Skip to main content

Assessment of the Pillar 3 Financial and Risk Information Disclosures Usefulness to the Commercial Banks Users

  • Conference paper
  • First Online:
Advanced Intelligent Computing Theories and Applications (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9227))

Included in the following conference series:

  • 3019 Accesses

Abstract

The paper analyses usefulness of the Pillar 3 financial and risk information disclosures to the commercial banks users. The Pillar 3 are specific regulatory disclosures requirements set out in the Basel 2 framework and incorporated into EU law and subsequently laws of the member states. According to Pillar 3 intention market participants should be able to understand and subsequently judge the relevance of the bank risk position and risk management and try to discipline “risky” banks. Due to that the European authorities are focused on control and improvements of the banks’ disclosures. However, less is done as far as usefulness of the Pillar 3 risk information to the commercial banks users is. The authors try to assess at which extent is information useful for users of the banks that operate in countries where banking sectors are dominated by foreign-owned entities and depositors (sophisticated and non sophisticated; insured and uninsured; primarily non-financial ones) is a key source of market discipline. The authors focus on modelling of visitor behaviours at website where financial and risk information according to Pillar 3 requirements is available. The results show that there is in general small interest in Pillar 3 information and even financial and risk related information belongs to those where interests is the lowest one.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bliss, R.R., Flannery, M.-J.: Market discipline in the governance of US bank holding companies. In: Liu, J., Lean, D., Elroy, E. (eds) European Finance Review, vol. 6, no. 3, pp. 361–395 (2002)

    Google Scholar 

  2. Stephanou, C.: Rethinking Market Discipline in Banking. The World Bank (2010)

    Google Scholar 

  3. Freixas, X., Laux, C.H.: Disclosure, transparency and market discipline. Vox CEPR’s Policy Portal (2012). http://www.voxeu.org/article/market-discipline-disclosure-and-transparency

  4. Basel Committee on Banking Supervision: Review of the Pillar 3 requirements (2014). http://www.bis.org/publ/bcbs286.pdf

  5. Parwada, J.-T., Ruenzi, S., Saghal, S.: Market discipline and basel Pillar 3 reporting. In: Warren, G.-J. (ed.) 2013 Center for International Finance and Regulation Research Paper Series, pp. 9–11 (2013)

    Google Scholar 

  6. EBA: Guidelines on materiality, proprietary and confidentiality and on disclosure frequency under Articles 433(1), 433(2) and 433 of EU Regulation 575/2013 (2014)

    Google Scholar 

  7. EBF: Comments on the EBA consultation on draft guidelines on materiality, proprietary and confidentiality and on disclosure frequency under Articles 433(1), 433(2) and 433 of EU Regulation 575/2013 (2014)

    Google Scholar 

  8. Hasan, I., Jackowicz, K., Kowalewski, O., Kozlowski, L.: Market discipline during crisis: evidence from bank depositors in transition countries. In: Solanko, L. (ed.) Bank of Finland Discussion Papers (2013)

    Google Scholar 

  9. Liu, H., Kešelj, V.: Combined mining of Web server logs and web contents for classifying user navigation patterns and predicting users’ future requests. Data Knowl. Eng. 61(2), 304–330 (2007)

    Article  Google Scholar 

  10. Stevanovic, D., An, A., Vlajic, V.: Feature evaluation for web crawler detection with data mining techniques. Expert Syst. Appl. 39(10), 8707–8717 (2012)

    Article  Google Scholar 

  11. Xing, D., Shen, J.: Efficient data mining for web navigation patterns. Inf. Softw. Technol. 46(1), 55–63 (2004)

    Article  Google Scholar 

  12. Munk, M., Drlik, M.: Impact of different pre-processing tasks on effective identification of users’ behavioral patterns in web-based educational system. Procedia Comput. Sci. 4, 1640–1649 (2011)

    Article  Google Scholar 

  13. Munk, M., Kapusta, J., Svec, P.: Data preprocessing evaluation for web log mining: reconstruction of activities of a web visitor. In: ICCS 2010 - International Conference on Computational Science, pp. 2267–2274. Elsevier Science Bv, Amsterdam (2010)

    Google Scholar 

  14. Munk, M., Kapusta, J., Svec, P.: Data preprocessing dependency for web usage mining based on sequence rule analysis. In: IADIS Multi Conference on Computer Science and Information Systems, MCCSIS, Algarve, Portugal (2009)

    Google Scholar 

  15. Kapusta, J., Pilková, A., Munk, M., Švec, P.: Data pre-processing for web log mining: case study of commercial bank website usage analysis. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61(4), 973–979 (2013)

    Article  MATH  Google Scholar 

  16. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Proceedings of USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2004)

    Google Scholar 

  17. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  18. Cooper, B.F., Ramakrishnan, R., Srivastava, U., Silberstein, A., Bohannon, P., Jacobsen, H.-A., Puz, N., Weaver, D., Yerneni, R.: PNUTS: Yahoo!’s hosted data serving platform. Proc. VLDB Endow. (PVLDB) 1(2), 1277–1288 (2008)

