Do Machine-Readable Disclosures Fuel Nonbank Lending?

Lin Lu, Steven Ongena and Jing Wang
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Lin Lu: University of Missouri at Columbia, Department of Finance
Steven Ongena: University of Zurich - Department Finance; Swiss Finance Institute; KU Leuven; NTNU Business School; Centre for Economic Policy Research (CEPR)
Jing Wang: University of Missouri at Columbia

No 26-18, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We study how reductions in public-information processing costs affect nonbank participation in syndicated loans. Exploiting the SEC's staggered introduction of machinereadable disclosures as an exogenous shock to information-processing costs, we find that lower costs increase nonbank participation but do not affect their likelihood of serving as lead arrangers. The effect is significant only among nonbanks without industry expertise. Reduced processing costs facilitate entry by new nonbanks but not banks. Following the shock, nonbanks rely less on relationship-based signals and more on standard financial metrics. Higher pre-loan automated EDGAR downloads predict greater nonbank participation, consistent with information-processing costs as the mechanism.

Keywords: Nonbank; Syndicated loans; Information processing costs; Public financial information; Machine readable disclosures; XBRL (search for similar items in EconPapers)
JEL-codes: G14 G21 G23 G32 M48 (search for similar items in EconPapers)
Pages: 64 pages
Date: 2026-02
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2618

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