IDEAS home Printed from https://ideas.repec.org/a/eee/empfin/v78y2024ics0927539824000690.html
   My bibliography  Save this article

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

Author

Listed:
  • Peng, Yueqian
  • Shi, Li
  • Shi, Xiaojun
  • Tan, Songtao

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.

Suggested Citation

  • Peng, Yueqian & Shi, Li & Shi, Xiaojun & Tan, Songtao, 2024. "Tone or term: Machine-learning text analysis, featured vocabulary extraction, and evidence from bond pricing in China," Journal of Empirical Finance, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:empfin:v:78:y:2024:i:c:s0927539824000690
    DOI: 10.1016/j.jempfin.2024.101534
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927539824000690
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jempfin.2024.101534?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Durnev, Art & Mangen, Claudine, 2020. "The spillover effects of MD&A disclosures for real investment: The role of industry competition," Journal of Accounting and Economics, Elsevier, vol. 70(1).
    2. Barta, Zsófia & Makszin, Kristin, 2021. "The politics of creditworthiness: political and policy commentary in sovereign credit rating reports," Journal of Public Policy, Cambridge University Press, vol. 41(2), pages 307-330, June.
    3. Tim Loughran & Bill McDonald, 2015. "The Use of Word Lists in Textual Analysis," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 16(1), pages 1-11, January.
    4. Jing-Zhi Huang & Bibo Liu & Zhan Shi, 2023. "Determinants of Short-Term Corporate Yield Spreads: Evidence from the Commercial Paper Market," Review of Finance, European Finance Association, vol. 27(2), pages 539-579.
    5. Jiaxing You & Bohui Zhang & Le Zhang, 2018. "Who Captures the Power of the Pen?," The Review of Financial Studies, Society for Financial Studies, vol. 31(1), pages 43-96.
    6. Price, S. McKay & Doran, James S. & Peterson, David R. & Bliss, Barbara A., 2012. "Earnings conference calls and stock returns: The incremental informativeness of textual tone," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 992-1011.
    7. Manela, Asaf & Moreira, Alan, 2017. "News implied volatility and disaster concerns," Journal of Financial Economics, Elsevier, vol. 123(1), pages 137-162.
    8. Steven L. Heston & Nitish Ranjan Sinha, 2017. "News vs. Sentiment: Predicting Stock Returns from News Stories," Financial Analysts Journal, Taylor & Francis Journals, vol. 73(3), pages 67-83, July.
    9. Cho, Charles H. & Roberts, Robin W. & Patten, Dennis M., 2010. "The language of US corporate environmental disclosure," Accounting, Organizations and Society, Elsevier, vol. 35(4), pages 431-443, May.
    10. Becker, Bo & Milbourn, Todd, 2011. "How did increased competition affect credit ratings?," Journal of Financial Economics, Elsevier, vol. 101(3), pages 493-514, September.
    11. Wang, Yuyue & Fang, Hongyan & Luo, Ronghua, 2022. "Does state ownership affect rating quality? Evidence from China's corporate bond market," Economic Modelling, Elsevier, vol. 111(C).
    12. Bian, Shibo & Jia, Dekui & Li, Ruihai & Sun, Wujun & Yan, Zhipeng & Zheng, Yingfei, 2021. "Can management tone predict IPO performance? – Evidence from mandatory online roadshows in China," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    13. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    14. Li, Fengyu & Yang, Mozhu & Zhang, Tong, 2023. "Does prospectus readability matter for bond issuance pricing? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    15. Blankespoor, Elizabeth & Miller, Gregory S. & White, Hal D., 2013. "The Role of Dissemination in Market Liquidity: Evidence from Firms' Use of Twitter," Research Papers 2106r, Stanford University, Graduate School of Business.
    16. Slapnik, Ursula & Lončarski, Igor, 2021. "On the information content of sovereign credit rating reports: Improving the predictability of rating transitions☆," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    17. Sumit Agarwal & Vincent Y. S. Chen & Weina Zhang, 2016. "The Information Value of Credit Rating Action Reports: A Textual Analysis," Management Science, INFORMS, vol. 62(8), pages 2218-2240, August.
    18. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    19. Angela K. Davis & Isho Tama†Sweet, 2012. "Managers’ Use of Language Across Alternative Disclosure Outlets: Earnings Press Releases versus MD&A," Contemporary Accounting Research, John Wiley & Sons, vol. 29(3), pages 804-837, September.
    20. Liu, Xiaojian & Chong, Beng Soon & Feng, Xiaozhi, 2023. "Does the market differentiate between investor-paid and issuer-paid ratings in the pricing of asset-backed securities?," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    21. Farshid Abdi & Angelo Ranaldo, 2017. "A Simple Estimation of Bid-Ask Spreads from Daily Close, High, and Low Prices," The Review of Financial Studies, Society for Financial Studies, vol. 30(12), pages 4437-4480.
