Fragmentation and inefficiencies in US equity markets: Evidence from the Dow 30

Autoři: Brian F. Tivnan aff001;  David Rushing Dewhurst aff002;  Colin M. Van Oort aff001;  John H. Ring, IV aff001;  Tyler J. Gray aff002;  Brendan F. Tivnan aff004;  Matthew T. K. Koehler aff001;  Matthew T. McMahon aff001;  David M. Slater aff001;  Jason G. Veneman aff001;  Christopher M. Danforth aff002
Působiště autorů: The MITRE Corporation, McLean, VA, United States of America aff001;  Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States of America aff002;  Department of Mathematics and Statistics, University of Vermont, Burlington, VT, United States of America aff003;  Computational Finance Lab, Burlington, VT, United States of America aff004;  Department of Computer Science, University of Vermont, Burlington, VT, United States of America aff005;  Computational Story Lab, University of Vermont, Burlington, VT, United States of America aff006;  School of Engineering, Tufts University, Medford, MA, United States of America aff007
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226968


Using the most comprehensive source of commercially available data on the US National Market System, we analyze all quotes and trades associated with Dow 30 stocks in calendar year 2016 from the vantage point of a single and fixed frame of reference. We find that inefficiencies created in part by the fragmentation of the equity marketplace are relatively common and persist for longer than what physical constraints may suggest. Information feeds reported different prices for the same equity more than 120 million times, with almost 64 million dislocation segments featuring meaningfully longer duration and higher magnitude. During this period, roughly 22% of all trades occurred while the SIP and aggregated direct feeds were dislocated. The current market configuration resulted in a realized opportunity cost totaling over $160 million, a conservative estimate that does not take into account intra-day offsetting events.

Klíčová slova:

Economics – Engines – Fiber optics – Finance – Financial markets – Money supply and banking – National security – Asymmetric information


1. Indices SDJ. Dow Jones Industrial Average; 2018.

2. Kirilenko A, Kyle AS, Samadi M, Tuzun T. The flash crash: The impact of high frequency trading on an electronic market. Available at SSRN. 2011;1686004. doi: 10.2139/ssrn.1686004

3. Goldstein MA, Kavajecz KA. Trading strategies during circuit breakers and extreme market movements. Journal of Financial Markets. 2004;7(3):301–333. doi: 10.1016/j.finmar.2003.11.003

4. Grinblatt M, Keloharju M. The investment behavior and performance of various investor types: a study of Finland’s unique data set. Journal of financial economics. 2000;55(1):43–67. doi: 10.1016/S0304-405X(99)00044-6

5. FINRA. ATS Transparency Data Quarterly Statistics;.

6. Fama EF. Efficient capital markets: A review of theory and empirical work. The Journal of Finance. 1970;25(2):383–417. doi: 10.1111/j.1540-6261.1970.tb00518.x

7. Bouchaud JP. Econophysics: Still fringe after 30 years? arXiv preprint arXiv:190103691. 2019;.

8. Foye J, Mramor D, Pahor M. The Persistence of Pricing Inefficiencies in the Stock Markets of the Eastern European EU Nations. 2013.

9. Fama EF, French KR. Size, value, and momentum in international stock returns. Journal of financial economics. 2012;105(3):457–472. doi: 10.1016/j.jfineco.2012.05.011

10. Johnson N, Zhao G, Hunsader E, Qi H, Johnson N, Meng J, et al. Abrupt rise of new machine ecology beyond human response time. Scientific reports. 2013;3:2627. doi: 10.1038/srep02627 24022120

11. O’Hara M. High frequency market microstructure. Journal of Financial Economics. 2015;116(2):257–270. doi: 10.1016/j.jfineco.2015.01.003

12. Lo AW. The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. 2004.

13. Akerlof GA. The market for “lemons”: Quality uncertainty and the market mechanism. In: Uncertainty in Economics. Elsevier; 1978. p. 235–251.

14. Grossman SJ, Stiglitz JE. On the impossibility of informationally efficient markets. The American economic review. 1980;70(3):393–408.

15. Bloomfield R, O’hara M, Saar G. How noise trading affects markets: An experimental analysis. The Review of Financial Studies. 2009;22(6):2275–2302. doi: 10.1093/rfs/hhn102

16. Budish E, Cramton P, Shim J. The high-frequency trading arms race: Frequent batch auctions as a market design response. The Quarterly Journal of Economics. 2015;130(4):1547–1621. doi: 10.1093/qje/qjv027

17. Black F. Noise. The Journal of finance. 1986;41(3):528–543. doi: 10.1111/j.1540-6261.1986.tb04513.x

18. Blume ME, Goldstein MA. Differences in Execution Prices among the NYSE, the Regionals, and the NASD. Available at SSRN 979072. 1991;.

19. Lee CM. Market integration and price execution for NYSE-listed securities. The Journal of Finance. 1993;48(3):1009–1038. doi: 10.1111/j.1540-6261.1993.tb04028.x

20. Hasbrouck J. One security, many markets: Determining the contributions to price discovery. The journal of Finance. 1995;50(4):1175–1199. doi: 10.1111/j.1540-6261.1995.tb04054.x

21. Barclay MJ, Hendershott T, McCormick DT. Competition among trading venues: Information and trading on electronic communications networks. The Journal of Finance. 2003;58(6):2637–2665. doi: 10.1046/j.1540-6261.2003.00618.x

22. Shkilko AV, Van Ness BF, Van Ness RA. Locked and crossed markets on NASDAQ and the NYSE. Journal of Financial Markets. 2008;11(3):308–337. doi: 10.1016/j.finmar.2007.02.001

23. Ding S, Hanna J, Hendershott T. How slow is the NBBO? A comparison with direct exchange feeds. Financial Review. 2014;49(2):313–332. doi: 10.1111/fire.12037

24. Bartlett RP, McCrary J. How rigged are stock markets? Evidence from microsecond timestamps. Journal of Financial Markets. 2019;. doi: 10.1016/j.finmar.2019.06.003

25. Alexander J, Giordano L, Brooks D. Dark Pool Execution Quality: A Quantitative View. http://blogthemistradingcom/wp-content/uploads/2015/08/Dark-Pook-Execution-Quality-Short-Finalpdf. 2015;.

26. Wah E. How Prevalent and Profitable are Latency Arbitrage Opportunities on US Stock Exchanges? 2016.

27. Securities US, Commission E. MIDAS: Market Information Data Analytics System; 2013.

28. Angel JJ, Harris LE, Spatt CS. Equity trading in the 21st century. The Quarterly Journal of Finance. 2011;1(01):1–53. doi: 10.1142/S2010139211000067

29. Angel JJ, Harris LE, Spatt CS. Equity trading in the 21st century: An update. The Quarterly Journal of Finance. 2015;5(01):1550002. doi: 10.1142/S2010139215500020

30. Carrion A. Very fast money: High-frequency trading on the NASDAQ. Journal of Financial Markets. 2013;16(4):680–711. doi: 10.1016/j.finmar.2013.06.005

31. Menkveld AJ. High frequency trading and the new market makers. Journal of Financial Markets. 2013;16(4):712–740. doi: 10.1016/j.finmar.2013.06.006

32. Mackintosh P, Herrick J, Chen KW. The Need for Speed Reports 1-5. 2014-2016;.

33. Goldstein MA, Kumar P, Graves FC. Computerized and High-Frequency Trading. Financial Review. 2014;49(2):177–202. doi: 10.1111/fire.12030

34. Chordia T, Goyal A, Lehmann BN, Saar G. High-frequency trading. 2013;.

35. Arnuk S, Saluzzi J. Broken markets: how high frequency trading and predatory practices on Wall Street are destroying investor confidence and your portfolio. FT Press; 2012.

36. Securities US, Commission E. Consolidated Audit Trail; 2012.

37. Sachs G. Sigma X2 Form ATS-N;.

38. Miller K. Calculating Optical Fiber Latency;.

39. Technologies A. Anova Technologies Network Map; 2018.

40. Inc TG. Thesys Group Inc.; 2018.

41. Opportunities FB. MIDAS Contract Award Notice; 2012.

42. Popper N, Protess B. To Regulate Rapid Traders, S.E.C. Turns to One of Them; 2012.

43. Insights NE. Fed Rate Decisions and Quote Volatility;.

44. Insights NE. Market Reactions After Fed Rate Cut;.

45. Lab CF. Dislocation Visualizer.

Článek vyšel v časopise


2020 Číslo 1