market microstructure Concepts

Empirical Market Microstructure: The Institutions, Economics and Econometrics of Securities Trading

by Joel Hasbrouck · 4 Jan 2007 · 209pp · 13,138 words

means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Oxford University Press. Library of Congress Cataloging-in-Publication Data Hasbrouck, Joel. Empirical market microstructure: the institutions, economics, and econometrics of securities trading/Joel Hasbrouck. p. cm. Includes bibliographical references and index. ISBN-13: 978-0-19-530164-9 ISBN

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the book presupposes only a basic familiarity with economics and statistics. I began writing this book because I perceived a need for treatment of empirical market microstructure that was unified, authoritative, and comprehensive. The need still exists, and perhaps someday when the field has reached a point of perfection and stasis

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realized only after trade has occurred. In security markets, the common value component reflects the cash flows from the security, as summarized 3 4 EMPIRICAL MARKET MICROSTRUCTURE in the present value of the flows or the security’s resale value. Private value components arise from differences in investment horizon, risk exposure, endowments

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provoking: “Liquidity is created through a give and take process in which multiple counterparties selectively reveal information in exchange for information ultimately 5 6 EMPIRICAL MARKET MICROSTRUCTURE leading to a trade.” The words are taken from the offering materials for the ICor Brokerage (an electronic swaps trading platform). It is a practical

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a few years previous may be seriously out of date. INTRODUCTION 1.5 The Questions Here is a partial list of significant outstanding questions in market microstructure: • • • • • • • • What are optimal trading strategies for typical trading problems? Exactly how is information impounded in prices? How do we enhance the information aggregation process?

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. Hamilton (1994) is the key reference here, and the present discussion often refers the reader to Hamilton for greater detail. For other 7 8 EMPIRICAL MARKET MICROSTRUCTURE econometric techniques (in particular, duration and limited dependent variable models), Greene (2002) is particularly useful. Alexander (2001), Gourieroux and Jasiak (2001) and Tsay (2002)

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the change. Through the mid-1990s, the market for futures based on German government debt (Bund futures) was dominated by a contract 13 14 EMPIRICAL MARKET MICROSTRUCTURE that was floor-traded on the London International Financial Futures Exchange (LIFFE). In 1997, the Deutsche Terminbörse (DTB, now Eurex) began to aggressively market an

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defined by its participants, not by the mechanism. Analyses of interdealer markets include Reiss and Werner (1998) and Viswanathan and Wang (2004). 15 16 EMPIRICAL MARKET MICROSTRUCTURE Dealer markets are typically flexible. The fixed technology and infrastructure costs are low. The main barrier to entry is access to a set of customers

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mechanism. A single-price clearing avoids these problems. It is generally implemented with a single-price double-sided auction. Supply and demand 17 18 EMPIRICAL MARKET MICROSTRUCTURE curves are constructed by ranking bids and offers. Prices, quantities, and trader identities are usually determined by maximizing the feasible trading volume. The double-sided

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payoff, and knowing this, the rational allocator keeps for him- or herself almost all of the total payoff. In practice, recipients often 19 20 EMPIRICAL MARKET MICROSTRUCTURE reject proposals perceived as unfair, and this forces allocators to discipline their greed. The economic literature on these games in voluminous. Thaler (1988) and Camerer

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price components due to fundamental security value and those attributable to the market organization and trading process. The former arise from information 23 24 EMPIRICAL MARKET MICROSTRUCTURE Figure 3.1. PCO price record at different time scales. about future security cash flows and are long-lasting, whereas the latter are transient.

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of estimators as the sample size increases. The usual forms of these theorems apply to data samples consisting of independent observations. 31 32 EMPIRICAL MARKET MICROSTRUCTURE Time-series data are by nature dependent. To maintain the strength of the LLN and CLT when independence doesn’t hold, we rely on alternative

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zero. Intuitively, the effects of lagged realizations eventually die out. When a convergent autoregressive representation exists, the moving average representation is said 35 36 EMPIRICAL MARKET MICROSTRUCTURE to be invertible. Convergence is determined by the magnitude of θ. The condition |θ| < 1 thus defines the invertible solution for the MA(1)

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neither stationary nor ergodic. The dynamics are not time-homogenous, although path realizations can be sequentially stacked to provide a semblance of ongoing trading. Theoretical market microstructure has two main sorts of asymmetric information models. In the sequential trade models, randomly selected traders arrive at the market singly, sequentially, and independently.

