Skip to main content Accessibility help
×
Home
  • Print publication year: 2018
  • Online publication date: February 2018

21 - Optimal Execution

from PART IX - PRACTICAL CONSEQUENCES

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).

Related content

Powered by UNSILO
Perold, A. F. (1988). The implementation shortfall: Paper versus reality. The Journal of Portfolio Management, 14(3), 4–9.
Kissell, R. (2006). The expanded implementation shortfall: Understanding transaction cost components. The Journal of Trading, 1(3), 6–16.
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.
Hasbrouck, J. (2007). Empirical market microstructure: The institutions, economics, and econometrics of securities trading. Oxford University Press.
Aldridge, I. (2009). High-frequency trading: A practical guide to algorithmic strategies and trading systems (Vol. 459). John Wiley and Sons.
Cartea, A., Jaimungal, S., & Penalva, J. (2015). Algorithmic and high-frequency trading. Cambridge University Press.
Guéant, O. (2016). The financial mathematics of market liquidity: From optimal execution to market-making (Vol. 33). CRC Press.
Bertsimas, D., & Lo, A. W. (1998). Optimal control of execution costs. Journal of Financial Markets, 1(1), 1–50.
Almgren, R., & Chriss, N. (2001). Optimal execution of portfolio transactions. Journal of Risk, 3, 5–40.
Huberman, G., & Stanzl, W. (2005). Optimal liquidity trading. Review of Finance, 9(2), 165–200.
Kissell, R., & Malamut, R. (2006). Algorithmic decision-making framework. The Journal of Trading, 1(1), 12–21.
Avellaneda, M., & Stoikov, S. (2008). High-frequency trading in a limit order book. Quantitative Finance, 8(3), 217–224.
Schied, A., & Schöneborn, T. (2009). Risk aversion and the dynamics of optimal liquidation strategies in illiquid markets. Finance and Stochastics, 13(2), 181–204.
Alfonsi, A., Fruth, A., & Schied, A. (2010). Optimal execution strategies in limit order books with general shape functions. Quantitative Finance, 10(2), 143–157.
Kharroubi, I., & Pham, H. (2010). Optimal portfolio liquidation with execution cost and risk. SIAM Journal on Financial Mathematics, 1(1), 897–931.
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.
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.
Guéant, O., Lehalle, C. A., & Fernandez-Tapia, J. (2012). Optimal portfolio liquidation with limit orders. SIAM Journal on Financial Mathematics, 3(1), 740–764.
Gârleanu, N., & Pedersen, L. H. (2013). Dynamic trading with predictable returns and transaction costs. The Journal of Finance, 68(6), 2309–2340.
Obizhaeva, A. A., & Wang, J. (2013). Optimal trading strategy and supply/demand dynamics. Journal of Financial Markets, 16(1), 1–32.
Schied, A. (2013). Robust strategies for optimal order execution in the Almgren-Chriss framework. Applied Mathematical Finance, 20(3), 264–286.
Guéant, O., & Royer, G. (2014). VWAP execution and guaranteed VWAP. SIAM Journal on Financial Mathematics, 5(1), 445–471.
Cartea, A., & Jaimungal, S. (2015). Optimal execution with limit and market orders. Quantitative Finance, 15(8), 1279–1291.
Guéant, O., & Lehalle, C. A. (2015). General intensity shapes in optimal liquidation. Mathematical Finance, 25(3), 457–495.
Alfonsi, A., & Blanc, P. (2016). Dynamic optimal execution in a mixed-market-impact Hawkes price model. Finance and Stochastics, 20(1), 183–218.
Cartea, A., & Jaimungal, S. (2016). Incorporating order-flow into optimal execution. Mathematics and Financial Economics, 10(3), 339–364.
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.
Moreau, L., Muhle-Karbe, J., & Soner, H. M. (2017). Trading with small price impact. Mathematical Finance, 27(2), 350–400.
Gatheral, J. (2010). No-dynamic-arbitrage and market impact. Quantitative Finance, 10(7), 749–759.
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.
Gatheral, J., Schied, A., & Slynko, A. (2012). Transient linear price impact and Fredholm integral equations. Mathematical Finance, 22(3), 445–474.
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.
Handa, P., & Schwartz, R. A. (1996). Limit order trading. The Journal of Finance, 51(5), 1835–1861.
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.
Handa, P., Schwartz, R. A., & Tiwari, A. (1998). The ecology of an order-driven market. The Journal of Portfolio Management, 24(2), 47–55.
Kaniel, R., & Liu, H. (2006). So what orders do informed traders use? The Journal of Business, 79(4), 1867–1913.
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.
Glosten, L. R. (1994). Is the electronic open limit order book inevitable? The Journal of Finance, 49(4), 1127–1161.
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.
Moallemi, C., & Yuan, K. (2014). The value of queue position in a limit order book. In Market microstructure: Confronting many viewpoints. Louis Bachelier Institute.