Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-745bb68f8f-l4dxg Total loading time: 0 Render date: 2025-02-10T21:33:03.999Z Has data issue: false hasContentIssue false

21 - Optimal Execution

from PART IX - PRACTICAL CONSEQUENCES

Published online by Cambridge University Press:  26 February 2018

Jean-Philippe Bouchaud ,
Julius Bonart ,
Jonathan Donier  and
Martin Gould
Jean-Philippe Bouchaud
Affiliation:
Capital Fund Management, Paris
Julius Bonart
Affiliation:
University College London
Jonathan Donier
Affiliation:
Capital Fund Management
Martin Gould
Affiliation:
CFM - Imperial Institute of Quantitative Finance
Get access

Summary

I will remember that I didn't make the world, and it doesn't satisfy my equations.

(Emanuel Derman and Paul Wilmott “The Modeler's Oath”)

Optimal execution is a major issue for financial institutions, in which asset managers, hedge funds, derivative desks, and many more seek to minimise the costs of trading. Such costs can consume a substantial fraction of the expected profit of a trading idea, which perhaps explains why most active managers fail to beat passive index investing in the long run. Market friction is especially relevant for high-turnover strategies. Given the importance of these considerations, it is unsurprising that a whole new branch of the financial industry has emerged to address the notion of best execution. Nowadays, many brokerage firms propose trading costs analysis (TCA) and optimised execution solutions for their buy-side clients.

Trading costs are usually classified into two groups:

  • • Direct trading costs are fees that must be paid to access a given market. These include brokerage fees, direct-access fees, transaction taxes, regulatory fees and liquidity fees. These fees are all relatively straightforward to understand and to measure. As a general rule, direct trading costs are of the order of 0.1–1 basis points and SEC regulatory fees are 0.01 basis points.

  • • Indirect trading costs arise due to market microstructure effects and due to the dynamics of the supply and demand. In contrast to direct trading costs, indirect trading costs are quite subtle. These costs include the bid–ask spread and impact costs. As we discussed in Chapter 16, the bid–ask spread is a consequence of the information asymmetry between different market participants and is determined endogenously by market dynamics. Spread costs are typically a few basis points in liquid markets, but can vary substantially over time, according to market conditions. Note that the tick size can have a considerable effect on the value of the spread, especially for large-tick stocks.

    • Impact costs are also a consequence of the bounded availability of liquidity. These costs also arise endogenously, but are deceptive because they are of a statistical nature. Impact is essentially invisible to the naked eye (after a buy trade, the price actually goes down about half the time, and vice-versa), and only appears clearly after careful averaging (see Chapter 12).

    Type
    Chapter
    Information
    Trades, Quotes and Prices
    Financial Markets Under the Microscope
     , pp. 384 - 405
    Publisher: Cambridge University Press
    Print publication year: 2018