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a trader who has private negative information. Rational, competitive market makers will set their bid and ask quotes accordingly. All else equal, 43 44 EMPIRICAL MARKET MICROSTRUCTURE more extreme information asymmetries lead to wider quotes. Trades will also engender a “permanent” impact on subsequent prices. The spread and trade-impact effects are

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) = δk (Orderk ; δk − 1 (Orderk − 1 ; δk − 2 (Orderk − 2 ; . . . ))). Verify that δ2 (Sell1 , Buy2 ) = δ. That is, offsetting trades are uninformative. 47 48 EMPIRICAL MARKET MICROSTRUCTURE Market dynamics have the following features: • The trade price series is a martingale. Recall from the foregoing • • • • analysis that Bk = E[V |Sellk ] and Ak

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are generally indistinguishable. The usual (Granger-Sims) test for causality is implemented as a test of forecasting ability (Hamilton (1994) p. 302). 53 54 EMPIRICAL MARKET MICROSTRUCTURE Focusing on the signal extraction process leads to interesting implications. Most important, market dynamics reflect the beliefs of market participants, not necessarily the reality. In

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of this distribution are En = Var(n) = θ.) • The duration τ between two successive arrivals is exponentially distributed: f (τ) = τe−λτ . 57 58 EMPIRICAL MARKET MICROSTRUCTURE Figure 6.2. Sequential trade model with event uncertainty and Poisson arrivals. • If two types of traders arrive independently with intensities λ1 and λ2 , then

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theoretical analyses. The following list is suggestive of the directions, but far from comprehensive. Back (1992) develops continuous time properties. Back and 65 66 EMPIRICAL MARKET MICROSTRUCTURE Figure 7.1. Market impact and number of auctions. Baruch (2004) explore the links between the strategic and sequential trade models. Notably, in their common

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Walk Decompositions Up to now, the chapter has explored the correspondence between a known structural model and its statistical representations. This was 71 72 EMPIRICAL MARKET MICROSTRUCTURE useful for illuminating what we could (and could not) learn about the former from the latter. Here we ask what can be inferred starting

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measure. If the prices are measured in logs, λσv is approximately the standard deviation of the trade-drive return component. The quantity 85 86 EMPIRICAL MARKET MICROSTRUCTURE 2 is a relative measure, essentially the coefficient of determination λ2 σv2 /σw in a project of price changes on trades. More generally, decomposition

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other markets tend to follow its moves. It is the venue where most value-relevant information is first revealed. The social 101 102 EMPIRICAL MARKET MICROSTRUCTURE welfare importance of an informationally efficient price is appreciated by regulators. The dominant market can assert that it is the largest producer of an important

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 Pa   Pa Pb  ∆  J (Preferred position)  Pb ∆ Inventory level Figure 11.2. The dependence of bid and ask prices on inventory position. 109 110 EMPIRICAL MARKET MICROSTRUCTURE 11.2 Risk Aversion and Dealer Behavior In the Amihud and Mendelson model, an extreme inventory position depresses the expected profits of a risk-neutral

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the positions for stock A. The salient features are: • Inventory are sometimes negative (short positions). • There is no obvious drift or divergence. 111 112 EMPIRICAL MARKET MICROSTRUCTURE Figure 11.3. Inventory for stock A. • The mean inventory is near zero. Although not obvious from the figure, overnight positions are small: Dealers tend

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to trades, with inventories contributing little explanatory power. Positions are not apparently managed by adjustment of publicly quoted bids and offers. 115 116 EMPIRICAL MARKET MICROSTRUCTURE This should not be too surprising. A dealer using public quotes would be signaling to the world at large his desire to buy or sell

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The Parlour prediction that same-side depth favors a market order is supported by Renaldo, Ellul et al., and Hasbrouck and Saar. 129 130 EMPIRICAL MARKET MICROSTRUCTURE Support for the hypothesis that an increase in opposite-side depth favors limit orders, however, is less clear. The effects of volatility in the crosssection

