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Electronic Commerce Research

Erschienen in:

22.11.2017

Do high-frequency fleeting orders exacerbate market illiquidity?

verfasst von: Kun Li

Erschienen in: Electronic Commerce Research | Ausgabe 2/2018

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Abstract

This paper investigates whether fleeting orders account for market illiquidity. By discussing relevant trading strategies, our study suggests that fleeting orders serve for market making and contribute to market liquidity. Moreover, fleeting orders do not distort price accuracy and are not the outcome of illegal manipulation. We then empirically examine fleeting orders using a NASDAQ ITCH dataset. Our results indicate that fleeting orders have very small effects on market illiquidity and account for neither the amplification of price impact nor the decrease of revenues to liquidity providers. In summary, fleeting orders are not the trigger of market illiquidity and thus should not be considered as “spoofing” defined by the Dodd–Frank Act.

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NASDAQ ITCH^SM is a data feed format offered by The NASDAQ Stock Market, LLC. The data was collected from a co-located server, micro-second time stamped, and manipulated by an anonymous trading firm. The data is courtesy of their generosity, and with NASDAQ approval. For a more detailed presentation of the data set, see Appendix A.

Each trading day, 9:30 a.m. to 4:00 p.m. EST consists of 390 min.

The midpoint price represents the true value of the stock agreed on the basis of the common knowledge only.

Angel, J. J., & McCabe, D. (2013). Fairness in financial markets: The case of high frequency trading. Journal of Business Ethics, 112(4), 585–595.CrossRef

Baruch, S., & Glosten, L. R. (2013). Fleeting orders. In Columbia business school research paper (13–43).

Bessembinder, H. (2003). Issues in assessing trade execution costs. Journal of Financial Markets, 6(3), 233–257.CrossRef

Bessembinder, H., & Venkataraman, K. (2009). Bid-ask spreads: Measuring trade execution costs in financial markets. Encyclopedia of quantitative finance. 184–190.

Brogaard, J., Hendershott, T., & Riordan, R. (2014). High-frequency trading and price discovery. The Review of Financial Studies, 27(8), 2267–2306. doi:10.1093/rfs/hhu032.CrossRef

Coombs, N. (2016). What is an algorithm? Financial regulation in the era of high-frequency trading. Economy and Society, 25(2), 278–302.CrossRef

Cooper, R., Davis, M., & Van Vliet, B. (2016). The mysterious ethics of high-frequency trading. Business Ethics Quarterly, 26(01), 1–22.CrossRef

Gerig, A., Michayluk, D., 2013. Automated liquidity provision. In Working paper.

Glosten, L. R. (1987). Components of the Bid-Ask spread and the statistical properties of transaction prices. The Journal of Finance, 42(5), 1293–1307.CrossRef

10.

Groth, S. S. (2009). Further evidence on technology and liquidity provision: The blurring of traditional definitions. Available at SSRN 1539990.

11.

Harris, L. (2003). Trading and exchanges: Market microstructure for practitioners. Oxford: Oxford University Press.

12.

Hasbrouck, J., & Saar, G. (2009). Technology and liquidity provision: The blurring of traditional definitions. Journal of financial Markets, 12(2), 143–172.CrossRef

13.

Hendershott, T., Jones, C. M., & Menkveld, A. J. (2011). Does algorithmic trading improve liquidity? The Journal of Finance, 66(1), 1–33.CrossRef

14.

In re Indiana Farm Bureau Coop. Ass’n, Inc. [1982–1984 Transfer Binder] Comm. Fut. Law. Rep. (CCH) P 21,796, 27, 288, n. 2. (CFTC December 17, 1982).

15.

Kang, J., & Shin, J. (2012). The role of high frequency traders in electronic limit order markets. In Working Paper.

16.

Kirilenko, A., Kyle, A. S., Samadi, M., & Tuzun, T. (2017). The flash crash: High-frequency trading in an electronic market. The Journal of Finance, 72, 967–998. doi:10.1111/jofi.12498.CrossRef

17.

Kyle, A. S., & Viswanathan, S. (2008). How to define illegal price manipulation. The American Economic Review, 98(2), 274–279.CrossRef

18.

Lange, A. C., Lenglet, M., & Seyfert, R. (2016). Cultures of high-frequency trading: Mapping the landscape of algorithmic developments in contemporary financial markets. Economy and Society, 25(2), 149–165.CrossRef

19.

Lee, C., & Ready, M. J. (1991). Inferring trade direction from intraday data. The Journal of Finance, 46(2), 733–746.CrossRef

20.

Li, K. (2015) The boundary of high-frequency trading and low-frequency trading in the limit order book. In The 51st Eastern Finance Association Anniversary Meeting Paper.

21.

Li, K., Cooper, R., & Van Vliet, B. (2017). How does high frequency trading affect low frequency trading? Journal of Behavioral Finance. doi:10.1080/15427560.2017.1376669.

22.

Menkveld, A. J. (2013). High frequency trading and the new market makers. Journal of Financial Markets, 16, 712–740.CrossRef

23.

U.S. Commodity Futures Trading Commission (2015). “CFTC Charges U.K. Resident Navinder Singh Sarao and His Company Nav Sarao Futures Limited PLC with Price Manipulation and Spoofing.” RELEASE: PR7156-15. April 21, 2015. http://www.cftc.gov/PressRoom/PressReleases/pr7156-15.

24.

U.S. Securities and Exchange Commission. (2010) “Concept release on equity market structure.” Rep. No. 34-61358. https://www.sec.gov/rules/concept/2010/34-61358.pdf.

25.

U.S. Securities and Exchange Commission. (2014). Equity market structure literature review, Part II: High frequency trading.

Titel: Do high-frequency fleeting orders exacerbate market illiquidity?
verfasst von: Kun Li
Publikationsdatum: 22.11.2017
Verlag: Springer US
Erschienen in: Electronic Commerce Research / Ausgabe 2/2018
Print ISSN: 1389-5753
Elektronische ISSN: 1572-9362
DOI: https://doi.org/10.1007/s10660-017-9273-8

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