{"id":"W2782453189","doi":"10.5539/ijef.v10n2p28","title":"Impact of Macroeconomic Variables on Karachi Stock Market Returns","year":2018,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economics; Econometrics; Stock exchange; Stock market; Interest rate; Regression analysis; Inflation (cosmology); Exchange rate; Stock (firearms); Variables; Stock market index; Index (typography); Financial economics; Monetary economics; Statistics; Mathematics; Finance; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005684591,0.0002220595,0.0002998664,0.0008988463,0.0002936852,0.00138987,0.000156277,0.0002576483,0.002575508],"category_scores_gemma":[0.004610294,0.0001021508,0.0003785426,0.001283404,0.0003708436,0.0005252808,0.0006291962,0.0005475291,0.0003992949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006142869,"about_ca_system_score_gemma":0.001246063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01184052,"about_ca_topic_score_gemma":0.01107874,"domain_scores_codex":[0.9993463,0.0001415415,0.00007062306,0.00007183166,0.0001902019,0.0001794334],"domain_scores_gemma":[0.9968522,0.001193651,0.001077982,0.0001027049,0.0004758421,0.0002976899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002305108,0.0001231894,0.9828697,0.00008333951,0.0002060793,0.0008174145,0.0004223014,0.0008357674,0.0006814939,0.0004539951,0.0005621915,0.01271403],"study_design_scores_gemma":[0.000005540003,0.00009658183,0.9969912,0.00002025757,0.00007849704,0.0001251357,0.000801219,0.0007006116,0.0002717077,0.0001618498,0.0007358438,0.00001170479],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963363,0.0004988698,0.00005313889,0.0002879356,0.00001566183,0.000005778371,0.0002486693,0.000005899716,0.002547692],"genre_scores_gemma":[0.9991933,0.0002887585,0.00002063131,0.00002661308,0.00001803593,0.00000241003,0.0001390169,0.000001557258,0.0003098027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01184052,"threshold_uncertainty_score":0.02354318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03795833111763607,"score_gpt":0.2580395471082992,"score_spread":0.2200812159906632,"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."}}