    Access options

    Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

    References

    Perold, A. F. (1988). The implementation shortfall: Paper versus reality. The Journal of Portfolio Management, 14(3), 4–9.CrossRefGoogle Scholar
    Kissell, R. (2006). The expanded implementation shortfall: Understanding transaction cost components. The Journal of Trading, 1(3), 6–16.CrossRefGoogle Scholar
    Hendershott, T., Jones, C., & Menkveld, A. J. (2013). Implementation shortfall with transitory price effects. In Easley, D., Lopez de Prado, M, & O'Hara, M. (Eds.), High frequency trading: New realities for trades, markets and regulators. Risk Books.Google Scholar
    Hasbrouck, J. (2007). Empirical market microstructure: The institutions, economics, and econometrics of securities trading. Oxford University Press.Google Scholar
    Aldridge, I. (2009). High-frequency trading: A practical guide to algorithmic strategies and trading systems (Vol. 459). John Wiley and Sons.Google Scholar
    Cartea, A., Jaimungal, S., & Penalva, J. (2015). Algorithmic and high-frequency trading. Cambridge University Press.Google Scholar
    Guéant, O. (2016). The financial mathematics of market liquidity: From optimal execution to market-making (Vol. 33). CRC Press.Google Scholar
    Bertsimas, D., & Lo, A. W. (1998). Optimal control of execution costs. Journal of Financial Markets, 1(1), 1–50.CrossRefGoogle Scholar
    Almgren, R., & Chriss, N. (2001). Optimal execution of portfolio transactions. Journal of Risk, 3, 5–40.CrossRefGoogle Scholar
    Huberman, G., & Stanzl, W. (2005). Optimal liquidity trading. Review of Finance, 9(2), 165–200.CrossRefGoogle Scholar
    Kissell, R., & Malamut, R. (2006). Algorithmic decision-making framework. The Journal of Trading, 1(1), 12–21.Google Scholar
    Avellaneda, M., & Stoikov, S. (2008). High-frequency trading in a limit order book. Quantitative Finance, 8(3), 217–224.CrossRefGoogle Scholar
    Schied, A., & Schöneborn, T. (2009). Risk aversion and the dynamics of optimal liquidation strategies in illiquid markets. Finance and Stochastics, 13(2), 181–204.CrossRefGoogle Scholar
    Alfonsi, A., Fruth, A., & Schied, A. (2010). Optimal execution strategies in limit order books with general shape functions. Quantitative Finance, 10(2), 143–157.CrossRefGoogle Scholar
    Kharroubi, I., & Pham, H. (2010). Optimal portfolio liquidation with execution cost and risk. SIAM Journal on Financial Mathematics, 1(1), 897–931.CrossRefGoogle Scholar
    Gatheral, J.,& Schied, A. (2011). Optimal trade execution under geometric Brownian motion in the Almgren and Chriss framework. International Journal of Theoretical and Applied Finance, 14(03), 353–368.CrossRefGoogle Scholar
    Forsyth, P. A., Kennedy, J. S., Tse, S. T., & Windcliff, H. (2012). Optimal trade execution: A mean quadratic variation approach. Journal of Economic Dynamics and Control, 36(12), 1971–1991.CrossRefGoogle Scholar
    Guéant, O., Lehalle, C. A., & Fernandez-Tapia, J. (2012). Optimal portfolio liquidation with limit orders. SIAM Journal on Financial Mathematics, 3(1), 740–764.CrossRefGoogle Scholar
    Gârleanu, N., & Pedersen, L. H. (2013). Dynamic trading with predictable returns and transaction costs. The Journal of Finance, 68(6), 2309–2340.CrossRefGoogle Scholar
    Obizhaeva, A. A., & Wang, J. (2013). Optimal trading strategy and supply/demand dynamics. Journal of Financial Markets, 16(1), 1–32.CrossRefGoogle Scholar
    Schied, A. (2013). Robust strategies for optimal order execution in the Almgren-Chriss framework. Applied Mathematical Finance, 20(3), 264–286.CrossRefGoogle Scholar
    Guéant, O., & Royer, G. (2014). VWAP execution and guaranteed VWAP. SIAM Journal on Financial Mathematics, 5(1), 445–471.Google Scholar
    Cartea, A., & Jaimungal, S. (2015). Optimal execution with limit and market orders. Quantitative Finance, 15(8), 1279–1291.CrossRefGoogle Scholar
    Guéant, O., & Lehalle, C. A. (2015). General intensity shapes in optimal liquidation. Mathematical Finance, 25(3), 457–495.CrossRef
    Alfonsi, A., & Blanc, P. (2016). Dynamic optimal execution in a mixed-market-impact Hawkes price model. Finance and Stochastics, 20(1), 183–218.CrossRefGoogle Scholar
    Cartea, A., & Jaimungal, S. (2016). Incorporating order-flow into optimal execution. Mathematics and Financial Economics, 10(3), 339–364.CrossRefGoogle Scholar
    Guéant, O. (2016). Optimal market-making. arXiv:1605.01862.
    Curato, G., Gatheral, J., & Lillo, F. (2017). Optimal execution with non-linear transient market impact. Quantitative Finance, 17(1), 41–54.CrossRefGoogle Scholar
    Moreau, L., Muhle-Karbe, J., & Soner, H. M. (2017). Trading with small price impact. Mathematical Finance, 27(2), 350–400.CrossRefGoogle Scholar
    Gatheral, J. (2010). No-dynamic-arbitrage and market impact. Quantitative Finance, 10(7), 749–759.CrossRefGoogle Scholar
    Alfonsi, A., Schied, A., & Slynko, A. (2012). Order book resilience, price manipulation, and the positive portfolio problem. SIAM Journal on Financial Mathematics, 3(1), 511–533.CrossRefGoogle Scholar
    Gatheral, J., Schied, A., & Slynko, A. (2012). Transient linear price impact and Fredholm integral equations. Mathematical Finance, 22(3), 445–474.CrossRefGoogle Scholar
    Donier, J., Bonart, J., Mastromatteo, I., & Bouchaud, J. P. (2015). A fully consistent, minimal model for non-linear market impact. Quantitative Finance, 15(7), 1109–1121.CrossRefGoogle Scholar
    Handa, P., & Schwartz, R. A. (1996). Limit order trading. The Journal of Finance, 51(5), 1835–1861.CrossRefGoogle Scholar
    Harris, L., & Hasbrouck, J. (1996). Market vs. limit orders: The SuperDOT evidence on order submission strategy. Journal of Financial and Quantitative Analysis, 31(02), 213–231.CrossRefGoogle Scholar
    Handa, P., Schwartz, R. A., & Tiwari, A. (1998). The ecology of an order-driven market. The Journal of Portfolio Management, 24(2), 47–55.CrossRefGoogle Scholar
    Kaniel, R., & Liu, H. (2006). So what orders do informed traders use? The Journal of Business, 79(4), 1867–1913.CrossRefGoogle Scholar
    Wyart, M., Bouchaud, J. P., Kockelkoren, J., Potters, M., & Vettorazzo, M. (2008). Relation between bid-ask spread, impact and volatility in order-driven markets. Quantitative Finance, 8(1), 41–57.CrossRefGoogle Scholar
    Glosten, L. R. (1994). Is the electronic open limit order book inevitable? The Journal of Finance, 49(4), 1127–1161.CrossRefGoogle Scholar
    Stoikov, S. (2012). http://market-microstructure.institutlouisbachelier.org/uploads/ 91 3%20STOIKOV%20Microstructure talk.pdf.
    Farmer, J. D., & Skouras, S. (2013). An ecological perspective on the future of computer trading. Quantitative Finance, 13(3), 325–346.CrossRefGoogle Scholar
    Moallemi, C., & Yuan, K. (2014). The value of queue position in a limit order book. In Market microstructure: Confronting many viewpoints. Louis Bachelier Institute.Google Scholar

    Save book to Kindle

    To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

    Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

    Find out more about the Kindle Personal Document Service.

    Available formats
    ×

    Save book to Dropbox

    To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

    Available formats
    ×

    Save book to Google Drive

    To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

    Available formats
    ×