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1{2 "validation_errors": [],3 "doc_id": "jof_12490",4 "ontology_version": "1.0",5 "prompt_version": "1.0",6 "model": "claude-opus-5-5",7 "effort": "medium",8 "annotated_at": "2026-11-03T22:56:40+00:00",9 "source_sha256": "22332f923950a346e53845513bb3c898a690075826c1ff3f9c38cbd237058120",10 "parser": "pymupdf-jof-heuristics-1.3",11 "payload_chars": 24514,12 "usage": {13  "input": 8499,14  "output": 2273,15  "cache_read": 3968,16  "cache_write": 017 },18 "cost_usd": 0.08025,19 "request_id": "req_011CggpXrUs8VJiNd5tJs5TJ",20 "annotation": {21  "genre": "mixed_theory_empirical",22  "subfield": "corporate_finance",23  "subfield_secondary": "corporate cash holdings / governance",24  "primary_method": "instrumental_variables",25  "identification": {26   "estimand": "Effect of financial portfolio size on the share of the portfolio held in risky assets; marginal value of risky versus safe financial assets",27   "strategy": "Panel regressions run by OLS and by 2SLS. Portfolio size is instrumented with unexpected operating cash flow innovations. Value tests regress stock returns and market-to-book on financial asset holdings, following the Faulkender–Wang approach.",28   "key_assumptions": [29    "Unexpected cash flow innovations shift portfolio size but affect portfolio composition only through size",30    "Fair value footnote disclosures accurately capture asset classes"31   ],32   "threats_addressed": [33    "Joint determination of portfolio size and composition (endogeneity)"34   ],35   "causal_language_strength": 3,36   "uses_causal_claim": true37  },38  "abstract_moves": {39   "question": {40    "present": true,41    "sentence_index": 242   },43   "gap": {44    "present": false,45    "sentence_index": -146   },47   "method": {48    "present": true,49    "sentence_index": 250   },51   "results": {52    "present": true,53    "sentence_index": 354   },55   "magnitude": {56    "present": true,57    "sentence_index": 158   },59   "contribution": {60    "present": true,61    "sentence_index": 462   }63  },64  "introduction": {65   "moves": [66    {67     "paragraph_index": 0,68     "move": "motivation"69    },70    {71     "paragraph_index": 0,72     "move": "tension_or_puzzle"73    },74    {75     "paragraph_index": 0,76     "move": "preview_of_approach"77    },78    {79     "paragraph_index": 1,80     "move": "preview_of_approach"81    },82    {83     "paragraph_index": 1,84     "move": "mechanism_or_theory_sketch"85    },86    {87     "paragraph_index": 2,88     "move": "mechanism_or_theory_sketch"89    },90    {91     "paragraph_index": 3,92     "move": "preview_of_results"93    },94    {95     "paragraph_index": 4,96     "move": "identification_defense"97    },98    {99     "paragraph_index": 4,100     "move": "preview_of_results"101    },102    {103     "paragraph_index": 5,104     "move": "preview_of_results"105    },106    {107     "paragraph_index": 5,108     "move": "robustness_mention"109    },110    {111     "paragraph_index": 6,112     "move": "contribution_statement"113    },114    {115     "paragraph_index": 6,116     "move": "related_literature_positioning"117    },118    {119     "paragraph_index": 7,120     "move": "policy_or_broader_implication"121    }122   ],123   "contribution_paragraph_index": 6,124   "contribution_types": [125    "new_evidence",126    "new_data_or_measure",127    "new_theory_or_model"128   ],129   "roadmap_present": false,130   "literature_positioning_location": "both"131  },132  "section_roles": [133   {134    "idx": 0,135    "role": "introduction"136   },137   {138    "idx": 1,139    "role": "data"140   },141   {142    "idx": 2,143    "role": "results"144   },145   {146    "idx": 3,147    "role": "theory"148   },149   {150    "idx": 4,151    "role": "results"152   },153   {154    "idx": 5,155    "role": "results"156   },157   {158    "idx": 6,159    "role": "conclusion"160   },161   {162    "idx": 7,163    "role": "appendix"164   },165   {166    "idx": 8,167    "role": "appendix"168   },169   {170    "idx": 9,171    "role": "appendix"172   },173   {174    "idx": 10,175    "role": "appendix"176   }177  ],178  "results_presentation": {179   "economic_significance_discussed": true,180   "statistical_significance_language": "unclear",181   "magnitude_statements_in_intro": true182  },183  "robustness": {184   "location": "within_results",185   "types": [186    "alternative_measure",187    "alternative_specification"188   ]189  },190  "tables": {191   "convention_notes": "Table notes define variables and write out the regression equations, including the 2SLS first stage. OLS and 2SLS columns are reported side by side. Descriptive tables give aggregate value-weighted figures, with equal-weighted versions in an appendix. Appendix tables reproduce firm disclosures from Google, Apple and Intel as illustrations.",192   "units_stated_in_notes": true,193   "standard_errors_description": "unclear"194  },195  "figures": {196   "uncertainty_shown": "no"197  },198  "conclusion": {199   "bounded_claims": true,200   "policy_implications": true,201   "open_questions": true,202   "restates_contribution": true203  },204  "hedging": {205   "level": "moderate",206   "typical_devices": [207    "'consistent with' framing of results against model predictions",208    "'suggest' for 2SLS estimates",209    "explicit sample limitation acknowledged in conclusion"210   ]211  },212  "claim_evidence": [213   {214    "claim": "Risky assets make up about 40% of firms' financial portfolios and about 6% of book assets.",215    "evidence_type": "table",216    "evidence_ref": "Table I"217   },218   {219    "claim": "The share held in risky assets rises from about 10% to 40% across portfolio-size quintiles, and allocation variation is higher in the top quintiles.",220    "evidence_type": "table",221    "evidence_ref": "Table II"222   },223   {224    "claim": "A one-point increase in portfolio size raises the risky share by about 30 bp (2SLS estimate).",225    "evidence_type": "table",226    "evidence_ref": "Table III"227   },228   {229    "claim": "Permanently reinvested earnings relate positively to total financial assets but insignificantly to risky assets.",230    "evidence_type": "table",231    "evidence_ref": "Table IV"232   },233   {234    "claim": "Poorly governed firms hold more risky assets.",235    "evidence_type": "table",236    "evidence_ref": "Table V"237   },238   {239    "claim": "Risky financial assets are valued at a 13–22% discount relative to safe assets.",240    "evidence_type": "table",241    "evidence_ref": "Table VII"242   },243   {244    "claim": "Constrained firms optimally avoid risky assets; unconstrained firms are at best indifferent to them.",245    "evidence_type": "equation_or_proof",246    "evidence_ref": "Section III; Internet Appendix"247   },248   {249    "claim": "There is no uniform tax incentive to invest in risky financial assets.",250    "evidence_type": "equation_or_proof",251    "evidence_ref": "Appendix C"252   }253  ],254  "structural_summary": "The paper opens with press anecdotes that challenge the assumption that corporate 'cash' is in fact cash. It introduces hand-collected fair value data made available by SFAS 157, then describes portfolio composition. A theory section sketches a precautionary-savings model extended to risky and illiquid assets, with the formal derivation in an internet appendix. Empirical tests follow: univariate sorts, then OLS/2SLS determinants (taxes, agency, CEO traits), then valuation and financing-source tests. The conclusion frames the findings as a largely unregulated shadow asset management industry.",255  "writing_profile_keywords": [256   "hand-collected data",257   "model-consistent predictions",258   "precautionary savings",259   "2SLS",260   "shadow hedge fund framing",261   "policy implications",262   "anecdotal motivation"263  ],264  "confidence": 0.8,265  "notes": "Theory is summarized in the main text and formally derived in an internet appendix. Standard error treatment is not visible in the excerpts. Section II is descriptive results."266 },267 "deterministic_checks": {268  "roadmap_regex": false,269  "contribution_regex_in_intro": false,270  "magnitude_in_abstract": true,271  "abstract_n_sentences": 6,272  "intro_n_paragraphs": 8,273  "intro_n_words": 1027,274  "causal_words_in_abstract": 0,275  "hedge_words_in_abstract": 1,276  "stars_mentioned": false,277  "clustered_se_mentioned": false,278  "internet_appendix_mentioned": true,279  "section_roles_rule": {280   "0": "introduction",281   "1": "data",282   "2": "other",283   "3": "theory",284   "4": "other",285   "5": "other",286   "6": "conclusion",287   "7": "appendix",288   "8": "appendix",289   "9": "appendix",290   "10": "appendix"291  }292 },293 "schema_valid": true,294 "code_version": "feb6c9e-dirty"295}
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