{"id":"W4404799444","doi":"10.18280/mmep.111106","title":"Integration of Technical Analysis and Machine Learning to Improve Stock Price Prediction Accuracy","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Stock price; Stock (firearms); Computer science; Machine learning; Technical analysis; Artificial intelligence; Econometrics; Economics; Financial economics; Engineering; Series (stratigraphy); Mechanical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.00359587,0.0001514132,0.0003628903,0.0006026625,0.00005538481,0.0002197307,0.0001339866,0.0000895046,0.00001788717],"category_scores_gemma":[0.004215785,0.0001091772,0.00008477227,0.001281834,0.00002773988,0.00015829,0.0001085714,0.0002942797,0.00000387107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002546459,"about_ca_system_score_gemma":0.000008877251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001737269,"about_ca_topic_score_gemma":0.000001216753,"domain_scores_codex":[0.998258,0.00005996272,0.0006268871,0.0004170167,0.0004585968,0.0001795576],"domain_scores_gemma":[0.9964944,0.003007913,0.00006904073,0.0002135485,0.00009126715,0.0001238624],"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":[0.00001075803,0.00001944381,0.00009300336,0.0002679784,0.00007867171,8.588123e-7,0.001227222,0.9056042,0.01727404,0.003153685,0.000008060692,0.07226203],"study_design_scores_gemma":[0.00004670722,0.00009110378,0.0002372525,0.0002215059,0.0001130187,0.0000107935,0.00002522711,0.9833199,0.000499669,0.01517375,0.0001594473,0.0001016329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1231095,0.0002501805,0.8759045,0.00008094731,0.00006307236,0.0001949194,0.000006350868,0.0001596661,0.0002308961],"genre_scores_gemma":[0.7628748,0.00001757931,0.2368562,0.000002385276,0.00002233052,0.00002776636,0.00000172269,0.00001496549,0.0001822459],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6397653,"threshold_uncertainty_score":0.5046992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07801984187410535,"score_gpt":0.3353881581979408,"score_spread":0.2573683163238355,"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."}}