IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i9p409-d1476590.html
   My bibliography  Save this article

Identifying Information Types in the Estimation of Informed Trading: An Improved Algorithm

Author

Listed:
  • Oguz Ersan

    (International Trade and Finance Department, Faculty of Economics, Administrative and Social Sciences, Kadir Has University, Cibali Mah., Fatih, 34083 Istanbul, Turkey)

  • Montasser Ghachem

    (Department of Economics, Stockholm University, 106 91 Stockholm, Sweden)

Abstract

The growing frequency of news arrivals, partly fueled by the proliferation of data sources, has made the assumptions of the classical probability of informed trading (PIN) model outdated. In particular, the model’s assumption of a single type of information event no longer reflects the complexity of modern financial markets, making the accurate detection of information types (layers) crucial for estimating the probability of informed trading. We propose a layer detection algorithm to accurately find the number of distinct information types within a dataset. It identifies the number of information layers by clustering order imbalances and examining their homogeneity using properly constructed confidence intervals for the Skellam distribution. We show that our algorithm manages to find the number of information layers with very high accuracy both when uninformed buyer and seller intensities are equal and when they differ from each other (i.e., between 86% and 95% accuracy rates). We work with more than 500,000 simulations of quarterly datasets with various characteristics and make a large set of robustness checks.

Suggested Citation

  • Oguz Ersan & Montasser Ghachem, 2024. "Identifying Information Types in the Estimation of Informed Trading: An Improved Algorithm," JRFM, MDPI, vol. 17(9), pages 1-20, September.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:9:p:409-:d:1476590
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/9/409/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/17/9/409/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Mark A Chen & Qinxi Wu & Baozhong Yang, 2019. "How Valuable Is FinTech Innovation?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 2062-2106.
    3. Henk Berkman & Paul D. Koch & P. Joakim Westerholm, 2014. "Informed Trading through the Accounts of Children," Journal of Finance, American Finance Association, vol. 69(1), pages 363-404, February.
    4. Yan, Yuxing & Zhang, Shaojun, 2014. "Quality of PIN estimates and the PIN-return relationship," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 137-149.
    5. Kimball L. Chapman & Nayana Reiter & Hal D. White & Christopher D. Williams, 2019. "Information overload and disclosure smoothing," Review of Accounting Studies, Springer, vol. 24(4), pages 1486-1522, December.
    6. Ersan, Oguz & Alıcı, Aslı, 2016. "An unbiased computation methodology for estimating the probability of informed trading (PIN)," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 74-94.
    7. Muhammed Basid Amnas & Murugesan Selvam & Satyanarayana Parayitam, 2024. "FinTech and Financial Inclusion: Exploring the Mediating Role of Digital Financial Literacy and the Moderating Influence of Perceived Regulatory Support," JRFM, MDPI, vol. 17(3), pages 1-20, March.
    8. Bazzana, Flavio & Collini, Andrea, 2020. "How does HFT activity impact market volatility and the bid-ask spread after an exogenous shock? An empirical analysis on S&P 500 ETF," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    9. William Lin, Hsiou-Wei & Ke, Wen-Chyan, 2011. "A computing bias in estimating the probability of informed trading," Journal of Financial Markets, Elsevier, vol. 14(4), pages 625-640, November.
    10. Jacob Boudoukh & Ronen Feldman & Shimon Kogan & Matthew Richardson, 2019. "Information, Trading, and Volatility: Evidence from Firm-Specific News," The Review of Financial Studies, Society for Financial Studies, vol. 32(3), pages 992-1033.
    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. 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).
    2. Bhurjee, Keerat & Saragur Ramanna, Vishwanatha, 2024. "Preopening auctions and price discovery in initial public offerings," Journal of Banking & Finance, Elsevier, vol. 165(C).
    3. Quan Gan & Wang Chun Wei & David Johnstone, 2015. "A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1805-1821, November.
    4. Ke, Wen-Chyan & Chen, Hueiling & Lin, Hsiou-Wei William, 2019. "A note of techniques that mitigate floating-point errors in PIN estimation," Finance Research Letters, Elsevier, vol. 31(C).
    5. Schreder, Max, 2018. "Idiosyncratic information and the cost of equity capital: A meta-analytic review of the literature," Journal of Accounting Literature, Elsevier, vol. 41(C), pages 142-172.
    6. Ersan, Oguz & Alıcı, Aslı, 2016. "An unbiased computation methodology for estimating the probability of informed trading (PIN)," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 74-94.
    7. Ahçi, Mustafa, 2023. "Essays on corporate disclosures, innovation, and investments," Other publications TiSEM 0dddb5f7-17e1-41ba-97da-0, Tilburg University, School of Economics and Management.
    8. Lu, Yao & Zhan, Shuwei & Zhan, Minghua, 2024. "Has FinTech changed the sensitivity of corporate investment to interest rates?—Evidence from China," Research in International Business and Finance, Elsevier, vol. 68(C).
    9. Pinto, Inês & Morais, Ana Isabel & Quick, Reiner, 2020. "The impact of the precision of accounting standards on the expanded auditor’s report in the European Union," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 40(C).
    10. McCannon, Bryan & Zhou, Yang & Hall, Joshua, 2021. "Measuring a Contract’s Breadth: A Text Analysis," Working Papers 11013, George Mason University, Mercatus Center.
    11. Yi Yang & Kunpeng Zhang & Yangyang Fan, 2023. "sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics," Information Systems Research, INFORMS, vol. 34(1), pages 137-156, March.
    12. Everett, Jeff & Shiraz Rahaman, Abu & Neu, Dean & Saxton, Gregory, 2024. "Letters to the editor, institutional experimentation, and the public accounting professional," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 99(C).
    13. Tuo, Ling & Rezaee, Zabihollah & Gao, Lei, 2024. "Is there a tradeoff between management earnings forecasts and sustainability reporting?," Global Finance Journal, Elsevier, vol. 59(C).
    14. Liu, Jiangtao & Zhang, Yi & Kuang, Jia, 2023. "Fintech development and green innovation: Evidence from China," Energy Policy, Elsevier, vol. 183(C).
    15. Itay Goldstein & Wei Jiang & G Andrew Karolyi, 2019. "To FinTech and Beyond," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1647-1661.
    16. Ana Abras & Guilherme G. C. Mattos, 2021. "Get Them While They Are Young," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(1), pages 97-113, April.
    17. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2022. "Media-expressed tone, option characteristics, and stock return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    18. Georgy B. Kleiner & Maksim A. Rybachuk & Venera A. Karpinskaya, 2020. "Development of ecosystems in the financial sector of Russia," Upravlenets, Ural State University of Economics, vol. 11(4), pages 2-15, September.
    19. Galeone, Graziana & Ranaldo, Simona & Fusco, Antonio, 2024. "ESG and FinTech: Are they connected?," Research in International Business and Finance, Elsevier, vol. 69(C).
    20. Kim, Jongkyum & Lim, Jee-Hae & Yoon, Kyunghee, 2022. "How do the content, format, and tone of Twitter-based corporate disclosure vary depending on earnings performance?," International Journal of Accounting Information Systems, Elsevier, vol. 47(C).

    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:gam:jjrfmx:v:17:y:2024:i:9:p:409-:d:1476590. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.