IDEAS home Printed from https://ideas.repec.org/a/eee/beexfi/v37y2023ics2214635022000636.html
   My bibliography  Save this article

The impact of the disclosure characteristics of the application material on the successful listing of companies on China’s Science and Technology Innovation Board

Author

Listed:
  • Han, Chen
  • Wu, Chengliang
  • Wei, Lu

Abstract

The registration statement, the inquiry letter, and the reply letter are the main application materials for companies wanting to list on the Science and Technology Innovation Board (STAR) need to submit to regulatory agencies In this paper, we aim to study the impact of these three kinds of application materials on the successful listing of companies on STAR market in China through six text characteristics, including Words, Boilerplate, Fog Index, HardInfoMix, Redundancy, and Specificity for the first time. In the empirical analysis, we collect the registration statements and the inquiry-reply letters of 220 listed companies and 64 unlisted companies from June 13, 2019 to January 31, 2021 to perform the regression analysis. The empirical results show that, for registration statements, higher Words and Boilerplate will improve the success rate for listing, but higher Redundancy will lead to the failed listing. For the inquiry-reply letter, only the number of questions contained in the inquiry letter is negatively significantly associated with the initial public offering (IPO) success rate, while the text characteristics of the reply letter have little to do with the IPO success rate.

Suggested Citation

  • Han, Chen & Wu, Chengliang & Wei, Lu, 2023. "The impact of the disclosure characteristics of the application material on the successful listing of companies on China’s Science and Technology Innovation Board," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
  • Handle: RePEc:eee:beexfi:v:37:y:2023:i:c:s2214635022000636
    DOI: 10.1016/j.jbef.2022.100733
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2214635022000636
    Download Restriction: no

    File URL: https://libkey.io/10.1016/j.jbef.2022.100733?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
    ---><---

