{"id":"W4389479138","doi":"10.47260/jfia/1241","title":"An Empirical Study on Chinese Futures Market Based on Bollinger Bands Strategy and R","year":2023,"lang":"en","type":"article","venue":"Journal of Finance and Investment Analysis","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Futures contract; Investment (military); Transaction cost; Sample (material); Investment strategy; Portfolio; Financial economics; Algorithmic trading; Trading strategy; Economics; Market timing; Investment portfolio; Technical analysis; Test (biology); Portfolio investment; Econometrics; Microeconomics","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":[],"consensus_categories":[],"category_scores_codex":[0.00790551,0.000207815,0.0006791508,0.001705647,0.0002216198,0.0002829632,0.0003344842,0.000068945,0.0001150612],"category_scores_gemma":[0.001532369,0.0001209735,0.0002357437,0.003411297,0.00007951387,0.000256529,0.00003811371,0.0002427807,0.000004127525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002595156,"about_ca_system_score_gemma":0.00006468137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000684823,"about_ca_topic_score_gemma":0.00002255744,"domain_scores_codex":[0.9964752,0.0008150517,0.00078905,0.0004153227,0.001291818,0.0002135361],"domain_scores_gemma":[0.9968523,0.001766482,0.0005754869,0.0004258726,0.00020747,0.0001723818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005866769,0.0004435725,0.9446574,0.000002811463,0.0003040837,0.0001230697,0.0008197434,0.01253554,0.00004156467,0.0000412336,0.01063239,0.02981189],"study_design_scores_gemma":[0.0006409595,0.002250873,0.8894704,0.00001476631,0.00025206,0.000003526436,0.0007897183,0.102643,0.000012733,0.002956273,0.0008446267,0.0001210809],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966359,0.0001357322,0.0003755731,0.0005864976,0.0001269659,0.00009665298,0.000005609562,0.00001074446,0.002026316],"genre_scores_gemma":[0.9971001,0.00006753636,0.001185712,0.0008183664,0.0001587783,0.000004099143,0.00000130368,0.000009129874,0.0006549619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09010746,"threshold_uncertainty_score":0.4933156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09903847540910993,"score_gpt":0.4481918028615035,"score_spread":0.3491533274523936,"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."}}