{"id":"W4388293444","doi":"10.22214/ijraset.2023.56419","title":"Predictive Quantitative Financial Analysis for Technology-Driven Investment Decision","year":2023,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Quantitative analysis (chemistry); Investment (military); Context (archaeology); Investment decisions; Asset (computer security); Business; Financial analysis; Financial sector; Asset allocation; Finance; Economics; Computer science","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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.02248991,0.0001288778,0.0003035583,0.02030227,0.00045127,0.0003203686,0.002317243,0.0001833226,0.000004491249],"category_scores_gemma":[0.05482144,0.0001068412,0.00008162529,0.01708682,0.0008833216,0.0002131878,0.0007374377,0.0005905018,0.000008934526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000426721,"about_ca_system_score_gemma":0.0004041108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002369998,"about_ca_topic_score_gemma":0.00001178123,"domain_scores_codex":[0.9957874,0.00003199242,0.0005870891,0.0006413916,0.002252528,0.0006996453],"domain_scores_gemma":[0.9925479,0.004522576,0.0001463661,0.0003011307,0.002350617,0.0001314566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005770554,0.00007042459,0.007977314,0.000008275623,0.0001806901,0.00004275364,0.0004420456,0.06423492,0.0256062,0.5508549,0.002157109,0.3478483],"study_design_scores_gemma":[0.0005359128,0.0002443665,0.0052248,0.00003548641,0.000008547006,0.00002383515,0.0007299049,0.4141147,0.001353013,0.56962,0.008003001,0.0001063776],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4242178,0.00005484104,0.5690522,0.004651575,0.0008942833,0.0006938801,0.00003604071,0.0001374761,0.0002619643],"genre_scores_gemma":[0.8189353,0.00004053247,0.1804807,0.00002736773,0.00006988915,0.0003942253,0.000001987931,0.00001130389,0.00003860328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3947176,"threshold_uncertainty_score":0.9908018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2261822232295471,"score_gpt":0.5214152576014744,"score_spread":0.2952330343719274,"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."}}