{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009213687,0.0004766969,0.0003058591,0.0006336062,0.0001845807,0.001072882,0.0004795237,0.0005901864,0.001386226],"category_scores_gemma":[0.006990314,0.0003250882,0.0003887691,0.0003960597,0.0003536587,0.001405407,0.0004206276,0.0009963955,0.0002501539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003664421,"about_ca_system_score_gemma":0.000335045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002996614,"about_ca_topic_score_gemma":0.002634044,"domain_scores_codex":[0.9998528,0.00007080461,0.000009957593,0.00002843661,0.0000258635,0.00001208069],"domain_scores_gemma":[0.9978278,0.001721879,0.0001799872,0.0001476492,0.00009617362,0.00002641409],"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":[0.00006272575,0.00009658738,0.008695128,0.00006138191,0.00008541334,0.0001313819,0.000136327,0.8905603,0.002019124,0.03580076,0.001328915,0.06102189],"study_design_scores_gemma":[0.000002959611,0.000002868553,0.0003937658,0.000003777647,0.000003744404,0.000003794277,0.000004739482,0.9871053,0.0001475867,0.01223796,0.00009149458,0.000002061312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.320222,0.0006224094,0.6732673,0.00112125,0.0001348262,0.00003069359,0.0003062036,0.000586439,0.003708863],"genre_scores_gemma":[0.9606158,0.0002867075,0.03781911,0.00004715321,0.0001094541,0.00002331448,0.0002051114,0.00003028592,0.0008629346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002996614,"threshold_uncertainty_score":0.005958378,"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."}}