SPB Git

spb/wp45_uqo

Public
8.1 KB · 287 lines json
1{2 "validation_errors": [],3 "repairs": [4  "dropped figures.figures_note"5 ],6 "doc_id": "jof_12607",7 "ontology_version": "1.0",8 "prompt_version": "1.0",9 "model": "claude-opus-5-5",10 "effort": "medium",11 "annotated_at": "2026-11-03T23:26:10+00:00",12 "source_sha256": "e807a0f0dd6857e588e55355b5a1a286d2796b5a6aec069adcbca353f6f3825a",13 "parser": "pymupdf-jof-heuristics-1.3",14 "payload_chars": 24073,15 "usage": {16  "input": 8447,17  "output": 2312,18  "cache_read": 3968,19  "cache_write": 020 },21 "cost_usd": 0.080822,22 "request_id": "req_011CggpsD3emDcfqvmpDHv9r",23 "annotation": {24  "genre": "methodological_econometric",25  "subfield": "asset_pricing",26  "subfield_secondary": "financial_econometrics",27  "primary_method": "other",28  "identification": {29   "estimand": "Posterior probabilities of competing linear factor pricing models, and Bayes factors for zero-alpha restrictions on test assets and excluded factors",30   "strategy": "Bayesian model comparison: priors on alphas are scaled by a multiple of the market Sharpe ratio, closed-form Bayes factors are derived from the GRS F-statistic, and probabilities are aggregated over all factor subsets, including categorical factor versions",31   "key_assumptions": [32    "Returns are i.i.d. multivariate normal conditional on the factors",33    "Prior on alphas is normal with scale tied to a plausible maximum Sharpe ratio multiple",34    "Uniform prior over models, or over factor versions in the categorical case",35    "Market factor included by default, a requirement relaxed later in the paper"36   ],37   "threats_addressed": [38    "Prior sensitivity, checked across multiple Sharpe ratio multiples",39    "Overfitting from multiple versions of the same factor concept",40    "Models being only approximately true",41    "Out-of-sample performance of the selected models"42   ],43   "causal_language_strength": 1,44   "uses_causal_claim": false45  },46  "abstract_moves": {47   "question": {48    "present": false,49    "sentence_index": -150   },51   "gap": {52    "present": false,53    "sentence_index": -154   },55   "method": {56    "present": true,57    "sentence_index": 058   },59   "results": {60    "present": true,61    "sentence_index": 262   },63   "magnitude": {64    "present": false,65    "sentence_index": -166   },67   "contribution": {68    "present": true,69    "sentence_index": 170   }71  },72  "introduction": {73   "moves": [74    {75     "paragraph_index": 0,76     "move": "motivation"77    },78    {79     "paragraph_index": 0,80     "move": "literature_gap"81    },82    {83     "paragraph_index": 0,84     "move": "contribution_statement"85    },86    {87     "paragraph_index": 1,88     "move": "related_literature_positioning"89    },90    {91     "paragraph_index": 1,92     "move": "literature_gap"93    },94    {95     "paragraph_index": 2,96     "move": "preview_of_approach"97    },98    {99     "paragraph_index": 2,100     "move": "preview_of_results"101    },102    {103     "paragraph_index": 3,104     "move": "preview_of_approach"105    },106    {107     "paragraph_index": 4,108     "move": "preview_of_approach"109    },110    {111     "paragraph_index": 4,112     "move": "preview_of_results"113    }114   ],115   "contribution_paragraph_index": 0,116   "contribution_types": [117    "new_method",118    "new_evidence"119   ],120   "roadmap_present": false,121   "literature_positioning_location": "inside_introduction"122  },123  "section_roles": [124   {125    "idx": 0,126    "role": "introduction"127   },128   {129    "idx": 1,130    "role": "theory"131   },132   {133    "idx": 2,134    "role": "theory"135   },136   {137    "idx": 3,138    "role": "theory"139   },140   {141    "idx": 4,142    "role": "theory"143   },144   {145    "idx": 5,146    "role": "results"147   },148   {149    "idx": 6,150    "role": "robustness"151   },152   {153    "idx": 7,154    "role": "results"155   },156   {157    "idx": 8,158    "role": "conclusion"159   },160   {161    "idx": 9,162    "role": "appendix"163   },164   {165    "idx": 10,166    "role": "appendix"167   },168   {169    "idx": 11,170    "role": "appendix"171   }172  ],173  "results_presentation": {174   "economic_significance_discussed": true,175   "statistical_significance_language": "mixed",176   "magnitude_statements_in_intro": true177  },178  "robustness": {179   "location": "mixed",180   "types": [181    "sensitivity_to_parameters",182    "alternative_sample_period",183    "other"184   ]185  },186  "tables": {187   "convention_notes": "Posterior model probabilities are reported in percent across several prior Sharpe multiples. Factor alphas are annualized with t-statistics in parentheses. Out-of-sample Sharpe ratios are compared across models.",188   "units_stated_in_notes": true,189   "standard_errors_description": "unclear"190  },191  "figures": {192   "uncertainty_shown": "no"193  },194  "conclusion": {195   "bounded_claims": true,196   "policy_implications": false,197   "open_questions": true,198   "restates_contribution": true199  },200  "hedging": {201   "level": "moderate",202   "typical_devices": [203    "Acknowledging that all models are literally false and testing approximate models",204    "Noting that maximum model probabilities stay below 50%",205    "Presenting prior sensitivity across multiple Sharpe multiples",206    "Listing limitations of the i.i.d. normal specification for future work"207   ]208  },209  "claim_evidence": [210   {211    "claim": "Bayesian test of zero alphas can be computed in closed form from the GRS F-statistic",212    "evidence_type": "equation_or_proof",213    "evidence_ref": "Section I; Appendices A-B"214   },215   {216    "claim": "Among FF3 subsets, {Mkt HML} gets about 60% posterior probability and FF3 about 39%",217    "evidence_type": "none_stated",218    "evidence_ref": "Introduction paragraph 2"219   },220   {221    "claim": "Six-factor model {Mkt IA ROE SMB HMLm UMD} has the highest posterior probability over 1972-2015",222    "evidence_type": "table",223    "evidence_ref": "Tables I-II; Figures 1-2"224   },225   {226    "claim": "HMLm is not redundant relative to the other factors in the best model",227    "evidence_type": "figure",228    "evidence_ref": "Figure 3; Table IV"229   },230   {231    "claim": "UMD becomes redundant when HMLm is excluded from the best model",232    "evidence_type": "figure",233    "evidence_ref": "Figure 4"234   },235   {236    "claim": "Top-ranked models achieve higher in-sample Sharpe ratios than CAPM, FF3, FF5 and HXZ",237    "evidence_type": "table",238    "evidence_ref": "Table V"239   },240   {241    "claim": "Absolute tests of the best model are shown on size/momentum and book-to-market/investment portfolios",242    "evidence_type": "figure",243    "evidence_ref": "Figures 5-6"244   }245  ],246  "structural_summary": "The paper opens by motivating the need for a joint model-comparison tool, then builds the method step by step. It first derives an absolute Bayesian alpha test, then relative tests between models, then simultaneous comparison of all factor subsets, and finally a categorical-factor extension. It applies the method to 10 prominent factors with prior-sensitivity analysis and redundancy checks. Descriptive out-of-sample Sharpe ratio evidence and absolute tests of the best model follow. The conclusion restates the method, positions it against related Bayesian work, and lists extensions.",247  "writing_profile_keywords": [248   "Bayesian model comparison",249   "closed-form derivation",250   "factor model horse race",251   "prior sensitivity",252   "method-then-application",253   "appendix derivations"254  ],255  "confidence": 0.8,256  "notes": "Rule-based section roles labeled the empirical sections as theory; they were reassigned to results/robustness. The primary method is a Bayesian econometric procedure, coded as 'other'. Section VI is framed as descriptive evidence on out-of-sample performance."257 },258 "deterministic_checks": {259  "roadmap_regex": false,260  "contribution_regex_in_intro": false,261  "magnitude_in_abstract": false,262  "abstract_n_sentences": 3,263  "intro_n_paragraphs": 5,264  "intro_n_words": 964,265  "causal_words_in_abstract": 0,266  "hedge_words_in_abstract": 0,267  "stars_mentioned": false,268  "clustered_se_mentioned": false,269  "internet_appendix_mentioned": true,270  "section_roles_rule": {271   "0": "introduction",272   "1": "theory",273   "2": "theory",274   "3": "theory",275   "4": "theory",276   "5": "theory",277   "6": "theory",278   "7": "theory",279   "8": "conclusion",280   "9": "appendix",281   "10": "appendix",282   "11": "appendix"283  }284 },285 "schema_valid": true,286 "code_version": "87c73cd-dirty"287}
Files · abe8945aefffa0096080b1470e6c1c6a089f5faa
Menu

Theme