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1{2 "validation_errors": [],3 "doc_id": "jof_12718",4 "ontology_version": "1.0",5 "prompt_version": "1.0",6 "model": "claude-opus-5-5",7 "effort": "medium",8 "annotated_at": "2026-11-03T23:03:14+00:00",9 "source_sha256": "5f0163609c5bc788d644fc87cc8c47c10364c94c50034cc9b849a89636a457fa",10 "parser": "pymupdf-jof-heuristics-1.3",11 "payload_chars": 24617,12 "usage": {13  "input": 8184,14  "output": 2331,15  "cache_read": 3968,16  "cache_write": 017 },18 "cost_usd": 0.08015,19 "request_id": "req_011Cggq2wa1WmUbXuGjYDRrA",20 "annotation": {21  "genre": "empirical",22  "subfield": "asset_pricing",23  "subfield_secondary": "behavioral_finance",24  "primary_method": "reduced_form_regression",25  "identification": {26   "estimand": "Difference in anomaly (long-minus-short) daily returns on firm-specific information days (earnings announcements, Dow Jones news days) versus noninformation days.",27   "strategy": "Panel regressions of daily stock returns on an aggregate anomaly measure interacted with information-day dummies, with time fixed effects. Competing explanations (dynamic risk, biased expectations, data mining) are distinguished through dynamic beta controls, analyst forecast errors, controls for contemporaneous monthly returns, and out-of-sample anomalies.",28   "key_assumptions": [29    "Time fixed effects absorb daily variation in risk premia",30    "The aggregate anomaly factor adequately captures time-varying beta exposure",31    "Out-of-sample anomalies are unlikely to be products of data mining",32    "Analyst forecast errors are not subject to the joint-hypothesis problem"33   ],34   "threats_addressed": [35    "time-varying risk premia",36    "time-varying betas",37    "day-of-the-week effects",38    "macroeconomic news days",39    "reverse causality",40    "high-volatility days",41    "data mining / spurious anomalies"42   ],43   "causal_language_strength": 2,44   "uses_causal_claim": false45  },46  "abstract_moves": {47   "question": {48    "present": true,49    "sentence_index": 150   },51   "gap": {52    "present": false,53    "sentence_index": -154   },55   "method": {56    "present": true,57    "sentence_index": 258   },59   "results": {60    "present": true,61    "sentence_index": 362   },63   "magnitude": {64    "present": true,65    "sentence_index": 066   },67   "contribution": {68    "present": true,69    "sentence_index": 270   }71  },72  "introduction": {73   "moves": [74    {75     "paragraph_index": 0,76     "move": "motivation"77    },78    {79     "paragraph_index": 0,80     "move": "tension_or_puzzle"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": 2,92     "move": "preview_of_approach"93    },94    {95     "paragraph_index": 2,96     "move": "preview_of_results"97    },98    {99     "paragraph_index": 3,100     "move": "mechanism_or_theory_sketch"101    },102    {103     "paragraph_index": 4,104     "move": "preview_of_results"105    },106    {107     "paragraph_index": 5,108     "move": "preview_of_results"109    },110    {111     "paragraph_index": 6,112     "move": "identification_defense"113    },114    {115     "paragraph_index": 7,116     "move": "mechanism_or_theory_sketch"117    },118    {119     "paragraph_index": 7,120     "move": "preview_of_results"121    },122    {123     "paragraph_index": 8,124     "move": "identification_defense"125    },126    {127     "paragraph_index": 8,128     "move": "preview_of_results"129    },130    {131     "paragraph_index": 9,132     "move": "related_literature_positioning"133    },134    {135     "paragraph_index": 10,136     "move": "contribution_statement"137    },138    {139     "paragraph_index": 11,140     "move": "contribution_statement"141    },142    {143     "paragraph_index": 12,144     "move": "contribution_statement"145    },146    {147     "paragraph_index": 13,148     "move": "roadmap"149    }150   ],151   "contribution_paragraph_index": 10,152   "contribution_types": [153    "new_evidence",154    "new_identification"155   ],156   "roadmap_present": true,157   "literature_positioning_location": "inside_introduction"158  },159  "section_roles": [160   {161    "idx": 0,162    "role": "introduction"163   },164   {165    "idx": 1,166    "role": "data"167   },168   {169    "idx": 2,170    "role": "results"171   },172   {173    "idx": 3,174    "role": "results"175   },176   {177    "idx": 4,178    "role": "results"179   },180   {181    "idx": 5,182    "role": "results"183   },184   {185    "idx": 6,186    "role": "conclusion"187   }188  ],189  "results_presentation": {190   "economic_significance_discussed": true,191   "statistical_significance_language": "unclear",192   "magnitude_statements_in_intro": true193  },194  "robustness": {195   "location": "within_results",196   "types": [197    "alternative_specification",198    "subsample",199    "falsification",200    "other"201   ]202  },203  "tables": {204   "convention_notes": "Table notes state the regression specification, the unit of observation (firm-day), and that the dependent variable is scaled by 100. They describe sample filters (e.g., a $5 price screen) and how anomaly portfolios are built from quintile sorts. Control-variable coefficients are left unreported. In the visible excerpts, the notes do not describe standard errors.",205   "units_stated_in_notes": true,206   "standard_errors_description": "unclear"207  },208  "figures": {209   "uncertainty_shown": "unclear"210  },211  "conclusion": {212   "bounded_claims": true,213   "policy_implications": false,214   "open_questions": true,215   "restates_contribution": true216  },217  "hedging": {218   "level": "moderate",219   "typical_devices": [220    "most consistent with",221    "could be explained by",222    "it is unlikely that",223    "at least partly",224    "likely reflect"225   ]226  },227  "claim_evidence": [228   {229    "claim": "Anomaly returns are about 50% higher on news days and six times higher on earnings announcement days.",230    "evidence_type": "table",231    "evidence_ref": "Table III"232   },233   {234    "claim": "Information-day effects appear on both the long and short sides of anomalies.",235    "evidence_type": "table",236    "evidence_ref": "Table IV"237   },238   {239    "claim": "The effect holds across the four anomaly categories.",240    "evidence_type": "table",241    "evidence_ref": "Table V"242   },243   {244    "claim": "The effects are not explained by day-of-week effects or macroeconomic news days.",245    "evidence_type": "table",246    "evidence_ref": "Table VI"247   },248   {249    "claim": "Dynamic betas rise on earnings days but do not explain the earnings-day anomaly effect.",250    "evidence_type": "table",251    "evidence_ref": "Table VII"252   },253   {254    "claim": "Analyst forecasts are too optimistic for anomaly-shorts and too pessimistic for anomaly-longs.",255    "evidence_type": "table",256    "evidence_ref": "Table VIII"257   },258   {259    "claim": "Out-of-sample anomalies still show information-day effects, making data mining unlikely as the explanation.",260    "evidence_type": "table",261    "evidence_ref": "Table IX, Table X"262   },263   {264    "claim": "Anomaly returns are concentrated within the window around earnings announcements.",265    "evidence_type": "figure",266    "evidence_ref": "Figure 1"267   }268  ],269  "structural_summary": "The paper opens with a long-standing puzzle and three competing explanations for it: risk, mispricing, and data mining. It previews tests that discriminate among them and situates its contributions inside the introduction. The body first establishes a stylized fact, information-day anomaly returns, and runs falsification checks on it. Separate sections then test each explanation in turn. The conclusion favors biased expectations while keeping the dynamic-risk alternative open.",270  "writing_profile_keywords": [271   "horse-race of explanations",272   "stylized facts first",273   "anomaly portfolios",274   "information days",275   "analyst forecast errors",276   "data mining tests",277   "hedged mispricing conclusion"278  ],279  "confidence": 0.85,280  "notes": "The Internet Appendix contains a simple representative-agent model. Statistical significance conventions are not visible in the table-note excerpts."281 },282 "deterministic_checks": {283  "roadmap_regex": true,284  "contribution_regex_in_intro": false,285  "magnitude_in_abstract": true,286  "abstract_n_sentences": 4,287  "intro_n_paragraphs": 14,288  "intro_n_words": 1713,289  "causal_words_in_abstract": 0,290  "hedge_words_in_abstract": 2,291  "stars_mentioned": false,292  "clustered_se_mentioned": false,293  "internet_appendix_mentioned": true,294  "section_roles_rule": {295   "0": "introduction",296   "1": "data",297   "2": "other",298   "3": "results",299   "4": "other",300   "5": "data",301   "6": "conclusion"302  }303 },304 "schema_valid": true,305 "code_version": "feb6c9e-dirty"306}
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