Abstract
Insider trading is known to negatively impact market risk and is considered a crime in many countries. The rate of enforcement however varies greatly. In Brazil especially very few legal cases have been pursued and a dataset of previous cases is, to the best of our knowledge, nonexistent. In this work, we consider the Brazilian market and deal with two problems. Firstly we propose a methodology for creating a dataset of evidences of insider trading. This requires both identifying impactful news events and suspicious negotiations that preceded these events. Secondly, we use our dataset in an attempt to recognise suspicious negotiations before relevant events are disclosed. We believe this work can potentially help funds in reducing risk exposure (suspicious trades may indicate undisclosed impactful news events) and enforcement agencies in focusing limited investigation resources. We employed a Machine Learning approach based on features from both spot and options markets. In our computational experiments we show that our approach consistently outperforms random predictors, which were developed due to lack of other related works in literature.
F. Lauar and C. Arbex Valle—Funded by CNPQ grant 420729/2018-6.
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Notes
- 1.
The dataset can be found at https://github.com/filipelauar/Insider-trading/, with detailed explanations given in the supplementary material.
References
Ahern, K.R.: Do proxies for informed trading measure informed trading? Evidence from illegal insider trades. Technical report, University of Southern California - Marshall School of Business; National Bureau of Economic Research (NBER) (2018)
Astorino, E.S.: Insider trading networks in Brazil. Ph.D. thesis, Universidade de São Paulo (2017)
B3: Brasil, Bolsa, Balcão (2019). https://www.b3.com.br. Accessed 28 Mar 2020
Bhattacharya, U., Daouk, H.: The world price of insider trading. J. Financ. 57(1), 75–108 (2002)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016). https://doi.org/10.1145/2939672.2939785
Conti, R.: Empirical properties of asset returns: stylized facts and statistical issues. Quant. Financ. 1(2), 223–236 (2001)
Dent, G.W.: Why legalized insider trading would be a disaster. Del. J. Corp. Law 38, 247–274 (2013)
Donoho, S.: Early detection of insider trading in option markets. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420–429 (2004)
Easley, D., Hvidkjaer, S., O’hara, M.: Is information risk a determinant of asset returns? J. Financ. 57(5), 2185–2221 (2002)
Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Financ. 25(2), 383–417 (1970)
Foster, L.H.: SEC - Insider trading investigations (2018). https://www.sec.gov/about/offices/oia/oia_enforce/foster.pdf. Accessed 28 Mar 2020
Goldberg, H.G., Kirkland, J.D., Lee, D., Shyr, P., Thakker, D.: The NASD securities observation, new analysis and regulation system (SONAR). In: Proceedings of the 15th Conference on Innovative Applications of Artificial Intelligence, pp. 11–18 (2003)
Islam, S.R., Ghafoor, S.K., Eberle, W.: Mining illegal insider trading of stocks: a proactive approach. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 1397–1406. IEEE (2018)
Kulkarni, A., Mani, P., Domeniconi, C.: Network-based anomaly detection for insider trading. arXiv preprint arXiv:1702.05809 (2017)
Martins, O.S., Paulo, E., Albuquerque, P.: Negociação com informação privilegiada na BMF&Bovespa e seu reflexo no retorno das ações (Informed trading and stock returns in the BMF&BOVESPA). Revista de Administração de Empresas FGV-EAESP 53(4), 350–362 (2013)
Minenna, M.: A supervisory perspective on insider trading: estimating the value of the information. CONSOB, Quaderni di Finanza 45(2000), 1–32 (2000)
Minenna, M.: The detection of market abuse on financial markets: a quantitative approach. CONSOB, Quaderni di finanza 1(54), 1–53 (2003)
Mitchell, M.L., Netter, J.M.: The role of financial economics in securities fraud cases: applications at the securities and exchange commission. Bus. Lawyer 49, 545–590 (1993)
Mitra, G., Yu, X.: The Handbook of Sentiment Analysis in Finance. Albury Books, New York (2016)
Parente, N.S.: Aspectos jurÃdicos do insider trading (1978). http://www.cvm.gov.br/export/sites/cvm/menu/acesso_informacao/serieshistoricas/estudos/anexos/Aspectos-Juridicos-do-insider-trading-NJP.pdf. Accessed 28 Mar 2020
SEC: Annual Report (2018). https://www.sec.gov/files/enforcement-annual-report-2018.pdf. Accessed 28 March 2020
U.S. Securities and Exchange Commission - SEC: Litigation releases. https://www.sec.gov/litigation/litreleases.shtml (May 2019)
Acknowledgments
We would like to thank Dr. Humberto Brandão for his essential contributions.
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Lauar, F., Arbex Valle, C. (2021). Detecting and Predicting Evidences of Insider Trading in the Brazilian Market. In: Dong, Y., Ifrim, G., Mladenić, D., Saunders, C., Van Hoecke, S. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12461. Springer, Cham. https://doi.org/10.1007/978-3-030-67670-4_15
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