Tone or term: Machine-learning text analysis, featured vocabulary extraction, and evidence from bond pricing in China

Yueqian Peng, Li Shi, Xiaojun Shi and Songtao Tan

Journal of Empirical Finance, 2024, vol. 78, issue C

Abstract: We apply the machine-learning technique proposed by Zhou et al. (2024) to analyze credit rating reports in China’s bond markets, identifying featured vocabulary and generating text analysis scores. Compared with the traditional bag-of-words text analysis, evidence suggests three advantages of machine-learning scoring. Firstly, it covers featured vocabulary that compensates for missing information; secondly, it reduces misclassification of words’ sentiments; moreover, it mitigates the problem of equal weighting inherent in the bag-of-words method. Our findings indicate that the featured vocabulary neglected in the bag-of-words method plays a crucial role in text analysis and significantly contributes to bond pricing. Additionally, we find that machine-learning text analysis can address AAA rating inflation within China’s bond markets to some extent. In contrast, the bag-of-words method exhibits limited efficacy in mitigating this issue.

Keywords: Machine learning; Text analysis; Rating reports; Bond pricing; Credit rating inflation (search for similar items in EconPapers)
JEL-codes: G12 G17 G24 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:78:y:2024:i:c:s0927539824000690

DOI: 10.1016/j.jempfin.2024.101534

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