IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2204.05979.html
   My bibliography  Save this paper

Discovering material information using hierarchical Reformer model on financial regulatory filings

Author

Listed:
  • Francois Mercier
  • Makesh Narsimhan

Abstract

Most applications of machine learning for finance are related to forecasting tasks for investment decisions. Instead, we aim to promote a better understanding of financial markets with machine learning techniques. Leveraging the tremendous progress in deep learning models for natural language processing, we construct a hierarchical Reformer ([15]) model capable of processing a large document level dataset, SEDAR, from canadian financial regulatory filings. Using this model, we show that it is possible to predict trade volume changes using regulatory filings. We adapt the pretraining task of HiBERT ([36]) to obtain good sentence level representations using a large unlabelled document dataset. Finetuning the model to successfully predict trade volume changes indicates that the model captures a view from financial markets and processing regulatory filings is beneficial. Analyzing the attention patterns of our model reveals that it is able to detect some indications of material information without explicit training, which is highly relevant for investors and also for the market surveillance mandate of financial regulators.

Suggested Citation

  • Francois Mercier & Makesh Narsimhan, 2022. "Discovering material information using hierarchical Reformer model on financial regulatory filings," Papers 2204.05979, arXiv.org.
    • Handle: RePEc:arx:papers:2204.05979
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2204.05979
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    2. Fama, Eugene F., 1998. "Market efficiency, long-term returns, and behavioral finance," Journal of Financial Economics, Elsevier, vol. 49(3), pages 283-306, September.
    3. Weiwei Jiang, 2020. "Applications of deep learning in stock market prediction: recent progress," Papers 2003.01859, arXiv.org.
    4. repec:pri:cepsud:91malkiel is not listed on IDEAS
    5. Bartram, Söhnke & Branke, Jürgen & Motahari, Mehrshad, 2020. "Artificial Intelligence in Asset Management," CEPR Discussion Papers 14525, C.E.P.R. Discussion Papers.
    6. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    7. 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.
    8. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    9. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    10. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    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. Taufiq Choudhry & Ranadeva Jayasekera, 2015. "Level of efficiency in the UK equity market: empirical study of the effects of the global financial crisis," Review of Quantitative Finance and Accounting, Springer, vol. 44(2), pages 213-242, February.
    2. Qianwei Ying & Tahir Yousaf & Qurat ul Ain & Yasmeen Akhtar & Muhammad Shahid Rasheed, 2019. "Stock Investment and Excess Returns: A Critical Review in the Light of the Efficient Market Hypothesis," JRFM, MDPI, vol. 12(2), pages 1-22, June.
    3. Kamal, Mona, 2014. "Studying the Validity of the Efficient Market Hypothesis (EMH) in the Egyptian Exchange (EGX) after the 25th of January Revolution," MPRA Paper 54708, University Library of Munich, Germany.
    4. Marianna Brunetti & Roberta De Luca, 2023. "Pairs trading in the index options market," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(1), pages 145-173, March.
    5. Jürgen Huber & Michael Kirchler, 2013. "Corporate campaign contributions and abnormal stock returns after presidential elections," Public Choice, Springer, vol. 156(1), pages 285-307, July.
    6. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2019. "Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    7. Mikio Ito & Akihiko Noda & Tatsuma Wada, 2016. "The evolution of stock market efficiency in the US: a non-Bayesian time-varying model approach," Applied Economics, Taylor & Francis Journals, vol. 48(7), pages 621-635, February.
    8. Thomas Holtfort, 2019. "From standard to evolutionary finance: a literature survey," Management Review Quarterly, Springer, vol. 69(2), pages 207-232, June.
    9. Alagidede, Paul, 2011. "Return behaviour in Africa's emerging equity markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(2), pages 133-140, May.
    10. Christopher R. Stephens & Harald A. Benink & José Luís Gordillo & Juan Pablo Pardo-Guerra, 2021. "A New Measure of Market Inefficiency," JRFM, MDPI, vol. 14(6), pages 1-22, June.
    11. Dionysia Dionysiou, 2015. "Choosing Among Alternative Long-Run Event-Study Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 158-198, February.
    12. Stefanescu, Razvan & Dumitriu, Ramona, 2016. "Particularitǎţi ale evoluţiei variabilelor financiare [Some particularities of the financial variables evolution]," MPRA Paper 73481, University Library of Munich, Germany, revised 02 Sep 2016.
    13. Majumder, Debasish, 2013. "Towards an efficient stock market: Empirical evidence from the Indian market," Journal of Policy Modeling, Elsevier, vol. 35(4), pages 572-587.
    14. Cristi Spulbar & Ramona Birau & Lucian Florin Spulbar, 2021. "A Critical Survey on Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH) and Fractal Markets Hypothesis (FMH) Considering Their Implication on Stock Markets Behavior," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 1161-1165, December.
    15. Admin Starcevic & Timothy Rodgers, 2011. "Market Efficiency within the German Stock Market: A Comparative Study of the Relative Efficiencies of the DAX, MDAX, SDAX and ASE Indices," International Econometric Review (IER), Econometric Research Association, vol. 3(1), pages 25-37, April.
    16. Ziliotto, Arianna & Serati, Massimiliano, 2015. "The semi-strong efficiency debate: In search of a new testing framework," Research in International Business and Finance, Elsevier, vol. 34(C), pages 412-438.
    17. Yardley, Ben, 2020. "The Effects of Donald Trump’s Tweets on The Stock Exchange," MPRA Paper 102578, University Library of Munich, Germany.
    18. Brice Corgnet & Cary Deck & Mark DeSantis & David Porter, 2022. "Forecasting Skills in Experimental Markets: Illusion or Reality?," Management Science, INFORMS, vol. 68(7), pages 5216-5232, July.
    19. Kevin Primicerio & Damien Challet & Stanislao Gualdi, 2017. "Wisdom of the institutional crowd," Working Papers hal-01484914, HAL.
    20. Rompotis, Gerasimos G., 2011. "Testing weak-form efficiency of exchange traded funds market," MPRA Paper 36020, University Library of Munich, Germany.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2204.05979. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.