1{2 "validation_errors": [],3 "doc_id": "jof_12833",4 "ontology_version": "1.0",5 "prompt_version": "1.0",6 "model": "claude-opus-5-5",7 "effort": "medium",8 "annotated_at": "2026-11-03T23:09:55+00:00",9 "source_sha256": "a873fab10ec11b01baf0f01eedd915aa344f183707db057fe6a76ce244863423",10 "parser": "pymupdf-jof-heuristics-1.3",11 "payload_chars": 19720,12 "usage": {13 "input": 6904,14 "output": 2073,15 "cache_read": 3968,16 "cache_write": 017 },18 "cost_usd": 0.06987,19 "request_id": "req_011CggqYdTS7WFZxfDL1WwFb",20 "annotation": {21 "genre": "mixed_theory_empirical",22 "subfield": "asset_pricing",23 "subfield_secondary": "behavioral_finance",24 "primary_method": "structural_estimation",25 "identification": {26 "estimand": "Degree of overreaction in analysts' long-term growth expectations (representativeness parameter theta) and its implications for return predictability across LTG-sorted portfolios",27 "strategy": "Portfolio sorts on analyst LTG; forecast-error regressions of the Coibion-Gorodnichenko type; estimation of a diagnostic Kalman filter model by simulated method of moments; cross-sectional tests of kernel-of-truth predictions",28 "key_assumptions": [29 "Analyst LTG forecasts proxy for market beliefs",30 "Earnings follow the assumed latent-fundamentals process",31 "Diagnostic expectations are the correct belief-formation model"32 ],33 "threats_addressed": [34 "Bayesian learning as an alternative explanation",35 "Adaptive/mechanical extrapolation as an alternative explanation"36 ],37 "causal_language_strength": 2,38 "uses_causal_claim": false39 },40 "abstract_moves": {41 "question": {42 "present": true,43 "sentence_index": 044 },45 "gap": {46 "present": false,47 "sentence_index": -148 },49 "method": {50 "present": true,51 "sentence_index": 152 },53 "results": {54 "present": true,55 "sentence_index": 356 },57 "magnitude": {58 "present": false,59 "sentence_index": -160 },61 "contribution": {62 "present": true,63 "sentence_index": 264 }65 },66 "introduction": {67 "moves": [68 {69 "paragraph_index": 0,70 "move": "motivation"71 },72 {73 "paragraph_index": 0,74 "move": "tension_or_puzzle"75 },76 {77 "paragraph_index": 1,78 "move": "preview_of_approach"79 },80 {81 "paragraph_index": 1,82 "move": "contribution_statement"83 },84 {85 "paragraph_index": 1,86 "move": "preview_of_results"87 },88 {89 "paragraph_index": 1,90 "move": "roadmap"91 },92 {93 "paragraph_index": 2,94 "move": "preview_of_results"95 },96 {97 "paragraph_index": 3,98 "move": "preview_of_results"99 },100 {101 "paragraph_index": 3,102 "move": "mechanism_or_theory_sketch"103 },104 {105 "paragraph_index": 4,106 "move": "contribution_statement"107 },108 {109 "paragraph_index": 5,110 "move": "related_literature_positioning"111 }112 ],113 "contribution_paragraph_index": 1,114 "contribution_types": [115 "new_evidence",116 "new_theory_or_model",117 "resolution_of_puzzle"118 ],119 "roadmap_present": true,120 "literature_positioning_location": "inside_introduction"121 },122 "section_roles": [123 {124 "idx": 0,125 "role": "introduction"126 },127 {128 "idx": 1,129 "role": "data"130 },131 {132 "idx": 2,133 "role": "results"134 },135 {136 "idx": 3,137 "role": "theory"138 },139 {140 "idx": 4,141 "role": "theory"142 },143 {144 "idx": 5,145 "role": "results"146 },147 {148 "idx": 6,149 "role": "results"150 },151 {152 "idx": 7,153 "role": "conclusion"154 }155 ],156 "results_presentation": {157 "economic_significance_discussed": true,158 "statistical_significance_language": "unclear",159 "magnitude_statements_in_intro": false160 },161 "robustness": {162 "location": "none_seen",163 "types": []164 },165 "tables": {166 "convention_notes": "Table notes describe the decile portfolio formation on LTG each December from 1981 to 2015, equal weighting, and the variable definitions. Significance is marked with a letter superscript (e.g., 'a' for 1%). Year fixed effects are included but not shown. Model parameters are defined at the quarterly scale.",167 "units_stated_in_notes": true,168 "standard_errors_description": "unclear"169 },170 "figures": {171 "uncertainty_shown": "unclear"172 },173 "conclusion": {174 "bounded_claims": true,175 "policy_implications": false,176 "open_questions": true,177 "restates_contribution": true178 },179 "hedging": {180 "level": "moderate",181 "typical_devices": [182 "'does a reasonable job' fit language",183 "'this is just a start' framing",184 "'in line with' phrasing for predictions"185 ]186 },187 "claim_evidence": [188 {189 "claim": "With 20 extra years of data, low-LTG stocks still outperform high-LTG stocks",190 "evidence_type": "figure",191 "evidence_ref": "Figure 1"192 },193 {194 "claim": "High-LTG firms show fast past EPS growth that slows afterward",195 "evidence_type": "figure",196 "evidence_ref": "Figure 2"197 },198 {199 "claim": "Analyst forecast errors are predictable, consistent with overreaction",200 "evidence_type": "table",201 "evidence_ref": "Table II"202 },203 {204 "claim": "The SMM-estimated diagnostic model reproduces key data patterns",205 "evidence_type": "table",206 "evidence_ref": "Table III; Figure 6"207 },208 {209 "claim": "The LLTG-minus-HLTG return spread widens for firms with more volatile or persistent fundamentals",210 "evidence_type": "table",211 "evidence_ref": "Table IV"212 },213 {214 "claim": "High-LTG group has a fat right tail of top performers, and analysts overstate its frequency",215 "evidence_type": "figure",216 "evidence_ref": "Figures 7-8"217 },218 {219 "claim": "Expectations revert even without news, reflecting true mean reversion in earnings growth",220 "evidence_type": "figure",221 "evidence_ref": "Figure 9"222 }223 ],224 "structural_summary": "Opens by revisiting a known return anomaly and frames it as a puzzle. Lists three innovations: a portable psychological model, a joint account of fundamentals, expectations and returns, and new predictions that set the model apart from adaptive expectations. Data and stylized facts come first. Next, a diagnostic Kalman filter model is developed and mapped to those facts. Overreaction is then quantified with forecast-error regressions and SMM estimation. Kernel-of-truth predictions are tested cross-sectionally. The conclusion restates portability and points to extensions.",225 "writing_profile_keywords": [226 "behavioral asset pricing",227 "representativeness",228 "diagnostic expectations",229 "portfolio sorts",230 "kernel of truth",231 "SMM estimation",232 "analyst forecasts"233 ],234 "confidence": 0.8,235 "notes": "Primary method is ambiguous: the paper mixes portfolio-sort evidence with SMM estimation. Bootstrapped means are mentioned in the figure captions, but whether confidence bands are plotted is not visible."236 },237 "deterministic_checks": {238 "roadmap_regex": false,239 "contribution_regex_in_intro": false,240 "magnitude_in_abstract": false,241 "abstract_n_sentences": 5,242 "intro_n_paragraphs": 6,243 "intro_n_words": 791,244 "causal_words_in_abstract": 0,245 "hedge_words_in_abstract": 1,246 "stars_mentioned": true,247 "clustered_se_mentioned": false,248 "internet_appendix_mentioned": false,249 "section_roles_rule": {250 "0": "introduction",251 "1": "data",252 "2": "other",253 "3": "theory",254 "4": "theory",255 "5": "other",256 "6": "other",257 "7": "conclusion"258 }259 },260 "schema_valid": true,261 "code_version": "87c73cd-dirty"262}