Anatomy of sovereign yield behaviour using textual news

Journal article

Kumar Banerjee, Ameet, Pradhan, H.K., Akhtaruzzaman, Md, Sensoy, Ahmet and Dann, Susan Jean. (2024). Anatomy of sovereign yield behaviour using textual news. Research in International Business and Finance. 71, pp. 1-18. https://doi.org/10.1016/j.ribaf.2024.102458
AuthorsKumar Banerjee, Ameet, Pradhan, H.K., Akhtaruzzaman, Md, Sensoy, Ahmet and Dann, Susan Jean
Abstract

While the relationship between the information content of macroeconomic news and the behavior of asset prices has been studied extensively in the finance literature, this study provides a new perspective by examining the impact of textual news on sovereign bond yield spreads in an emerging country. This study used bond market news published in newspapers to develop the sentiment scores using a modified word dictionary to unravel news characteristics. A nonlinear regime-shifting regression model of Markov Regime Shifting (MRS) is used to understand the impact of news on sovereign bond yield spreads. The paper results show that textual news sentiment may explain both steepening and flattening of the yield curve, with monetary and fiscal policy news having the most significant impact on yield spread behaviour. The results hold key implications for policymakers, debt fund managers and other market participants.

Keywordsyield spread ; sovereign bond; textual news ; macroeconomic news
Year01 Jan 2024
JournalResearch in International Business and Finance
Journal citation71, pp. 1-18
PublisherElsevier Inc. (USA)
ISSN0275-5319
Digital Object Identifier (DOI)https://doi.org/10.1016/j.ribaf.2024.102458
Web address (URL)https://www.sciencedirect.com/science/article/pii/S0275531924002514?via=ihub
Open accessPublished as non-open access
Research or scholarlyResearch
Page range1-18
Publisher's version
Output statusPublished
Publication dates
Online22 Jun 2024
Publication process dates
Accepted15 Jun 2024
Deposited25 Jul 2024
Additional information

© 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies

Place of publicationUnited States
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