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The first table reports numerical efficiency bounds from the model.",159 "units_stated_in_notes": true,160 "standard_errors_description": "unclear"161 },162 "figures": {163 "uncertainty_shown": "no"164 },165 "conclusion": {166 "bounded_claims": false,167 "policy_implications": false,168 "open_questions": false,169 "restates_contribution": true170 },171 "hedging": {172 "level": "low",173 "typical_devices": [174 "'potentially superior'",175 "'where bias is potentially large'",176 "conditional framing ('if a subset of forecasts can be biased')"177 ]178 },179 "claim_evidence": [180 {181 "claim": "CE is a poor parameter-free approximation of the ideal bias-adjusted surprise measure; FOM approximates it far better",182 "evidence_type": "equation_or_proof",183 "evidence_ref": "Section I; Appendix A"184 },185 {186 "claim": "Lower bound on FOM's relative efficiency versus the infeasible ideal can reach about 50% under plausible parameters",187 "evidence_type": "table",188 "evidence_ref": "Table I"189 },190 {191 "claim": "FOM explains announcement returns much better than CE; a 1-SD rise in FOM raises CAR by about 1.5%",192 "evidence_type": "table",193 "evidence_ref": "Table III"194 },195 {196 "claim": "FOM also outperforms in post-announcement drift regressions and in international stocks",197 "evidence_type": "table",198 "evidence_ref": "Tables IV, V"199 },200 {201 "claim": "Model selection favors FOM-based specifications",202 "evidence_type": "table",203 "evidence_ref": "Table VI"204 },205 {206 "claim": "FOM beats parameter-dependent bias-adjusted CE in explaining CAR",207 "evidence_type": "table",208 "evidence_ref": "Table X"209 }210 ],211 "structural_summary": "The paper opens with the standard use of the consensus error in event studies and argues that its validity rests on unbiased forecasts, which analyst bias undermines. 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