    References listed on IDEAS

    as
    1. Hongqi Yuan & Chujun Zhang & Desong Kong & Haina Shi, 2019. "The consequence of audit failure on audit firms: evidence from IPO approval in China," China Journal of Accounting Studies, Taylor & Francis Journals, vol. 7(2), pages 245-269, April.
    2. Elizabeth Blankespoor, 2019. "The Impact of Information Processing Costs on Firm Disclosure Choice: Evidence from the XBRL Mandate," Journal of Accounting Research, Wiley Blackwell, vol. 57(4), pages 919-967, September.
    3. Naderi Semiromi, Hamed & Lessmann, Stefan & Peters, Wiebke, 2020. "News will tell: Forecasting foreign exchange rates based on news story events in the economy calendar," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    4. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    5. Ouyang, Zi-sheng & Yang, Xi-te & Lai, Yongzeng, 2021. "Systemic financial risk early warning of financial market in China using Attention-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    6. Dyer, Travis & Lang, Mark & Stice-Lawrence, Lorien, 2017. "The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation," Journal of Accounting and Economics, Elsevier, vol. 64(2), pages 221-245.
    7. Wei, Lu & Li, Guowen & Li, Jianping & Zhu, Xiaoqian, 2019. "Bank risk aggregation with forward-looking textual risk disclosures," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    8. Thomas Renault, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205113, HAL.
    9. Zhifeng Yang, 2013. "Do Political Connections Add Value to Audit Firms? Evidence from IPO Audits in China," Contemporary Accounting Research, John Wiley & Sons, vol. 30(3), pages 891-921, September.
    10. Li, Feng, 2008. "Annual report readability, current earnings, and earnings persistence," Journal of Accounting and Economics, Elsevier, vol. 45(2-3), pages 221-247, August.
    11. Manela, Asaf & Moreira, Alan, 2017. "News implied volatility and disaster concerns," Journal of Financial Economics, Elsevier, vol. 123(1), pages 137-162.
    12. Ole-Kristian Hope & Danqi Hu & Hai Lu, 2016. "The benefits of specific risk-factor disclosures," Review of Accounting Studies, Springer, vol. 21(4), pages 1005-1045, December.
    13. Feng Li, 2010. "The Information Content of Forward‐Looking Statements in Corporate Filings—A Naïve Bayesian Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 48(5), pages 1049-1102, December.
    14. Li, Jingyu & Li, Jianping & Zhu, Xiaoqian, 2020. "Risk dependence between energy corporations: A text-based measurement approach," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 33-46.
    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. Katarzyna Anna Bilicka & Elisa Casi & Carol Seregni & Barbara Stage, 2021. "Tax Strategy Disclosure: A Greenwashing Mandate?," CESifo Working Paper Series 9030, CESifo.
    2. Richard Frankel & Jared Jennings & Joshua Lee, 2022. "Disclosure Sentiment: Machine Learning vs. Dictionary Methods," Management Science, INFORMS, vol. 68(7), pages 5514-5532, July.
    3. Blankespoor, Elizabeth & deHaan, Ed & Marinovic, Iván, 2020. "Disclosure processing costs, investors’ information choice, and equity market outcomes: A review," Journal of Accounting and Economics, Elsevier, vol. 70(2).
    4. John Donovan & Jared Jennings & Kevin Koharki & Joshua Lee, 2021. "Measuring credit risk using qualitative disclosure," Review of Accounting Studies, Springer, vol. 26(2), pages 815-863, June.
    5. Shen, Yiran & Liu, Chang & Sun, Xiaolei & Guo, Kun, 2023. "Investor sentiment and the Chinese new energy stock market: A risk–return perspective," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 395-408.
    6. Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Digital Finance, Springer, vol. 2(1), pages 1-13, September.
    7. M. J. Histen, 2022. "Taking Information Seriously: A Firm-side Interpretation of Risk Factor Disclosure," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 28(3), pages 119-131, November.
    8. 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.
    9. Wei, Lu & Jing, Haozhe & Huang, Jie & Deng, Yuqi & Jing, Zhongbo, 2023. "Do textual risk disclosures reveal corporate risk? Evidence from U.S. fintech corporations," Economic Modelling, Elsevier, vol. 127(C).
    10. Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020. "Artificial intelligence in asset management," Working Papers 20202001, Cambridge Judge Business School, University of Cambridge.
    11. Li, Jing & Li, Nan & Xia, Tongshui & Guo, Jinjin, 2023. "Textual analysis and detection of financial fraud: Evidence from Chinese manufacturing firms," Economic Modelling, Elsevier, vol. 126(C).
    12. Acheampong, Albert & Elshandidy, Tamer, 2021. "Does soft information determine credit risk? Text-based evidence from European banks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    13. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    14. Peng Liang & Nan Hu & Ling Liu & Ting Zhang, 2023. "Managerial tone and investors' hedging activities: Evidence from credit default swaps," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(4), pages 3971-3998, December.
    15. Fengler, Matthias & Phan, Minh Tri, 2023. "A Topic Model for 10-K Management Disclosures," Economics Working Paper Series 2307, University of St. Gallen, School of Economics and Political Science.
    16. Iatridis, George Emmanuel & Pappas, Kostas & Walker, Martin, 2022. "Narrative disclosure quality and the timeliness of goodwill impairments," The British Accounting Review, Elsevier, vol. 54(2).
    17. Li, Jianping & Feng, Yuyao & Li, Guowen & Sun, Xiaolei, 2020. "Tourism companies' risk exposures on text disclosure," Annals of Tourism Research, Elsevier, vol. 84(C).
    18. Allen H. Huang & Jianghua Shen & Amy Y. Zang, 2022. "The unintended benefit of the risk factor mandate of 2005," Review of Accounting Studies, Springer, vol. 27(4), pages 1319-1355, December.
    19. Hans B. Christensen & Luzi Hail & Christian Leuz, 2021. "Mandatory CSR and sustainability reporting: economic analysis and literature review," Review of Accounting Studies, Springer, vol. 26(3), pages 1176-1248, September.
    20. James P. Ryans, 2021. "Textual classification of SEC comment letters," Review of Accounting Studies, Springer, vol. 26(1), pages 37-80, March.

    More about this item

    Keywords

    STAR Market; Registration statement; Inquiry-Reply letter; Disclosure characteristic; Text analysis;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G39 - Financial Economics - - Corporate Finance and Governance - - - Other

    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:beexfi:v:37:y:2023:i:c:s2214635022000636. 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: https://www.journals.elsevier.com/journal-of-behavioral-and-experimental-finance .

    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.