{"text": "{\"genre\":\"empirical\",\"subfield\":\"asset_pricing\",\"subfield_secondary\":\"financial_intermediation_banking (sell-side analysts as information intermediaries)\",\"primary_method\":\"reduced_form_regression\",\"identification\":{\"estimand\":\"How much analyst revisions move stock prices (two-day CAR and probability of an influential revision), plus forecast precision and analyst activity, in bad times versus good times\",\"strategy\":\"Panel and probit regressions of announcement CARs and influential-revision indicators on bad-times dummies (crises, NBER recessions, high policy uncertainty) with firm, analyst and recommendation controls; cross-sectional interactions with firm opacity; competing hypotheses tested against each other\",\"key_assumptions\":[\"Bad-times dummies capture exogenous increases in macro uncertainty\",\"Controls absorb differences in firm and analyst characteristics across periods\",\"The DGTW characteristics-matched benchmark correctly measures abnormal returns\"],\"threats_addressed\":[\"Firm-specific vs marketwide uncertainty driving results\",\"Market reacting more to all firm news in bad times\",\"Investor overreaction or price pressure (checked via post-revision drift and reversals)\",\"Conflicts of interest\",\"Alternative specifications and samples\"],\"causal_language_strength\":2,\"uses_causal_claim\":false},\"abstract_moves\":{\"question\":{\"present\":true,\"sentence_index\":1},\"gap\":{\"present\":true,\"sentence_index\":1},\"method\":{\"present\":false,\"sentence_index\":-1},\"results\":{\"present\":true,\"sentence_index\":2},\"magnitude\":{\"present\":false,\"sentence_index\":-1},\"contribution\":{\"present\":false,\"sentence_index\":-1}},\"introduction\":{\"moves\":[{\"paragraph_index\":0,\"move\":\"literature_gap\"},{\"paragraph_index\":0,\"move\":\"tension_or_puzzle\"},{\"paragraph_index\":0,\"move\":\"preview_of_results\"},{\"paragraph_index\":1,\"move\":\"mechanism_or_theory_sketch\"},{\"paragraph_index\":1,\"move\":\"tension_or_puzzle\"},{\"paragraph_index\":2,\"move\":\"preview_of_results\"},{\"paragraph_index\":3,\"move\":\"preview_of_approach\"},{\"paragraph_index\":3,\"move\":\"preview_of_results\"},{\"paragraph_index\":4,\"move\":\"preview_of_results\"},{\"paragraph_index\":5,\"move\":\"preview_of_results\"},{\"paragraph_index\":6,\"move\":\"preview_of_results\"},{\"paragraph_index\":7,\"move\":\"related_literature_positioning\"},{\"paragraph_index\":7,\"move\":\"contribution_statement\"}],\"contribution_paragraph_index\":7,\"contribution_types\":[\"new_evidence\",\"new_data_or_measure\"],\"roadmap_present\":false,\"literature_positioning_location\":\"inside_introduction\"},\"section_roles\":[{\"idx\":0,\"role\":\"introduction\"},{\"idx\":1,\"role\":\"theory\"},{\"idx\":2,\"role\":\"results\"},{\"idx\":3,\"role\":\"robustness\"},{\"idx\":4,\"role\":\"results\"},{\"idx\":5,\"role\":\"results\"},{\"idx\":6,\"role\":\"conclusion\"}],\"results_presentation\":{\"economic_significance_discussed\":true,\"statistical_significance_language\":\"unclear\",\"magnitude_statements_in_intro\":false},\"robustness\":{\"location\":\"mixed\",\"types\":[\"alternative_specification\",\"subsample\",\"alternative_measure\",\"falsification\",\"other\"]},\"tables\":{\"convention_notes\":\"Table notes state variable definitions, sample periods and units (CARs and marginal effects in percent). Probit tables report marginal effects. Influential revisions are defined by a 1.96 threshold relative to expected volatility. Bad-times measures are defined once and cross-referenced across tables.\",\"units_stated_in_notes\":true,\"standard_errors_description\":\"unclear\"},\"figures\":{\"uncertainty_shown\":\"unclear\"},\"conclusion\":{\"bounded_claims\":true,\"policy_implications\":false,\"open_questions\":false,\"restates_contribution\":true},\"hedging\":{\"level\":\"moderate\",\"typical_devices\":[\"'consistent with' phrasing\",\"'likely explain'\",\"'might' for mechanisms\",\"'some evidence' for spillovers\",\"explicitly nonmutually exclusive hypotheses\"]},\"claim_evidence\":[{\"claim\":\"Recommendation changes have a larger two-day price impact and are more often influential in bad times\",\"evidence_type\":\"table\",\"evidence_ref\":\"Tables II-IV, Figure 1\"},{\"claim\":\"Earnings forecast revisions are more likely to be influential in bad times\",\"evidence_type\":\"table\",\"evidence_ref\":\"Table V\"},{\"claim\":\"Uncertainty measures such as VIX and implied volatility rise sharply in bad times\",\"evidence_type\":\"table\",\"evidence_ref\":\"Table I\"},{\"claim\":\"Traditional absolute forecast errors rise in bad times, but errors scaled by volatility fall\",\"evidence_type\":\"table\",\"evidence_ref\":\"Table VII\"},{\"claim\":\"The increase in analyst impact in bad times is larger for opaque firms\",\"evidence_type\":\"table\",\"evidence_ref\":\"Table VIII\"},{\"claim\":\"Analysts produce more forecasts and longer reports in bad times, consistent with greater effort\",\"evidence_type\":\"table\",\"evidence_ref\":\"Tables IX-XI\"},{\"claim\":\"Increased crisis impact of downgrades corresponds to roughly $80 million in market value per typical change\",\"evidence_type\":\"descriptive_statistic\",\"evidence_ref\":\"Conclusion, Table I Panel C\"},{\"claim\":\"Overreaction cannot explain results since post-revision drift shows no reversals\",\"evidence_type\":\"none_stated\",\"evidence_ref\":\"Section V.E\"}],\"structural_summary\":\"The introduction states a tension: higher uncertainty in bad times could make analyst output more valuable or make analysts' jobs harder. It then previews the main finding and a Bayesian argument motivating uncertainty-scaled precision, and walks through results for five competing explanations before positioning against prior work. The body proceeds in order: a hypothesis section with three 'less impact' and five 'more impact' hypotheses; main results on price impact using several bad-times proxies; a robustness section; forecast-precision analysis; and hypothesis-by-hypothesis tests of mechanisms. The conclusion restates findings and adds an economic magnitude.\",\"writing_profile_keywords\":[\"competing hypotheses\",\"horse-race of explanations\",\"bad-times proxies\",\"event-study CARs\",\"probit marginal effects\",\"uncertainty-scaled precision\",\"mechanism sections\"],\"confidence\":0.8,\"notes\":\"No roadmap paragraph is visible in the introduction excerpts. The second hypothesis (analyst effort) appears to be truncated from the excerpted introduction paragraphs. How significance is presented is not visible in the excerpts.\"}", "json": {"genre": "empirical", "subfield": "asset_pricing", "subfield_secondary": "financial_intermediation_banking (sell-side analysts as information intermediaries)", "primary_method": "reduced_form_regression", "identification": {"estimand": "How much analyst revisions move stock prices (two-day CAR and probability of an influential revision), plus forecast precision and analyst activity, in bad times versus good times", "strategy": "Panel and probit regressions of announcement CARs and influential-revision indicators on bad-times dummies (crises, NBER recessions, high policy uncertainty) with firm, analyst and recommendation controls; cross-sectional interactions with firm opacity; competing hypotheses tested against each other", "key_assumptions": ["Bad-times dummies capture exogenous increases in macro uncertainty", "Controls absorb differences in firm and analyst characteristics across periods", "The DGTW characteristics-matched benchmark correctly measures abnormal returns"], "threats_addressed": ["Firm-specific vs marketwide uncertainty driving results", "Market reacting more to all firm news in bad times", "Investor overreaction or price pressure (checked via post-revision drift and reversals)", "Conflicts of interest", "Alternative specifications and samples"], "causal_language_strength": 2, "uses_causal_claim": false}, "abstract_moves": {"question": {"present": true, "sentence_index": 1}, "gap": {"present": true, "sentence_index": 1}, "method": {"present": false, "sentence_index": -1}, "results": {"present": true, "sentence_index": 2}, "magnitude": {"present": false, "sentence_index": -1}, "contribution": {"present": false, "sentence_index": -1}}, "introduction": {"moves": [{"paragraph_index": 0, "move": "literature_gap"}, {"paragraph_index": 0, "move": "tension_or_puzzle"}, {"paragraph_index": 0, "move": "preview_of_results"}, {"paragraph_index": 1, "move": "mechanism_or_theory_sketch"}, {"paragraph_index": 1, "move": "tension_or_puzzle"}, {"paragraph_index": 2, "move": "preview_of_results"}, {"paragraph_index": 3, "move": "preview_of_approach"}, {"paragraph_index": 3, "move": "preview_of_results"}, {"paragraph_index": 4, "move": "preview_of_results"}, {"paragraph_index": 5, "move": "preview_of_results"}, {"paragraph_index": 