{"id":"W4410597130","doi":"10.3390/jrfm18060288","title":"Incorporating Media Coverage and the Impact of Geopolitical Events for Stock Market Predictions with Machine Learning","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geopolitics; Stock market; Stock (firearms); Media coverage; Computer science; Business; Financial economics; Econometrics; Economics; Artificial intelligence; Geography; Political science; Media studies; Sociology; Archaeology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007782371,0.0001105294,0.0003742031,0.00031713,0.0002728062,0.00006480731,0.0002076134,0.00003748139,0.00001366045],"category_scores_gemma":[0.009506512,0.00005590288,0.0001362451,0.0004056846,0.000168008,0.0001211041,0.0001587106,0.0002503468,9.449581e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003389944,"about_ca_system_score_gemma":0.00007656529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005182422,"about_ca_topic_score_gemma":0.00002549936,"domain_scores_codex":[0.9981548,0.0004410913,0.0006350537,0.0001511049,0.0004665501,0.0001514457],"domain_scores_gemma":[0.9941132,0.004730406,0.000705497,0.0001338758,0.0002516409,0.00006536786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003213522,0.00007182327,0.5315661,0.00003922072,0.0001348203,0.000007520567,0.0005015093,0.001942991,0.000001011684,0.01222639,0.001959511,0.4483356],"study_design_scores_gemma":[0.003268785,0.0004430041,0.8199828,0.0001208169,0.0001871407,0.00002034156,0.0002514181,0.01642519,0.000001430709,0.1575831,0.001651262,0.00006469886],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3802516,0.0002985115,0.6168505,0.0001022227,0.0002527571,0.0003100235,0.00003151548,0.000003857499,0.001898992],"genre_scores_gemma":[0.9870405,0.0001840087,0.01226053,0.00001924639,0.0000798398,0.000007842161,4.880353e-7,0.000005292061,0.0004022869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6067889,"threshold_uncertainty_score":0.9988368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02462435042963248,"score_gpt":0.3423242123523325,"score_spread":0.3176998619227,"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."}}