25885 Market Microstructure
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Credit points: 6 cp
PostgraduateResult type: Grade and marks
There are course requisites for this subject. See access conditions.
This course provides an introduction to the field of market microstructure. It covers microstructure theory, the current state of practice in market design/regulation and empirical models/methods used in microstructure research. Classic models of the trading process are discussed, describing the interaction of different types of traders, the nature of adverse selection, the effects of inventory management by liquidity providers, the causes of variation in liquidity, and the process by which information becomes reflected in prices. Also discussed is market design and how it affects the functioning of markets. This includes topical issues such as fragmentation of markets, transparency and automated/high-frequency trading. The subject places market microstructure in the broader context of finance by reviewing how market microstructure impacts on asset pricing and corporate finance decisions.
Subject learning objectives (SLOs)
|1.||Design and execute empirical analysis, including sourcing and processing microstructure data and estimating microstructure models/metrics|
|2.||Interpret results using theory and place them in the context of existing literature|
|3.||Discuss market design and how it influences outcomes|
|4.||Discuss how market microstructure interacts with other areas of finance such as asset pricing and corporate finance|
Contribution to the development of graduate attributes
The subject places an emphasis on preparing students to conduct empirical market microstructure research. This is achieved through several practical sessions in a computer laboratory, using the SAS statistical software package, allowing students to learn by doing. The empirical market microstructure models and metrics covered in the course are organised into the two categories: liquidity, including spreads, depth and price impact; and price discovery, including high-frequency measures of informational efficiency, ways of measuring the information content of individual trades, and models to quantify contributions to price discovery among multiple prices.
Teaching and learning strategies
The course is structured around three intensive weekends of face-to-face instruction, involving a mix of lectures/discussions and practical sessions in a computer laboratory. Students are expected to complete pre-reading before each weekend and will have practical tasks to work on between weekends. At the end of the course students will undertake a small microstructure research project, which will draw together the knowledge and skills gained during the course.
- Market design
- Working with large datasets
- Data sourcing
- Market microstructure models
- Data processing
- Assigning trade indicator
- Market microstructure and asset pricing
- Market microstructure and corporate finance
- Price discovery
- Informational efficiency
- High-frequency estimation
- Current issues in market microstructure
Assessment task 1: Exercises (individual)
This addresses subject learning objective(s):
1, 2, 3 and 4
Assessment task 2: Assignment (individual)
This addresses subject learning objective(s):
1, 2, 3 and 4
Students must achieve at least 50% of the subject’s total marks.
Readings as listed below.
See Appendix for the sequence in which the readings will be covered and which are compulsory vs optional/reference readings.
There are many references below. However, to make the reading manageable, only some are compulsory and the rest are there for students that want to dig deeper into a given topic, see some recent applications in a given topic, or find a classic reference. The readings below are a mix of the best and most up-to-date textbooks in microstructure, survey articles and chapters in edited books, and journal articles. The compulsory readings (underlined in the schedule in the Appendix) are often the textbook chapters and survey articles as they cover a lot of ground in an efficient and approachable manner, however, some of the journal articles (which tend to be narrower) are also compulsory to provide exposure to the way market microstructure papers are written.
Brunnermeier, M.K., 2003, Asset Pricing Under Information Asymmetry (Oxford University Press, Oxford).
De Jong, F., and B. Rindi, 2009, The Microstructure of Financial Markets (Cambridge University Press, New York).
Foucault, T., M. Pagano, and A. Röell, 2013. Market Liquidity: Theory, Evidence, and Policy (Oxford University Press, Oxford).
Hasbrouck, J., 2007, Empirical Market Microstructure (Oxford University Press, New York).
O'Hara, M., 1995, Market Microstructure Theory (Blackwell Publishers, Cambridge).
Amihud, Y., 2019. Illiquidity and stock returns: A revisit. Critical Finance Review (forthcoming).
Amihud, Y., and H. Mendelson, 2008, Liquidity, the value of the firm, and corporate finance, Journal of Applied Corporate Finance 20, 32–45.
Anthonisz, S., and T.J. Putnins, 2017, Asset pricing with downside liquidity risks, Management Science 63, 2549–2572.
