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1{2 "validation_errors": [],3 "doc_id": "jof_12591",4 "ontology_version": "1.0",5 "prompt_version": "1.0",6 "model": "claude-opus-5-5",7 "effort": "medium",8 "annotated_at": "2026-11-03T23:00:27+00:00",9 "source_sha256": "2743beca9bf99d3d89e8544bcb65a6927073ab432d7a11fef527df1bfea02d55",10 "parser": "pymupdf-jof-heuristics-1.3",11 "payload_chars": 20247,12 "usage": {13  "input": 7420,14  "output": 2013,15  "cache_read": 3968,16  "cache_write": 017 },18 "cost_usd": 0.070734,19 "request_id": "req_011CggppjiYreP4Y1q26AeVm",20 "annotation": {21  "genre": "mixed_theory_empirical",22  "subfield": "financial_intermediation_banking",23  "subfield_secondary": "macro_finance",24  "primary_method": "reduced_form_regression",25  "identification": {26   "estimand": "Predictive association between ex-ante bank LMI and crisis outcomes (stock crash probability, borrowing from Fed facilities/TARP); aggregate liquidity mismatch over time",27   "strategy": "Construct a theory-based measure from balance sheets weighted by market-based liquidity factors; validate it through time-series aggregates, a stress test, and cross-sectional probability regressions on pre-crisis LMI with controls; compare against Basel LCR/NSFR and the Berger-Bouwman measure",28   "key_assumptions": [29    "Repo haircuts and the OIS-Tbill spread proxy asset and funding liquidity",30    "Calibrated liquidity weights (e.g., kappa) are appropriate",31    "Measuring LMI ex ante makes it predetermined relative to crisis outcomes"32   ],33   "threats_addressed": [34    "Fixed versus time-varying weights",35    "Comparison with alternative liquidity measures",36    "Bank characteristic controls"37   ],38   "causal_language_strength": 1,39   "uses_causal_claim": false40  },41  "abstract_moves": {42   "question": {43    "present": true,44    "sentence_index": 045   },46   "gap": {47    "present": false,48    "sentence_index": -149   },50   "method": {51    "present": true,52    "sentence_index": 053   },54   "results": {55    "present": true,56    "sentence_index": 357   },58   "magnitude": {59    "present": true,60    "sentence_index": 161   },62   "contribution": {63    "present": true,64    "sentence_index": 465   }66  },67  "introduction": {68   "moves": [69    {70     "paragraph_index": 0,71     "move": "motivation"72    },73    {74     "paragraph_index": 0,75     "move": "literature_gap"76    },77    {78     "paragraph_index": 0,79     "move": "related_literature_positioning"80    },81    {82     "paragraph_index": 1,83     "move": "preview_of_approach"84    },85    {86     "paragraph_index": 1,87     "move": "preview_of_results"88    },89    {90     "paragraph_index": 2,91     "move": "preview_of_results"92    },93    {94     "paragraph_index": 2,95     "move": "policy_or_broader_implication"96    },97    {98     "paragraph_index": 3,99     "move": "related_literature_positioning"100    },101    {102     "paragraph_index": 3,103     "move": "contribution_statement"104    },105    {106     "paragraph_index": 3,107     "move": "preview_of_results"108    },109    {110     "paragraph_index": 4,111     "move": "roadmap"112    }113   ],114   "contribution_paragraph_index": 3,115   "contribution_types": [116    "new_data_or_measure",117    "new_theory_or_model",118    "new_evidence"119   ],120   "roadmap_present": true,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": "empirical_design"135   },136   {137    "idx": 3,138    "role": "results"139   },140   {141    "idx": 4,142    "role": "results"143   },144   {145    "idx": 5,146    "role": "conclusion"147   }148  ],149  "results_presentation": {150   "economic_significance_discussed": true,151   "statistical_significance_language": "unclear",152   "magnitude_statements_in_intro": true153  },154  "robustness": {155   "location": "within_results",156   "types": [157    "alternative_measure",158    "sensitivity_to_parameters",159    "alternative_sample_period"160   ]161  },162  "tables": {163   "convention_notes": "Table notes state the sample, the regression equation in probability form, dependent-variable definitions, and the quarters covered. Stress-test table reports scenarios at 1σ, 2σ and 3σ shocks. Standard errors are not visible in the excerpts.",164   "units_stated_in_notes": true,165   "standard_errors_description": "unclear"166  },167  "figures": {168   "uncertainty_shown": "no"169  },170  "conclusion": {171   "bounded_claims": true,172   "policy_implications": true,173   "open_questions": true,174   "restates_contribution": true175  },176  "hedging": {177   "level": "moderate",178   "typical_devices": [179    "acknowledging the measure is not a finished product",180    "noting calibration choices drive results",181    "suggesting future data to pin down weights"182   ]183  },184  "claim_evidence": [185   {186    "claim": "Aggregate LMI falls from about +$4 trillion before the crisis to about -$6 trillion in 2008",187    "evidence_type": "figure",188    "evidence_ref": "Figure 4"189   },190   {191    "claim": "A stress test based on LMI signals banking fragility in early 2007",192    "evidence_type": "table",193    "evidence_ref": "Table III, Figure 9"194   },195   {196    "claim": "Banks with lower pre-crisis LMI were more likely to borrow from Fed facilities and TARP",197    "evidence_type": "table",198    "evidence_ref": "Table VI"199   },200   {201    "claim": "Lower ex-ante LMI predicts higher stock crash probability during the crisis",202    "evidence_type": "table",203    "evidence_ref": "Table VII"204   },205   {206    "claim": "With fixed liquidity weights, LMI varies little between normal and crisis periods",207    "evidence_type": "figure",208    "evidence_ref": "Figure 8"209   },210   {211    "claim": "Federal Reserve liquidity injections correlate with the increase in LMI in 2008",212    "evidence_type": "figure",213    "evidence_ref": "Figure 6"214   }215  ],216  "structural_summary": "The paper opens with liquidity crises and post-crisis regulation, then identifies the lack of an agreed liquidity measurement framework. It derives the LMI from a recursive theoretical framework and builds the empirical measure using haircut-based and spread-based liquidity weights. It then validates the measure on macro criteria (aggregate time series, decomposition, stress test) and on micro criteria (cross-sectional prediction of crash risk and government borrowing), benchmarking against the Basel ratios and Berger-Bouwman. The conclusion restates the measure's advantages and notes that the calibration is still open to refinement.",217  "writing_profile_keywords": [218   "measurement paper",219   "macroprudential",220   "stress test",221   "benchmark comparison",222   "theory-grounded index",223   "validation criteria"224  ],225  "confidence": 0.8,226  "notes": "Validation is predictive rather than causal. How statistical significance is reported is not visible in the excerpts."227 },228 "deterministic_checks": {229  "roadmap_regex": true,230  "contribution_regex_in_intro": true,231  "magnitude_in_abstract": false,232  "abstract_n_sentences": 7,233  "intro_n_paragraphs": 5,234  "intro_n_words": 884,235  "causal_words_in_abstract": 0,236  "hedge_words_in_abstract": 0,237  "stars_mentioned": false,238  "clustered_se_mentioned": false,239  "internet_appendix_mentioned": false,240  "section_roles_rule": {241   "0": "introduction",242   "1": "theory",243   "2": "empirical_design",244   "3": "other",245   "4": "other",246   "5": "conclusion"247  }248 },249 "schema_valid": true,250 "code_version": "feb6c9e-dirty"251}
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