A taxonomy of financial market manipulations: establishing trust and market integrity in the financialized economy through automated fraud detection
Financial market manipulations represent a major threat to trust and market integrity in capital markets. Manipulations contribute to mispricing, market imperfections and an increase in transaction costs for market participants and in costs of capital for issuers. Manipulations are facilitated by increased transaction velocity, speculative trading and abusive usage of new trading technologies, i.e., they are directly linked to financial sector changes that drive financialization. Research at the intersection of financialization and IS might support regulatory authorities and market operators in improving market surveillance and helping to detect fraudulent activities. However, confusing terminology is prevalent on financial markets with respect to different manipulation techniques and their characteristics, which hampers efficient fraud detection. Furthermore, recognizing manipulations is challenging given the large number of information sources and the vast number of trades occurring not least because of high-frequency traders. Therefore, automated market surveillance tools require a comprehensive taxonomy of financial market manipulations as a basis for appropriate configuration. Based on a cluster analysis of SEC litigation releases, a review of the latest market abuse regulation and academic studies, we develop a taxonomy of manipulations that structures and details existing manipulation techniques and reveals how these techniques differ along several dimensions. In a case study, we show how the taxonomy can be utilized to guide the development of appropriate decision support systems for fraud detection.
Keywordsfinancial market manipulation market surveillance taxonomy fraud detection decision support
- Aalbers, M. (2016). Corporate financialization. In D. Richardson, N. Castree, M. F. Goodchild, A. Kobayashi, W. Liu, and R. A. Marston (Eds.), International Encyclopedia of Geography: People, the Earth, Environment and Technology. Oxford: Wiley.Google Scholar
- Abbasi, A., Zhang, Z., Zimbra, D., Chen, H., Nunamaker, Jr, and Jay, F. (2010). Detecting Fake Websites: The Contribution of Statistical Learning Theory. MIS Quarterly, 34(3), 435–461.Google Scholar
- Baker, N. (2005). Fraud and Artificial Intelligence. Internal Auditor, 62(1), 29–32.Google Scholar
- Bartels, K. C. (2000). Click Here to Buy the Next Microsoft: The Penny Stock Rules, Online Microcap Fraud, and the Unwary Investor. Indiana Law Journal, 75(1), 353–378.Google Scholar
- Biais, B. and Woolley, P. (2011). High frequency trading [WWW document] http://www.eifr.eu/files/file2220879.pdf (accessed 24th February 2016).
- Bonner, S. E., Palmrose, Z.-V., and Young, S. M. (1998). Fraud Type and Auditor Litigation: An Analysis of SEC Accounting and Auditing Enforcement Releases. The Accounting Review, 73(4), 503–532.Google Scholar
- Carroll, B. (2006). How to Prevent Investment Adviser Fraud. Journal of Accountancy, 201(1), 40–43.Google Scholar
- Cataldo, A. J., and Killough, L. N. (2003). Market Makers’ Methods of Stock Manipulation. Management Accounting Quarterly, 4(4), 10–13.Google Scholar
- Cumming, D., Zhan, F. and Aitken, M. (2012). High Frequency Trading and End-of-Day Manipulation [WWW document] https://www.legacy.wlu.ca/documents/54105/Cumming-Zhan-Aitken-15012013_2.pdf (accessed 24th February 2016).
- European Parliament and Council (2014). Market Abuse Regulation No 596/2014 [WWW document] http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32014R0596 (accessed 24th February 2016).
- European Parliament and Council (2016). Benchmark Regulation 2016/1011 [WWW document] http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32016R1011&from=EN (accessed 4th August 2016).
- Fatudimu, I., Musa, A., Ayo, C., and Sofoluwe, A. B. (2008). Knowledge Discovery in Online Repositories: A Text Mining Approach. European Journal of Scientific Research, 22(2), 241–250.Google Scholar
- Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17(3), 37–54.Google Scholar
- Financial Conduct Authority (FCA) (2014). FCA Final Notice: Reference Number 124491 [WWW document] http://www.fca.org.uk/your-fca/documents/final-notices/2014/jpmorgan-chase-bank (accessed 25th August 2016).
- Financial Conduct Authority (FCA) (2015). Call for Input: Supporting the Development and Adoption of RegTech [WWW document] https://www.fca.org.uk/publication/call-for-input/regtech-call-for-input.pdf (accessed 12th September 2016).
- Financial Services Authority (FSA) (2012). FSA Final Notice: Reference Number 122702 [WWW document] http://www.fsa.gov.uk/static/pubs/final/barclays-jun12.pdf (accessed 25th August 2016).
- Frank, J. (1994). Artificial Intelligence and Intrusion Detection: Current and Future Directions, in 17th National Computer Security Conference; Baltimore, USA, 1994, pp. 1–12.Google Scholar
- Golmohammadi, K., Zaiane, O.R. and Diaz, D. (2014). Detecting Stock Market Manipulation Using Supervised Learning Algorithms, in International Conference on Data Science and Advanced Analytics (DSAA); Shanghai, China, 2014, pp. 435–441.Google Scholar
- Gomber, P., Sagade, S., Theissen, E., Weber, M.C. and Westheide, C. (2016). Competition Between Equity Markets: A Review of the Consolidation Versus Fragmentation Debate, Journal of Economic Surveys (forthcoming).Google Scholar
- Hazen, T. L. (2010). Are Existing Stock Broker Standards Sufficient? Principles, Rules and Fiduciary Duties. Columbia Business Law Review, 2010(3), 710–761.Google Scholar
- IIROC (2012). Proposed Guidance on Certain Manipulative and Deceptive Trading Practices [WWW document] http://www.iiroc.ca/Documents/2012/f62c746a-b5c9-448a-b57f-f1c04c88de14_en.pdf (accessed 19th February 2016).
