JOURNAL ARTICLE
Price Formation in Markets with Trading Delays.
Published In: Management Science (INFORMS), 2025, v. 71, n. 7. P. 6131 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Pintér, Gábor; Üslü, Semih 3 of 3
Abstract
This article develops a parsimonious price formation model to analyze how trading delays affect information aggregation, information acquisition, and price informativeness in financial markets. The model shows that when trading delays apply uniformly to both informed and uninformed traders, the level of delays does not affect the unique information content of prices (revelatory price efficiency, RPE) conditional on the fraction of informed traders; however, longer delays reduce traders’ incentives to acquire costly private information, leading to a lower equilibrium fraction of informed traders and thus an inverse relationship between trading delays and price informativeness. Additionally, the model predicts that price dispersion and illiquidity premia are nonmonotonic functions of trading delays when information acquisition is endogenous, with empirical evidence from the UK corporate bond market supporting these theoretical implications. The study uses transaction-level data to show that bonds traded more frequently (implying shorter trading delays) exhibit higher informational efficiency and that price dispersion is hump-shaped in trading frequency, being lowest for both very thinly and very actively traded bonds.
Additional Information
- Source:Management Science (INFORMS). 2025/07, Vol. 71, Issue 7, p6131
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2025
- ISSN:0025-1909
- DOI:10.1287/mnsc.2020.01400
- Accession Number:187524641
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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