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54,000 PRIIPs KIDs - how to read them (all)

In: Artificial Intelligence and Financial Behaviour

Author

Listed:
  • Adrien Amzallag

Abstract

This chapter presents the results of an ESMA pilot exercise to apply natural language processing techniques on a unique dataset of c. 54 000 Key Information Documents that describe structured retail products produced under the Packaged Retail Investment and Insurance-Based Products Regulation. The techniques involved include measuring linguistic richness and semantic uncertainty, as well as sentiment analysis. This work - an application of SupTech - aims to illustrate how these techniques can produce useful measures for European supervisors, policymakers and risk analysts. Information extracted from text opens up new possibilities for supervisory assessments, for example with respect to information completeness and to legal requirements that a document be comprehensible to investors. In addition, text-based information is uncorrelated with (i.e. complementary to) numerical information, which can help policymakers determine if the legislation is working as intended. Lastly, text-based information can identify new sources of financial risks to investors.

Suggested Citation

  • Adrien Amzallag, 2023. "54,000 PRIIPs KIDs - how to read them (all)," Chapters, in: Riccardo Viale & Shabnam Mousavi & Umberto Filotto & Barbara Alemanni (ed.), Artificial Intelligence and Financial Behaviour, chapter 12, pages 215-239, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:21559_12
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