics; Economics; Computer science; Machine learning; Artificial intelligence; Macroeconomics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003693846,0.0002576021,0.0003840015,0.0005518614,0.0002610396,0.0008859846,0.0003772587,0.00006395864,0.001075748],"category_scores_gemma":[0.0008091473,0.0002260689,0.0001336647,0.0003296662,0.0001513123,0.0006312575,0.0002732088,0.0002861552,0.0002494261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004726787,"about_ca_system_score_gemma":0.0002541142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000180729,"about_ca_topic_score_gemma":0.00003070451,"domain_scores_codex":[0.9976196,0.0003725532,0.0007162194,0.0007800103,0.0001890526,0.0003225235],"domain_scores_gemma":[0.9923499,0.006906368,0.0003121083,0.0001808839,0.0001214032,0.0001293696],"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":[0.00019261,0.00001693218,0.008643297,0.00002512339,0.0001546977,0.00003106515,0.002496422,0.7665564,0.00004267717,0.004486627,0.0002492053,0.2171049],"study_design_scores_gemma":[0.0002411995,0.0001085009,0.00114776,0.0001137985,0.00001680032,0.0001630833,0.0005342543,0.9600253,0.00004681803,0.02894614,0.008391717,0.0002646869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7158313,0.0003381524,0.2779009,0.0001817907,0.0008399838,0.0001970159,0.00006656048,0.0001036043,0.004540653],"genre_scores_gemma":[0.91509,0.00001143466,0.08365762,0.0001773801,0.000182669,0.0000179656,0.00002988814,0.00005438007,0.0007786733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2168402,"threshold_uncertainty_score":0.9998374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1159999722831062,"score_gpt":0.3859591718129475,"score_spread":0.2699591995298413,"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."}}