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Insider trading in Brazil's stock market

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  • Marzagão, Thiago

Abstract

How much insider trading happens in Brazil’s stock market? Previous research has used the model proposed by Easley et al. [1996] to estimate the probability of insider trading (PIN) for different stocks in Brazil. Those estimates have a number of problems: i) they are based on a factorization that biases the PIN downward, especially for high-activity stocks; ii) they fail to account for boundary solutions, which biases most PIN estimates upward (and a few of them downward); and iii) they are a decade old and therefore based on a very different market (for instance, the number of retail investors grew from 600 thousand in 2011 to 3.5 million in 2021). In this paper I address those three problems and estimate the probability of insider trading for 431 different stocks in the Brazilian stock market, for each quarter from October 2019 to March 2021.

Suggested Citation

  • Marzagão, Thiago, 2021. "Insider trading in Brazil's stock market," OSF Preprints fu9mg, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:fu9mg
    DOI: 10.31219/osf.io/fu9mg
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    References listed on IDEAS

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    1. Martins, Orleans Silva & Paulo, Edilson & Albuquerque, Pedro Henrique Melo, 2013. "Negociação com informação privilegiada e retorno das ações na BM&FBOVESPA," RAE - Revista de Administração de Empresas, FGV-EAESP Escola de Administração de Empresas de São Paulo (Brazil), vol. 53(4), July.
    2. 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.
    3. Agudelo, Diego A. & Giraldo, Santiago & Villarraga, Edwin, 2015. "Does PIN measure information? Informed trading effects on returns and liquidity in six emerging markets," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 149-161.
    4. Easley, David, et al, 1996. "Liquidity, Information, and Infrequently Traded Stocks," Journal of Finance, American Finance Association, vol. 51(4), pages 1405-1436, September.
    5. Easley, David & Hvidkjaer, Soeren & O’Hara, Maureen, 2010. "Factoring Information into Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(2), pages 293-309, April.
    6. 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.
    7. Lee, Charles M C & Ready, Mark J, 1991. "Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
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