{"id":"W2991371684","doi":"10.5539/ijef.v11n12p66","title":"Enhance and Protect Portfolio Returns: A Dynamic Put Spread Optimization","year":2019,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Volatility (finance); Portfolio; Skew; Investment strategy; Computer science; Portfolio optimization; Mathematical optimization; Asset (computer security); Asset allocation; Replicating portfolio; Econometrics; Economics; Financial economics; Finance; Mathematics; Market liquidity","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008110962,0.001022345,0.0008507306,0.0005674341,0.0002130646,0.001234226,0.0007349033,0.001277963,0.002032984],"category_scores_gemma":[0.001491907,0.0004384997,0.0004976765,0.0003518971,0.0005041834,0.001034889,0.0008028211,0.0007288987,0.0002974901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004728508,"about_ca_system_score_gemma":0.0005583628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007385795,"about_ca_topic_score_gemma":0.0005705775,"domain_scores_codex":[0.9997111,0.0000863501,0.00001369657,0.00005376746,0.00008993508,0.00004503077],"domain_scores_gemma":[0.999571,0.0002041718,0.00007975759,0.00004181735,0.00006901621,0.00003427604],"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":[0.00005860071,0.00007900085,0.0005817217,0.00002923419,0.00003509743,0.00006181322,0.00002061163,0.9661147,0.004854521,0.003796479,0.0002179366,0.02415025],"study_design_scores_gemma":[0.00001240412,0.00009622112,0.0001574418,0.000005466233,0.00001523287,0.00002119814,0.000005808301,0.9968585,0.001301712,0.001238426,0.0002829914,0.000004656552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1564817,0.0003847076,0.8325913,0.0002408447,0.00003434825,0.0001168521,0.00004703409,0.0003056884,0.00979746],"genre_scores_gemma":[0.8475983,0.0001625512,0.1481002,0.00009772798,0.00002476978,0.0001039621,0.00006255783,0.00007729216,0.003772553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002032984,"threshold_uncertainty_score":0.006801069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009875708040911509,"score_gpt":0.2110711355757804,"score_spread":0.2011954275348689,"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."}}