Brogaard, J., T.H. Nguyen, T.J. Putnins, and E. Wu, 2018, What Moves Stock Prices? The Role of News, Noise, and Information. The Role of News, Noise, and Information, SSRN.
Comerton-Forde, C., and T.J. Putnins, 2015, Dark trading and price discovery, Journal of Financial Economics 118, 70–92.
Eaton, G.W., P.J. Irvine, and T. Liu, 2018, Measuring Institutional Trading Costs and the Implications for Finance Research: The Case of Tick Size Reductions. Available at SSRN 3255735.
Easley, D., M. Lopez de Prado, and M. O’Hara, 2016, Discerning information from trade data, Journal of Financial Economics 120, 269-285.
Foley, S., and T.J. Putnins, 2016, Should we be afraid of the dark? Dark trading and market quality, Journal of Financial Economics 122, 456–481.
Fox, M.B., L.R. Glosten, and G.V. Rauterberg, 2015, The new stock market: Sense and nonsense, Duke Law Journal 65, 191–277.
Fraeman, K., 2008, Common sense tips and clever tricks for programming with extremely large SAS data sets, Working paper.
Glosten, L.R., and P.R. Milgrom, 1985, Bid, ask, and transaction prices in a specialist market with heterogeneously informed traders, Journal of Financial Economics 14, 71–100.
Glosten, L.R., and T.J. Putnins, 2017, Welfare costs of informed trade, Working paper.
Goyenko, R.Y., C.W. Holden, and C.A. Trzcinka, 2009, Do liquidity measures measure liquidity?, Journal of Financial Economics 92, 153–181.
Hasbrouck, J., 1991, Measuring the information content of stock trades, Journal of Finance 46, 179–207.
Haslag, P., and M.C. Ringgenberg, 2017, The causal impact of market fragmentation on liquidity, Working paper.
Holden, C.W., S. Jacobsen, and A. Subrahmanyam, 2013, The empirical analysis of liquidity, Foundations and Trends in Finance 8, 263–365.
Hou, K., and T.J. Moskowitz, 2005, Market frictions, price delay, and the cross-section of expected returns, Review of Financial Studies 18, 981–1020.
Kyle, A., 1985, Continuous auctions and insider trading, Econometrica 53, 1315–1335.
Lee, C.M.C., and M.J. Ready, 1991, Inferring trade direction from intraday data, Journal of Finance 46, 733–746.
Lo, A., and C. MacKinlay, 1988, Stock market prices do not follow random walks: Evidence from a simple specification test, Review of Financial Studies 1, 41–66.
Mahoney, P.G., and G. Rauterberg, 2017, The regulation of trading markets: A survey and evaluation, Virginia Law and Economics Research Paper No. 2017-07.
Menkveld, A., 2014, High frequency traders and market structure, Financial Review 49, 333–344.
Menkveld, A.J., 2016, The economics of high-frequency trading: Taking stock, Annual Review of Financial Economics 8, 1–24.
O'Hara, M., 2015, High frequency market microstructure, Journal of Financial Economics 116, 257–270.
Pastor, L. and R.F. Stambaugh, 2019, Liquidity Risk After 20 Years. National Bureau of Economic Research.
Petersen, M.A., 2009, Estimating standard errors in finance panel data sets: Comparing approaches, Review of Financial Studies 22, 435–480.
Pirrong, C., 2014, Pick your poison—Fragmentation or market power? An analysis of RegNMS, high frequency trading, and securities market structure, Journal of Applied Corporate Finance 26, 8–14.
Putnins, T.J., 2013, What do price discovery metrics really measure?, Journal of Empirical Finance 13, 3–33.
Putnins, T.J., and J. Barbara, 2018, Heterogeneity in the effects of algorithmic and high-frequency traders on institutional transaction costs, Working paper.
Rösch, D.M., A. Subrahmanyam, and M.A. van Dijk, 2017, The dynamics of market efficiency, Review of Financial Studies 30, 1151–1187.
Thompson, S.B., 2011, Simple formulas for standard errors that cluster by both firm and time, Journal of Financial Economics 99, 1–10.
Van Kervel, V. and A.J. Menkveld, 2019. High?frequency trading around large institutional orders. Journal of Finance (forthcoming).