- Ionescu, L. (2010). Madoff’s Fraudulent Financial Scheme, His Decades-Long Swindle, and the Failure of Operational Risk Management. Economics, Management, and Financial Markets, 5(3), 239–244.Google Scholar
- Jones, M. J. (2011). Creative Accounting, Fraud And International Accounting Scandals. Hoboken: Wiley.Google Scholar
- Lagoarde-Segot, T. (2016). Financialization: Towards a New Research Agenda, International Review of Financial Analysis. doi:10.1016/j.irfa.2016.03.007.
- Lenard, M. J., and Alam, P. (2009). An Historical Perspective on Fraud Detection: From Bankruptcy Models to Most Effective Indicators of Fraud in Recent Incidents. Journal of Forensic & Investigative Accounting, 1(1), 1–27.Google Scholar
- Levy, Y., and Ellis, T. J. (2006). A Systems Approach to Conduct an Effective Literature Review in Support of Information Systems Research. Informing Science: International Journal of an Emerging Transdiscipline, 9(1), 181–212.Google Scholar
- LR 17346 (2002). Litigation Release No. 17346: Securities and Exchange Commission v. Patrick O. Wheeler, Steven S. Gallers, and Robert L. Carberry, Case No. 02-60131-CIV-GRAHAM (S.D. Fla.) [WWW document] http://www.sec.gov/litigation/litreleases/lr17346.htm (accessed 19th February 2016).
- LR 17645 (2002). Litigation Release No. 17645: Securities and Exchange Commission v. Michael A. Ofstedahl, et al., United States District Court for the Northern District of California, Civil Action No. C-02-3685 RS. [WWW document] http://www.sec.gov/litigation/litreleases/lr17645.htm (accessed 19th February 2016).
- LR 21423 (2010). Litigation Release No. 21423: Securities and Exchange Commission v. Frank J. Custable, Jr., et al., Civil Action No. 03-CV-2182 (N.D. Ill.) [WWW document] http://www.sec.gov/litigation/litreleases/2010/lr21423.htm (accessed 19th February 2016).
- LR 22545 (2012). Litigation Release No. 22545: SEC v. Berton M. Hochfeld et al., Civil Action No. 12-CV-8202 (S.D.N.Y.) [WWW document] http://www.sec.gov/litigation/litreleases/2012/lr22545.htm (accessed 19th February 2016).
- MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations, in Fifth Berkeley Symposium on Mathematical Statistics and Probability (Berkeley, USA, 1967), pp. 281–297.Google Scholar
- Pelleg, D. and Moore, A. (2000). X-Means: Extending K-Means with Efficient Estimation of the Number of Clusters, in 17th International Conference on Machine Learning; Stanford, USA, 2000, pp. 727–734.Google Scholar
- Pozza, C. L., Jr., Cox, T. R., and Morad, R. J. (2009). Review of Recent Investor Issues in the Madoff, Standford and Forte Ponzi Scheme Cases. Journal of Business and Securities Law, 10, 113–132.Google Scholar
- Rapoport, N. B. (2012). Black Swans, Ostriches, and Ponzi Schemes. Golden Gate University Law Review, 42, 627–661.Google Scholar
- Roddenberry, S., and Bacon, F. (2011). Insider Trading and Market Efficiency: Do Insiders Buy Low and Sell High? Journal of Finance & Accountancy, 8, 1–15.Google Scholar
- Rokach, L. and Maimon, O. (2005). Clustering Methods, in O. Maimon and L. Rokach, (eds.), Data Mining and Knowledge Discovery Handbook, New York: Springer US, pp. 321–352.Google Scholar
- Scopino, G. (2015). The (Questionable) Legality Of High-Speed Pinging and Front Running in the Futures Market. Connecticut Law Review, 47(3), 607–697.Google Scholar
- Shapiro, S., Kinkela, K., and Harris, P. (2012). Churning and Suitability of Investments: A Financial Industry Regulatory Authority Arbitration Case Study. Review of Business & Finance Case Studies, 3(1), 61–67.Google Scholar
- Thel, S. (1993). 850,000 in Six Minutes-The Mechanics of Securities Manipulation. Cornell Law Review, 79(2), 219–298.Google Scholar
- U.S. Commodity Futures Trading Commission (CFTC) (2016). Education Center: CFTC Glossary [WWW document] http://www.cftc.gov/consumerprotection/educationcenter/cftcglossary/glossary_b (accessed 4th August 2016).
- Vaughan, L. (2016). Broken Benchmarks: Six Years of Probes into Financial Fiddling [WWW document] https://www.bloomberg.com/quicktake/broken-benchmarks (accessed 25th August 2016).
- Webster, J., and Watson, R. T. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. Management Information Systems Quarterly, 26(2), 3.Google Scholar
- Zahedi, F. M., Abbasi, A., and Chen, Yan. (2015). Fake-Website Detection Tools: Identifying Elements that Promote Individuals’ Use and Enhance Their Performance. Journal of the Association for Information Systems, 16(6), 448–484.Google Scholar