1{2 "validation_errors": [],3 "repairs": [4 "dropped hedging.typical_devices_note"5 ],6 "doc_id": "jof_12747",7 "ontology_version": "1.0",8 "prompt_version": "1.0",9 "model": "claude-opus-5-5",10 "effort": "medium",11 "annotated_at": "2026-11-03T23:26:27+00:00",12 "source_sha256": "1003c0785087714d103c43bb64b6d58afe608257d5da747efb905113a59af3fe",13 "parser": "pymupdf-jof-heuristics-1.3",14 "payload_chars": 35706,15 "usage": {16 "input": 11201,17 "output": 2497,18 "cache_read": 3968,19 "cache_write": 020 },21 "cost_usd": 0.095538,22 "request_id": "req_011CggqFZsej3JzRp4mSAUVi",23 "annotation": {24 "genre": "mixed_theory_empirical",25 "subfield": "asset_pricing",26 "subfield_secondary": "household_finance / mutual funds",27 "primary_method": "reduced_form_regression",28 "identification": {29 "estimand": "Sensitivity of aggregate monthly flows into active value and growth funds to option-implied investor preferences for downside protection and upside potential.",30 "strategy": "Time-series regressions of category-level active fund flows on pricing-kernel slopes and Prelec weighting parameters estimated from S&P 500 option prices, with sentiment, return and flow controls. Cross-sectional splits by fund tail properties, Active Share, and retirement versus retail funds support the hypothesized channel.",31 "key_assumptions": [32 "Option-implied pricing kernel reflects risk preferences of mutual fund investors via no-arbitrage",33 "Preference proxies are not merely proxies for investor sentiment",34 "Prelec functional form, or the moneyness range chosen for the PK slopes, captures the relevant tail preferences"35 ],36 "threats_addressed": [37 "investor sentiment confound (Baker-Wurgler control, low correlations)",38 "business cycle / recessions (NBER indicator)",39 "market volatility (VIX control)",40 "benchmark choice for active vs passive comparison",41 "differences in security characteristics held by active vs passive funds"42 ],43 "causal_language_strength": 3,44 "uses_causal_claim": true45 },46 "abstract_moves": {47 "question": {48 "present": false,49 "sentence_index": -150 },51 "gap": {52 "present": true,53 "sentence_index": 054 },55 "method": {56 "present": true,57 "sentence_index": 258 },59 "results": {60 "present": true,61 "sentence_index": 162 },63 "magnitude": {64 "present": false,65 "sentence_index": -166 },67 "contribution": {68 "present": true,69 "sentence_index": 270 }71 },72 "introduction": {73 "moves": [74 {75 "paragraph_index": 0,76 "move": "tension_or_puzzle"77 },78 {79 "paragraph_index": 0,80 "move": "preview_of_results"81 },82 {83 "paragraph_index": 1,84 "move": "preview_of_approach"85 },86 {87 "paragraph_index": 2,88 "move": "preview_of_results"89 },90 {91 "paragraph_index": 3,92 "move": "robustness_mention"93 },94 {95 "paragraph_index": 4,96 "move": "mechanism_or_theory_sketch"97 },98 {99 "paragraph_index": 5,100 "move": "preview_of_approach"101 },102 {103 "paragraph_index": 6,104 "move": "preview_of_results"105 },106 {107 "paragraph_index": 7,108 "move": "preview_of_approach"109 },110 {111 "paragraph_index": 8,112 "move": "robustness_mention"113 },114 {115 "paragraph_index": 9,116 "move": "identification_defense"117 },118 {119 "paragraph_index": 10,120 "move": "preview_of_results"121 },122 {123 "paragraph_index": 11,124 "move": "identification_defense"125 },126 {127 "paragraph_index": 12,128 "move": "preview_of_results"129 },130 {131 "paragraph_index": 13,132 "move": "preview_of_results"133 },134 {135 "paragraph_index": 14,136 "move": "preview_of_results"137 },138 {139 "paragraph_index": 15,140 "move": "related_literature_positioning"141 },142 {143 "paragraph_index": 16,144 "move": "contribution_statement"145 },146 {147 "paragraph_index": 17,148 "move": "policy_or_broader_implication"149 },150 {151 "paragraph_index": 18,152 "move": "roadmap"153 }154 ],155 "contribution_paragraph_index": 16,156 "contribution_types": [157 "new_evidence",158 "new_theory_or_model",159 "resolution_of_puzzle"160 ],161 "roadmap_present": true,162 "literature_positioning_location": "inside_introduction"163 },164 "section_roles": [165 {166 "idx": 0,167 "role": "introduction"168 },169 {170 "idx": 1,171 "role": "data"172 },173 {174 "idx": 2,175 "role": "results"176 },177 {178 "idx": 3,179 "role": "theory"180 },181 {182 "idx": 4,183 "role": "results"184 },185 {186 "idx": 5,187 "role": "results"188 },189 {190 "idx": 6,191 "role": "conclusion"192 }193 ],194 "results_presentation": {195 "economic_significance_discussed": true,196 "statistical_significance_language": "unclear",197 "magnitude_statements_in_intro": true198 },199 "robustness": {200 "location": "mixed",201 "types": [202 "alternative_measure",203 "subsample",204 "alternative_specification"205 ]206 },207 "tables": {208 "convention_notes": "Notes describe sample period, bootstrap design (100 funds, 400 repetitions), variable construction and controls. Summary statistics state units (millions USD, %). Regression tables are described as aggregate flow regressions with listed controls. Tercile sorts are defined in the notes. Standard error treatment is not visible in the excerpts.",209 "units_stated_in_notes": true,210 "standard_errors_description": "unclear"211 },212 "figures": {213 "uncertainty_shown": "unclear"214 },215 "conclusion": {216 "bounded_claims": false,217 "policy_implications": false,218 "open_questions": true,219 "restates_contribution": true220 },221 "hedging": {222 "level": "moderate",223 "typical_devices": [224 "'can help explain' / 'might help explain'",225 "'appear to' / 'appear consistent with'",226 "'to the extent that'",227 "explicit caveat that results need not imply investor segmentation",228 "'suggest' for implications"229 ]230 },231 "claim_evidence": [232 {233 "claim": "Active value funds hedge downside risk better than passive benchmarks (lower co-skewness, downside beta, jump-risk hedging).",234 "evidence_type": "table",235 "evidence_ref": "Tables II-IV"236 },237 {238 "claim": "Active growth funds show higher skewness and positive idiosyncratic skewness relative to benchmarks.",239 "evidence_type": "table",240 "evidence_ref": "Tables II-III"241 },242 {243 "claim": "In an RDEU portfolio model, demand for active value (growth) funds rises with downside-protection (upside-seeking) preference.",244 "evidence_type": "figure",245 "evidence_ref": "Figures 2-3"246 },247 {248 "claim": "Investors would pay reasonable fees for access to active fund portfolios under the model.",249 "evidence_type": "figure",250 "evidence_ref": "Figure 4"251 },252 {253 "claim": "Option-implied downside (upside) preferences explain flows into active large value (growth) funds, with magnitudes comparable to past performance.",254 "evidence_type": "table",255 "evidence_ref": "Tables V-VI"256 },257 {258 "claim": "Flow sensitivity to preferences is stronger for funds with better tail properties and higher Active Share.",259 "evidence_type": "table",260 "evidence_ref": "Tables VIII-XI"261 },262 {263 "claim": "Retirement value funds are more sensitive to downside preferences; retail growth funds more sensitive to upside preferences.",264 "evidence_type": "table",265 "evidence_ref": "Table XII"266 }267 ],268 "structural_summary": "Opens with the puzzle of large active-fund assets despite mediocre performance. First documents distributional differences between active and passive funds using bootstrapped returns and several benchmarks. Then builds a rank-dependent expected utility portfolio model showing these differences matter to tail-sensitive investors. Next constructs option-implied preference proxies and runs time-series flow regressions with controls. Cross-sectional splits sharpen the channel. Closes by restating the findings and linking them to debates on managerial skill and non-EU preferences.",269 "writing_profile_keywords": [270 "puzzle-driven",271 "model-then-test",272 "bootstrap comparisons",273 "option-implied preferences",274 "cross-sectional validation",275 "alternative proxies",276 "literature complement"277 ],278 "confidence": 0.8,279 "notes": "Mixed paper: a numerical theory section sits inside the main text, followed by empirical tests. Standard error conventions and significance notation are not visible in the excerpts. The Internet Appendix holds robustness checks."280 },281 "deterministic_checks": {282 "roadmap_regex": true,283 "contribution_regex_in_intro": true,284 "magnitude_in_abstract": false,285 "abstract_n_sentences": 5,286 "intro_n_paragraphs": 19,287 "intro_n_words": 2771,288 "causal_words_in_abstract": 1,289 "hedge_words_in_abstract": 1,290 "stars_mentioned": false,291 "clustered_se_mentioned": false,292 "internet_appendix_mentioned": true,293 "section_roles_rule": {294 "0": "introduction",295 "1": "data",296 "2": "other",297 "3": "other",298 "4": "results",299 "5": "other",300 "6": "conclusion"301 }302 },303 "schema_valid": true,304 "code_version": "87c73cd-dirty"305}