    Article  Google Scholar 

  19. Zhou, J., Bruno, N., Wu, M.-C., Larson, P.-Å., Chaiken, R., Shakib, D.: SCOPE: parallel databases meet MapReduce. VLDB J. 21(5), 611–636 (2012)

    Article  Google Scholar 

  20. Leibiusky, J., Eisbruch, G., Simonassi, D.: Getting Started with Storm. O’Reilly, Ireland (2012)

    Google Scholar 

  21. Goodhope, K., Koshy, J., Kreps, J., Narkhede, N., Park, R., Rao, J., Ye, V.Y.: Building LinkedIn’s real-time activity data pipeline. IEEE Data Eng. Bull. 35(2), 33–45 (2012)

    Google Scholar 

  22. Lam, W., Liu, L., Prasad, S., Rajaraman, A., Vacheri, Z., Doan, A.: Muppet: MapReduce-style processing of fast data. Proc. VLDB Endow. (PVLDB) 5(12), 1814–1825 (2012)

    Article  Google Scholar 

  23. Doulkeridis, C., Nørvåg, K.: A survey of large-scale analytical query processing in MapReduce. VLDB J. 23(3), 355–380 (2014)

    Article  Google Scholar 

  24. Sivaraman, E., Manickachezian, R.: High performance and fault tolerant distributed file system for big data storage and processing using hadoop. In: Proceedings - 2014 International Conference on Intelligent Computing Applications, ICICA 2014, Article no. 6965006, pp. 32–36 (2014)

    Google Scholar 

  25. Su, C.-T., Tsao, W.-K., Chu, W.-R., Liao, M.-R.: Mining web browsing log by using relaxed biclique enumeration algorithm in MapReduce. In: Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT 2012, Article no. 6511648, pp. 54–58 (2012)

    Google Scholar 

  26. Premchaiswadi, W., Romsaiyud, W.: Extracting weblog of Siam University for learning user behavior on MapReduce. In: ICIAS 2012 - 2012 4th International Conference on Intelligent and Advanced Systems: A Conference of World Engineering, Science and Technology Congress (ESTCON) - Conference Proceedings, 1, Article no. 6306177, pp. 149–154 (2012)

    Google Scholar 

  27. Sakr, S., Liu, A., Fayoumi, A.G.: The family of mapreduce and large-scale data processing systems. ACM Comput. Surv. 46(1), 11 (2013)

    Article  Google Scholar 

  28. Ortega, J.L., Aguillo, I.: Differences between web sessions according to the origin of their visits. J. Informetrics 4(3), 331–337 (2010)

    Article  Google Scholar 

  29. Doran, D., Gokhale, S.S.: Web robot detection techniques: overview and limitations. Data Min. Knowl. Disc. 22(1–2), 183–210 (2011)

    Article  Google Scholar 

  30. Stevanovic, D., An, A., Vlajic, N.: Detecting web crawlers from web server access logs with data mining classifiers. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS, vol. 6804, pp. 483–489. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  31. Tan, P.-N., Kumar, V.: Discovery of web robot sessions based on their navigational patterns. Data Min. Knowl. Disc. 6(1), 9–35 (2002)

    Article  MathSciNet  Google Scholar 

  32. Thomas, P., O’Neill, A., Paris, C.: Interaction differences in web search and browse logs. In: Proceedings of the Fifteenth Australasian Document Computing Symposium, (ADCS) 2010, pp. 52–59 (2010)

    Google Scholar 

  33. Cápay, M., Balogh, Z., Boledovičová, M., Mesárošová, M.: Interpretation of questionnaire survey results in comparison with usage analysis in e-learning system for healthcare. Commun. Comput. Inf. Sci. 167(2), 504–516 (2011)

    Article  MATH  Google Scholar 

  34. Klocokova, D.: Integration of heuristics elements in the web-based environment: Experimental evaluation and usage analysis. Procedia Soc. Behav. Sci. 15, 1010–1014 (2011)

    Article  Google Scholar 

  35. Lančarič, D., Tóth, M., Savov, R.: Which legal form of agricultural firm based on return on equity should be preferred? A panel data analysis of Slovak agricultural firms. Stud. Agric. Econ. 115(3), 172–173 (2013)

    Article  Google Scholar 

Download references

Acknowledgments

This paper is supported by the project VEGA 1/0392/13 Modelling of Stakeholders’ Behaviour in Commercial Bank during the Recent Financial Crisis and Expectations of Basel Regulations under Pillar 3- Market Discipline.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michal Munk .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Pilkova, A., Munk, M., Svec, P., Medo, M. (2015). Assessment of the Pillar 3 Financial and Risk Information Disclosures Usefulness to the Commercial Banks Users. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_46

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22053-6_46

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22052-9

  • Online ISBN: 978-3-319-22053-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics