{"id":"W4416909509","doi":"10.65521/ijacect.v14i1.562","title":"API Augmented Reinforcement Learning Framework Utilizing LLMs for Enhanced News-Based Stock Portfolio Strategies","year":2025,"lang":"","type":"article","venue":"International Journal on Advanced Computer Engineering and Communication Technology","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Reinforcement learning; Automatic summarization; Portfolio; Stock market; Stock (firearms); Project portfolio management; Benchmark (surveying); Portfolio optimization","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001824612,0.0004915569,0.0006381119,0.002653571,0.000612724,0.0009453632,0.002603658,0.0004255757,0.00005647471],"category_scores_gemma":[0.003266091,0.000501067,0.0002446421,0.001153733,0.0001993435,0.0004655932,0.0007634896,0.001960534,0.000005216742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000436627,"about_ca_system_score_gemma":0.0003078509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004183716,"about_ca_topic_score_gemma":0.0000015325,"domain_scores_codex":[0.9960758,0.0002596864,0.001639275,0.0006639613,0.0008246188,0.0005366318],"domain_scores_gemma":[0.9920059,0.004331965,0.001085506,0.001013514,0.001425852,0.0001372949],"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":[0.0002649761,0.00005989588,0.00006760629,0.00001547091,0.0001830929,0.000003706785,0.00008003956,0.5055314,0.0006566859,0.05180309,0.00007609844,0.4412579],"study_design_scores_gemma":[0.002409315,0.0008276649,0.0002974705,0.002631726,0.00004685844,0.00005315616,0.000804667,0.8947111,0.004164187,0.04580602,0.04777064,0.0004771513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01988525,0.001552472,0.9693949,0.004401943,0.003099703,0.0004942457,0.000003916277,0.0002938921,0.0008736353],"genre_scores_gemma":[0.659692,0.0008101574,0.3386224,0.0003611545,0.0001029444,0.00009135688,0.000009840954,0.00002950428,0.0002805627],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6398068,"threshold_uncertainty_score":0.9997441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02709272405320909,"score_gpt":0.3739594492761544,"score_spread":0.3468667252229453,"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."}}