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Abnormal outcomes are measured against a 200-day window of days without economic news. Cross-sectional sample splits use proxies for retail ownership.",28 "key_assumptions": [29 "Sensational news events are exogenous to the stock market and carry no fundamental information",30 "These events distract noise traders more than informed speculators or market makers",31 "Estimation days without economic news provide a valid counterfactual"32 ],33 "threats_addressed": [34 "confounding economic news",35 "reverse causality",36 "event list construction / keyword choice",37 "event clustering",38 "distraction of insiders or market makers",39 "investor sentiment"40 ],41 "causal_language_strength": 3,42 "uses_causal_claim": true43 },44 "abstract_moves": {45 "question": {46 "present": true,47 "sentence_index": 048 },49 "gap": {50 "present": false,51 "sentence_index": -152 },53 "method": {54 "present": true,55 "sentence_index": 156 },57 "results": {58 "present": true,59 "sentence_index": 260 },61 "magnitude": {62 "present": false,63 "sentence_index": -164 },65 "contribution": {66 "present": true,67 "sentence_index": 368 }69 },70 "introduction": {71 "moves": [72 {73 "paragraph_index": 0,74 "move": "literature_gap"75 },76 {77 "paragraph_index": 0,78 "move": "motivation"79 },80 {81 "paragraph_index": 0,82 "move": "preview_of_results"83 },84 {85 "paragraph_index": 0,86 "move": "identification_defense"87 },88 {89 "paragraph_index": 1,90 "move": "preview_of_results"91 },92 {93 "paragraph_index": 1,94 "move": "robustness_mention"95 },96 {97 "paragraph_index": 1,98 "move": "identification_defense"99 },100 {101 "paragraph_index": 2,102 "move": "preview_of_results"103 },104 {105 "paragraph_index": 2,106 "move": "mechanism_or_theory_sketch"107 },108 {109 "paragraph_index": 3,110 "move": "preview_of_results"111 },112 {113 "paragraph_index": 3,114 "move": "mechanism_or_theory_sketch"115 },116 {117 "paragraph_index": 4,118 "move": "roadmap"119 }120 ],121 "contribution_paragraph_index": 0,122 "contribution_types": [123 "new_evidence",124 "new_identification"125 ],126 "roadmap_present": true,127 "literature_positioning_location": "separate_section"128 },129 "section_roles": [130 {131 "idx": 0,132 "role": "introduction"133 },134 {135 "idx": 1,136 "role": "literature"137 },138 {139 "idx": 2,140 "role": "data"141 },142 {143 "idx": 3,144 "role": "results"145 },146 {147 "idx": 4,148 "role": "results"149 },150 {151 "idx": 5,152 "role": "robustness"153 },154 {155 "idx": 6,156 "role": "results"157 },158 {159 "idx": 7,160 "role": "conclusion"161 },162 {163 "idx": 8,164 "role": "appendix"165 }166 ],167 "results_presentation": {168 "economic_significance_discussed": true,169 "statistical_significance_language": "mixed",170 "magnitude_statements_in_intro": true171 },172 "robustness": {173 "location": "main_text_section",174 "types": [175 "alternative_measure",176 "alternative_specification",177 "subsample",178 "other"179 ]180 },181 "tables": {182 "convention_notes": "Event-study tables report abnormal values with t-statistics shown below each estimate. Notes state the sample period, the number of distraction events and the estimation window, and sort stocks into terciles. Variables are defined in the Appendix.",183 "units_stated_in_notes": true,184 "standard_errors_description": "unclear"185 },186 "figures": {187 "uncertainty_shown": "mixed"188 },189 "conclusion": {190 "bounded_claims": true,191 "policy_implications": false,192 "open_questions": true,193 "restates_contribution": true194 },195 "hedging": {196 "level": "moderate",197 "typical_devices": [198 "consistent with",199 "we speculate",200 "may have",201 "suggest",202 "possible explanations",203 "alternative interpretation"204 ]205 },206 "claim_evidence": [207 {208 "claim": "Distraction events reduce TV-watching investors' availability, validated by higher TV viewership on event days",209 "evidence_type": "table",210 "evidence_ref": "Table I"211 },212 {213 "claim": "Retail and institutional trading activity declines on distraction days",214 "evidence_type": "table",215 "evidence_ref": "Table II, Table III, Figure 3"216 },217 {218 "claim": "Market-wide effects are weak overall",219 "evidence_type": "table",220 "evidence_ref": "Table IV"221 },222 {223 "claim": "In small stocks, liquidity worsens 1-6%, volatility falls about 3%, and reversals fall about 4% on distraction days",224 "evidence_type": "table",225 "evidence_ref": "Table V"226 },227 {228 "claim": "Events with larger trading declines show larger reductions in liquidity, volatility and reversals",229 "evidence_type": "table",230 "evidence_ref": "Table VI"231 },232 {233 "claim": "Distraction effects change across pre-Internet, Internet and algorithmic trading eras",234 "evidence_type": "table",235 "evidence_ref": "Table VII"236 },237 {238 "claim": "In 1993-2006, effects are stronger among tech stocks",239 "evidence_type": "table",240 "evidence_ref": "Table VIII"241 },242 {243 "claim": "In stocks with more algorithmic trading, effects are stronger for activity and volatility and attenuated for liquidity",244 "evidence_type": "table",245 "evidence_ref": "Table IX"246 }247 ],248 "structural_summary": "The introduction opens with a gap: the attention literature focuses on informed agents and has neglected noise traders. It states two identification challenges and proposes sensational-news distraction days as the solution, then previews results with magnitudes and defends causality. A separate literature section follows, then data and methodology. Results move from investor-level trading to market outcomes, framed by a Kyle-model extension placed in the Internet Appendix. A dedicated robustness, endogeneity and alternatives section is followed by a technology and time-variation section. The conclusion restates findings, reconciles them with prior liquidity evidence, and offers speculative explanations plus a call for future work.",249 "writing_profile_keywords": [250 "natural experiment",251 "event study",252 "limited attention",253 "noise traders",254 "liquidity",255 "sample splits",256 "Kyle model",257 "technology and time trends"258 ],259 "confidence": 0.82,260 "notes": "The model is given only in the Internet Appendix and the excerpts contain no equations. Standard-error details are not visible: tables report t-statistics, and figures show confidence intervals (Figure 3 only). Nonparametric tests are mentioned in the introduction."261 },262 "deterministic_checks": {263 "roadmap_regex": true,264 "contribution_regex_in_intro": true,265 "magnitude_in_abstract": false,266 "abstract_n_sentences": 5,267 "intro_n_paragraphs": 5,268 "intro_n_words": 833,269 "causal_words_in_abstract": 2,270 "hedge_words_in_abstract": 1,271 "stars_mentioned": false,272 "clustered_se_mentioned": false,273 "internet_appendix_mentioned": true,274 "section_roles_rule": {275 "0": "introduction",276 "1": "literature",277 "2": "data",278 "3": "other",279 "4": "other",280 "5": "robustness",281 "6": "other",282 "7": "conclusion",283 "8": "appendix"284 }285 },286 "schema_valid": true,287 "code_version": "87c73cd-dirty"288}