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OLS regressions control for trade characteristics and market conditions. A cointegrated VAR/VECM in the spirit of Hasbrouck measures permanent price impact and price discovery. Comparisons are made across trading protocols and against contemporaneous interdealer quotes.",28 "key_assumptions": [29 "Mid-quotes (Markit composites for D2C, GFI executable quotes for D2D) proxy for efficient prices",30 "The SEF on which a trade occurs correctly identifies whether it is D2C or D2D",31 "Algorithms correctly identify package transactions and SEFs",32 "A 15-minute horizon captures price impact"33 ],34 "threats_addressed": [35 "Selection bias from differing trade characteristics and market conditions across segments",36 "Contamination from package trades (rolls, curve trades, delta hedges)"37 ],38 "causal_language_strength": 2,39 "uses_causal_claim": false40 },41 "abstract_moves": {42 "question": {43 "present": true,44 "sentence_index": 045 },46 "gap": {47 "present": false,48 "sentence_index": -149 },50 "method": {51 "present": false,52 "sentence_index": -153 },54 "results": {55 "present": true,56 "sentence_index": 157 },58 "magnitude": {59 "present": false,60 "sentence_index": -161 },62 "contribution": {63 "present": true,64 "sentence_index": 465 }66 },67 "introduction": {68 "moves": [69 {70 "paragraph_index": 0,71 "move": "motivation"72 },73 {74 "paragraph_index": 0,75 "move": "literature_gap"76 },77 {78 "paragraph_index": 1,79 "move": "preview_of_approach"80 },81 {82 "paragraph_index": 2,83 "move": "preview_of_results"84 },85 {86 "paragraph_index": 3,87 "move": "preview_of_approach"88 },89 {90 "paragraph_index": 3,91 "move": "preview_of_results"92 },93 {94 "paragraph_index": 4,95 "move": "mechanism_or_theory_sketch"96 },97 {98 "paragraph_index": 5,99 "move": "preview_of_results"100 },101 {102 "paragraph_index": 6,103 "move": "preview_of_results"104 },105 {106 "paragraph_index": 7,107 "move": "policy_or_broader_implication"108 },109 {110 "paragraph_index": 8,111 "move": "related_literature_positioning"112 },113 {114 "paragraph_index": 9,115 "move": "related_literature_positioning"116 }117 ],118 "contribution_paragraph_index": 9,119 "contribution_types": [120 "new_evidence",121 "new_data_or_measure"122 ],123 "roadmap_present": false,124 "literature_positioning_location": "inside_introduction"125 },126 "section_roles": [127 {128 "idx": 0,129 "role": "introduction"130 },131 {132 "idx": 1,133 "role": "other"134 },135 {136 "idx": 2,137 "role": "data"138 },139 {140 "idx": 3,141 "role": "results"142 },143 {144 "idx": 4,145 "role": "results"146 },147 {148 "idx": 5,149 "role": "results"150 },151 {152 "idx": 6,153 "role": "results"154 },155 {156 "idx": 7,157 "role": "results"158 },159 {160 "idx": 8,161 "role": "conclusion"162 }163 ],164 "results_presentation": {165 "economic_significance_discussed": true,166 "statistical_significance_language": "unclear",167 "magnitude_statements_in_intro": true168 },169 "robustness": {170 "location": "none_seen",171 "types": []172 },173 "tables": {174 "convention_notes": "Table notes define variables with abbreviations (EffcSprd, RlzdSprd, PrcImp), state the sample period and sample sizes, and split panels by index (CDX.IG vs CDX.HY). Means are reported by trade-size quartile.",175 "units_stated_in_notes": true,176 "standard_errors_description": "unclear"177 },178 "figures": {179 "uncertainty_shown": "unclear"180 },181 "conclusion": {182 "bounded_claims": true,183 "policy_implications": true,184 "open_questions": false,185 "restates_contribution": true186 },187 "hedging": {188 "level": "moderate",189 "typical_devices": [190 "'suggest' / 'appear to'",191 "'consistent with' a theoretical model",192 "explicit statement that welfare effects cannot be addressed empirically",193 "'suggestive evidence'"194 ]195 },196 "claim_evidence": [197 {198 "claim": "Client order flow comprises few, large trades, with D2C volume far exceeding D2D volume.",199 "evidence_type": "table",200 "evidence_ref": "Table I"201 },202 {203 "claim": "D2C transaction costs exceed D2D costs, but the gap is fully explained by higher price impact.",204 "evidence_type": "table",205 "evidence_ref": "Tables II and III"206 },207 {208 "claim": "Price impact of client trades is largely permanent and price discovery occurs mainly in the D2C segment.",209 "evidence_type": "table",210 "evidence_ref": "Table IV, Figure 4"211 },212 {213 "claim": "Mid-market matches have lower costs and price impacts than limit order book trades, consistent with Zhu's (2014) venue selection model.",214 "evidence_type": "table",215 "evidence_ref": "Tables V and VI, Figure 5"216 },217 {218 "claim": "D2C trades almost always improve on contemporaneous interdealer inside quotes.",219 "evidence_type": "table",220 "evidence_ref": "Table VII"221 },222 {223 "claim": "Client transaction costs are widely dispersed, consistent with dealers price discriminating on perceived price impact.",224 "evidence_type": "table",225 "evidence_ref": "Table VIII"226 }227 ],228 "structural_summary": "The introduction motivates the paper with the importance of the index CDS market and the Dodd-Frank SEF mandate as a test of market structure. It then previews data and algorithms and walks sequentially through findings with numeric magnitudes, closing with related literature. The body describes institutional detail and data/identification algorithms, then builds the empirical results in sequence: a spread decomposition with regression controls, a VECM for price discovery, interdealer protocol segmentation, price improvement, and dispersion of client costs. The conclusion summarizes and interprets the two-tiered structure as a consequence of client trade characteristics.",229 "writing_profile_keywords": [230 "market microstructure",231 "OTC derivatives",232 "Dodd-Frank",233 "spread decomposition",234 "VECM price discovery",235 "institutional detail",236 "descriptive-heavy"237 ],238 "confidence": 0.8,239 "notes": "Robustness checks are not visible in the excerpts. Significance language is mentioned in the introduction ('statistically significant'), but the format in tables is not visible. 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