    22. Stephen P. Ferris & (Grace) Qing Hao & (Stella) Min-Yu Liao, 2013. "The Effect of Issuer Conservatism on IPO Pricing and Performance," Review of Finance, European Finance Association, vol. 17(3), pages 993-1027.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yan Luo & Linying Zhou, 2020. "Textual tone in corporate financial disclosures: a survey of the literature," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 17(2), pages 101-110, September.
    2. Christina Bannier & Thomas Pauls & Andreas Walter, 2019. "Content analysis of business communication: introducing a German dictionary," Journal of Business Economics, Springer, vol. 89(1), pages 79-123, February.
    3. Bask, Mikael & Forsberg, Lars & Östling, Andreas, 2024. "Media sentiment and stock returns," The Quarterly Review of Economics and Finance, Elsevier, vol. 94(C), pages 303-311.
    4. Bassyouny, Hesham & Abdelfattah, Tarek & Tao, Lei, 2022. "Narrative disclosure tone: A review and areas for future research," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 49(C).
    5. Ingrid E. Fisher & Margaret R. Garnsey & Mark E. Hughes, 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 157-214, July.
    6. Wanli Li & Tiantian Yan & Yue Li & Ziqiao Yan, 2023. "Earnings management and CSR report tone: Evidence from China," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 30(4), pages 1883-1902, July.
    7. Yekini, Liafisu Sina & Wisniewski, Tomasz Piotr & Millo, Yuval, 2016. "Market reaction to the positiveness of annual report narratives," The British Accounting Review, Elsevier, vol. 48(4), pages 415-430.
    8. Xue, Wenjun & He, Zhongzhi & Wang, FeiFei, 2024. "MD&A tone and stock returns," Journal of Contemporary Accounting and Economics, Elsevier, vol. 20(3).
    9. Eryka Probierz & Adam Galuszka & Katarzyna Klimczak & Karol Jedrasiak & Tomasz Wisniewski & Tomasz Dzida, 2021. "Financial Sentiment on Twitter's Community and it's Connection to Polish Stock Market Movements in Context of Behavior Modelling," European Research Studies Journal, European Research Studies Journal, vol. 0(4 - Part ), pages 56-65.
    10. Ahmed, Yousry & Elshandidy, Tamer, 2016. "The effect of bidder conservatism on M&A decisions: Text-based evidence from US 10-K filings," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 176-190.
    11. Bannier, Christina E. & Pauls, Thomas & Walter, Andreas, 2017. "CEO-speeches and stock returns," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168192, Verein für Socialpolitik / German Economic Association.
    12. D. G. DeBoskey & Yan Luo & Linying Zhou, 2019. "CEO power, board oversight, and earnings announcement tone," Review of Quantitative Finance and Accounting, Springer, vol. 52(2), pages 657-680, February.
    13. Renato Camodeca & Alex Almici & Umberto Sagliaschi, 2018. "Sustainability Disclosure in Integrated Reporting: Does It Matter to Investors? A Cheap Talk Approach," Sustainability, MDPI, vol. 10(12), pages 1-34, November.
    14. Wang, Chunlan & Xin, Jianxuan & Sun, Fangfang & Shi, Yan & Du, Yuxuan, 2024. "The effects of manager sentiment in financial disclosure: Perspectives of operational efficiency and market reaction," Finance Research Letters, Elsevier, vol. 64(C).
    15. Miwa, Kotaro, 2021. "Language barriers in analyst reports," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 223-236.
    16. Arslan-Ayaydin, Özgür & Boudt, Kris & Thewissen, James, 2016. "Managers set the tone: Equity incentives and the tone of earnings press releases," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 132-147.
    17. Chang, Liang & Tan, Na & Zhang, Xinyue & Yuan, Yiyun, 2023. "Does peer firms’ tone affect corporate investment? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 90(C).
    18. Auzepy, Alix & Bannier, Christina E. & Martin, Fabio, 2023. "Walk the talk: Shareholders' soft engagement at annual general meetings," CFS Working Paper Series 689, Center for Financial Studies (CFS).
    19. Maciej Wujec, 2021. "Analysis of the Financial Information Contained in the Texts of Current Reports: A Deep Learning Approach," JRFM, MDPI, vol. 14(12), pages 1-17, December.
    20. Durnev, Art & Mangen, Claudine, 2020. "The spillover effects of MD&A disclosures for real investment: The role of industry competition," Journal of Accounting and Economics, Elsevier, vol. 70(1).

    More about this item

    Keywords

    Machine learning; Text analysis; Rating reports; Bond pricing; Credit rating inflation;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:empfin:v:78:y:2024:i:c:s0927539824000690. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jempfin .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.