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   "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.",
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    "Time fixed effects absorb daily variation in risk premia",
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    "claim": "Anomaly returns are about 50% higher on news days and six times higher on earnings announcement days.",
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    "evidence_ref": "Table III"
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   {
    "claim": "Information-day effects appear on both the long and short sides of anomalies.",
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    "evidence_ref": "Table IV"
   },
   {
    "claim": "The effect holds across the four anomaly categories.",
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    "evidence_ref": "Table V"
   },
   {
    "claim": "The effects are not explained by day-of-week effects or macroeconomic news days.",
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    "evidence_ref": "Table VI"
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   {
    "claim": "Dynamic betas rise on earnings days but do not explain the earnings-day anomaly effect.",
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    "evidence_ref": "Table VII"
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   {
    "claim": "Analyst forecasts are too optimistic for anomaly-shorts and too pessimistic for anomaly-longs.",
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    "evidence_ref": "Table VIII"
   },
   {
    "claim": "Out-of-sample anomalies still show information-day effects, making data mining unlikely as the explanation.",
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    "evidence_ref": "Table IX, Table X"
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    "claim": "Anomaly returns are concentrated within the window around earnings announcements.",
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    "evidence_ref": "Figure 1"
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