6, "move": "preview_of_results"}, {"paragraph_index": 7, "move": "related_literature_positioning"}, {"paragraph_index": 7, "move": "contribution_statement"}], "contribution_paragraph_index": 7, "contribution_types": ["new_evidence", "new_data_or_measure"], "roadmap_present": false, "literature_positioning_location": "inside_introduction"}, "section_roles": [{"idx": 0, "role": "introduction"}, {"idx": 1, "role": "theory"}, {"idx": 2, "role": "results"}, {"idx": 3, "role": "robustness"}, {"idx": 4, "role": "results"}, {"idx": 5, "role": "results"}, {"idx": 6, "role": "conclusion"}], "results_presentation": {"economic_significance_discussed": true, "statistical_significance_language": "unclear", "magnitude_statements_in_intro": false}, "robustness": {"location": "mixed", "types": ["alternative_specification", "subsample", "alternative_measure", "falsification", "other"]}, "tables": {"convention_notes": "Table notes state variable definitions, sample periods and units (CARs and marginal effects in percent). Probit tables report marginal effects. Influential revisions are defined by a 1.96 threshold relative to expected volatility. Bad-times measures are defined once and cross-referenced across tables.", "units_stated_in_notes": true, "standard_errors_description": "unclear"}, "figures": {"uncertainty_shown": "unclear"}, "conclusion": {"bounded_claims": true, "policy_implications": false, "open_questions": false, "restates_contribution": true}, "hedging": {"level": "moderate", "typical_devices": ["'consistent with' phrasing", "'likely explain'", "'might' for mechanisms", "'some evidence' for spillovers", "explicitly nonmutually exclusive hypotheses"]}, "claim_evidence": [{"claim": "Recommendation changes have a larger two-day price impact and are more often influential in bad times", "evidence_type": "table", "evidence_ref": "Tables II-IV, Figure 1"}, {"claim": "Earnings forecast revisions are more likely to be influential in bad times", "evidence_type": "table", "evidence_ref": "Table V"}, {"claim": "Uncertainty measures such as VIX and implied volatility rise sharply in bad times", "evidence_type": "table", "evidence_ref": "Table I"}, {"claim": "Traditional absolute forecast errors rise in bad times, but errors scaled by volatility fall", "evidence_type": "table", "evidence_ref": "Table VII"}, {"claim": "The increase in analyst impact in bad times is larger for opaque firms", "evidence_type": "table", "evidence_ref": "Table VIII"}, {"claim": "Analysts produce more forecasts and longer reports in bad times, consistent with greater effort", "evidence_type": "table", "evidence_ref": "Tables IX-XI"}, {"claim": "Increased crisis impact of downgrades corresponds to roughly $80 million in market value per typical change", "evidence_type": "descriptive_statistic", "evidence_ref": "Conclusion, Table I Panel C"}, {"claim": "Overreaction cannot explain results since post-revision drift shows no reversals", "evidence_type": "none_stated", "evidence_ref": "Section V.E"}], "structural_summary": "The introduction states a tension: higher uncertainty in bad times could make analyst output more valuable or make analysts' jobs harder. It then previews the main finding and a Bayesian argument motivating uncertainty-scaled precision, and walks through results for five competing explanations before positioning against prior work. The body proceeds in order: a hypothesis section with three 'less impact' and five 'more impact' hypotheses; main results on price impact using several bad-times proxies; a robustness section; forecast-precision analysis; and hypothesis-by-hypothesis tests of mechanisms. The conclusion restates findings and adds an economic magnitude.", "writing_profile_keywords": ["competing hypotheses", "horse-race of explanations", "bad-times proxies", "event-study CARs", "probit marginal effects", "uncertainty-scaled precision", "mechanism sections"], "confidence": 0.8, "notes": "No roadmap paragraph is visible in the introduction excerpts. The second hypothesis (analyst effort) appears to be truncated from the excerpted introduction paragraphs. How significance is presented is not visible in the excerpts."}, "model": "claude-opus-5-5", "stop_reason": "end_turn", "latency_s": 19.33, "usage": {"input": 8143, "output": 2327, "cache_read": 3968, "cache_write": 0}, "cost_usd": 0.079906, "request_id": "req_011Cggpxe6S8PS4aD3CqPySw", "cached": false, "ts": "2026-11-03T23:02:18+00:00", "effort": "medium"}