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Natural Language Processing and Cognitive Networks Identify UK Insurers’ Trends in Investor Day Transcripts

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  • Stefan Claus

    (CogNosco Lab, Department of Computer Science, University of Exeter, Exeter EX4 4PY, UK)

  • Massimo Stella

    (CogNosco Lab, Department of Computer Science, University of Exeter, Exeter EX4 4PY, UK)

Abstract

The ability to spot key ideas, trends, and relationships between them in documents is key to financial services, such as banks and insurers. Identifying patterns across vast amounts of domain-specific reports is crucial for devising efficient and targeted supervisory plans, subsequently allocating limited resources where most needed. Today, insurance supervisory planning primarily relies on quantitative metrics based on numerical data (e.g., solvency financial returns). The purpose of this work is to assess whether Natural Language Processing (NLP) and cognitive networks can highlight events and relationships of relevance for regulators that supervise the insurance market, replacing human coding of information with automatic text analysis. To this aim, this work introduces a dataset of N I D T = 829 investor transcripts from Bloomberg and explores/tunes 3 NLP techniques: (1) keyword extraction enhanced by cognitive network analysis; (2) valence/sentiment analysis; and (3) topic modelling. Results highlight that keyword analysis, enriched by term frequency-inverse document frequency scores and semantic framing through cognitive networks, could detect events of relevance for the insurance system like cyber-attacks or the COVID-19 pandemic. Cognitive networks were found to highlight events that related to specific financial transitions: The semantic frame of “climate” grew in size by +538% between 2018 and 2020 and outlined an increased awareness that agents and insurers expressed towards climate change. A lexicon-based sentiment analysis achieved a Pearson’s correlation of ρ = 0.16 ( p < 0.001 , N = 829 ) between sentiment levels and daily share prices. Although relatively weak, this finding indicates that insurance jargon is insightful to support risk supervision. Topic modelling is considered less amenable to support supervision, because of a lack of results’ stability and an intrinsic difficulty to interpret risk patterns. We discuss how these automatic methods could complement existing supervisory tools in supporting effective oversight of the insurance market.

Suggested Citation

  • Stefan Claus & Massimo Stella, 2022. "Natural Language Processing and Cognitive Networks Identify UK Insurers’ Trends in Investor Day Transcripts," Future Internet, MDPI, vol. 14(10), pages 1-18, October.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:10:p:291-:d:940478
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    References listed on IDEAS

    as
    1. Sharma, Tripti & French, Declan & McKillop, Donal, 2022. "The UK equity release market: Views from the regulatory authorities, product providers and advisors," International Review of Financial Analysis, Elsevier, vol. 79(C).
    2. Skinner, Dj, 1994. "Why Firms Voluntarily Disclose Bad-News," Journal of Accounting Research, Wiley Blackwell, vol. 32(1), pages 38-60.
    3. Priyank Gandhi & Tim Loughran & Bill McDonald, 2019. "Using Annual Report Sentiment as a Proxy for Financial Distress in U.S. Banks," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 20(4), pages 424-436, October.
    4. Corrêa, Edilson A. & Marinho, Vanessa Q. & Amancio, Diego R., 2020. "Semantic flow in language networks discriminates texts by genre and publication date," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    5. Picault, Matthieu & Pinter, Julien & Renault, Thomas, 2022. "Media sentiment on monetary policy: Determinants and relevance for inflation expectations," Journal of International Money and Finance, Elsevier, vol. 124(C).
    6. Nitish Ranjan Sinha, 2016. "Underreaction to News in the US Stock Market," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 1-46, June.
    7. Russell S. Cropanzano & Sebastiano Massaro & William J. Becker, 2017. "Deontic Justice and Organizational Neuroscience," Journal of Business Ethics, Springer, vol. 144(4), pages 733-754, September.
    8. Kadilli, Anjeza, 2015. "Predictability of stock returns of financial companies and the role of investor sentiment: A multi-country analysis," Journal of Financial Stability, Elsevier, vol. 21(C), pages 26-45.
    9. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    10. Quispe, Laura V.C. & Tohalino, Jorge A.V. & Amancio, Diego R., 2021. "Using virtual edges to improve the discriminability of co-occurrence text networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    11. Fabio Bento & Marco Tagliabue & Flora Lorenzo, 2020. "Organizational Silos: A Scoping Review Informed by a Behavioral Perspective on Systems and Networks," Societies, MDPI, vol. 10(3), pages 1-27, July.
    12. Cynthia S. Q. Siew & Dirk U. Wulff & Nicole M. Beckage & Yoed N. Kenett, 2019. "Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics," Complexity, Hindawi, vol. 2019, pages 1-24, June.
    13. Hudson Golino & Alexander P. Christensen & Robert Moulder & Seohyun Kim & Steven M. Boker, 2022. "Modeling Latent Topics in Social Media using Dynamic Exploratory Graph Analysis: The Case of the Right-wing and Left-wing Trolls in the 2016 US Elections," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 156-187, March.
    14. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    15. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    16. Bailey, Andrew & Breeden, Sarah & Stevens, Gregory, 2012. "The Prudential Regulation Authority," Bank of England Quarterly Bulletin, Bank of England, vol. 52(4), pages 354-362.
    17. Jacob Boudoukh & Ronen Feldman & Shimon Kogan & Matthew Richardson, 2013. "Which News Moves Stock Prices? A Textual Analysis," NBER Working Papers 18725, National Bureau of Economic Research, Inc.
    18. 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.
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    2. Hojat Behrooz & Carlo Lipizzi & George Korfiatis & Mohammad Ilbeigi & Martin Powell & Mina Nouri, 2023. "Towards Automating the Identification of Sustainable Projects Seeking Financial Support: An AI-Powered Approach," Sustainability, MDPI, vol. 15(12), pages 1-12, June.

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