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side of the book). The next steepest lines (dash/dotted) are the median book price schedules. The differences between mean and 131 132 EMPIRICAL MARKET MICROSTRUCTURE Figure 13.1. Estimated limit order book and price revision schedules for two representative OMX stocks. median are suggestive of outliers, times when depth is

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the endowment n. More precisely, we assume that across the population of potential customers, endowments are normally distrubted, n ∼ N (0, σn2 ), 135 136 EMPIRICAL MARKET MICROSTRUCTURE independent of X and ε. The customer’s trade now arises, in the liquidity suppliers’ view, from a mix of informational and noninformational motives. The

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losses at q = 6 larger than the figure indicates. Figure 13.3. Price and expectations revision schedules for a monopolistic dealer. 139 140 EMPIRICAL MARKET MICROSTRUCTURE 13.4 Additional Empirical Evidence on Limit Order Book Price Schedules Sandas (2001) estimates for his OMX data a modified version of the Glosten model

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desk. The framing perspectives of the decisions can be characterized plainly as long-term versus short-term, but there are usually 143 144 EMPIRICAL MARKET MICROSTRUCTURE fundamental and far-reaching differences in the skills and information sets of the two groups. The portfolio manager’s communication of an order to the

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be negative. An execution cost may be negative, for example, if the stock is purchased below the benchmark. An opportunity cost 145 146 EMPIRICAL MARKET MICROSTRUCTURE may be negative if an intended purchase was not completed for a stock that subsequently declined in value. The separation of investment and trading decisions

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execution and opportunity costs, however, is somewhat strained. For one thing, given that v = n0 , the opportunity cost actually applies to 147 148 EMPIRICAL MARKET MICROSTRUCTURE filled orders. The risk-neutral liquidity suppliers (such as the dealers in the sequential trade models or the limit order traders in Glosten 1994) are

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short relative to portfolio turnover. The Plexus Group, for example, computes the opportunity cost of unexecuted orders over a 30-day window. 149 150 EMPIRICAL MARKET MICROSTRUCTURE A number of firms in the trading cost industry have shared order data with academic researchers (having taken steps to maintain customer anonymity). Studies of

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noninformational components of the bid-ask spread). It is also common in strategy analyses to allow trades to have permanent price effects. 153 154 EMPIRICAL MARKET MICROSTRUCTURE To facilitate the reader’s access to this literature, this presentation will also allow for permanent effects. It should be emphasized, however, that trading strategies

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because counterparties (and regulatory authorities) may view the intent of the initial sale as establishment of an “artificial” (i.e., “manipulative”) price. 157 158 EMPIRICAL MARKET MICROSTRUCTURE 15.2 Models of Order Placement The next analysis also deals with a purchase under a time constraint but with different emphasis. In the models

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One distinctive feature is particularly relevant. If the diffusion price process has zero drift, then it will eventually (with probability one) hit 163 164 EMPIRICAL MARKET MICROSTRUCTURE any finite barrier. This implies that any limit order will eventually execute. This is also true of the exponential model (if λ > 0). In the

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Yakov, and Haim Mendelson, 1987, Trading mechanisms and stock returns: An empirical investigation, Journal of Finance 42, 533–53. Amihud, Yakov, and Haim Mendelson, 1991. Market microstructure and price discovery on the Tokyo Stock Exchange, in William T. Ziemba, Warren Bailley, and Yasushi Hamao, eds., Japanese Financial Market Research, Contributions to Economic

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with particular attention to measurement of the “business cycle,” Journal of Monetary Economics 7, 151–74. Biais, Bruno, Lawrence R. Glosten and Chester Spatt, 2005, Market microstructure: A survey of microfoundations, empirical results, and policy implications. Journal of Financial Markets 8, 217–64. Biais, Bruno, Pierre Hillion and Chester Spatt, 1995, An

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Stuart, 2001, An Introduction to the Statistical Modeling of Extreme Values (Springer-Verlag, London). Comerton-Forde, Carole, and James Rydge, 2004, A review of stock market microstructure (Securities Industry Research Centre of Asia-Pacific, Sydney). Conrad, Jennifer S., Kevin M. Johnson, and Sunil Wahal, 2001, Institutional trading and soft dollars, Journal of

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Gopikrishnan, Vasiliki Plerou, and H. Eugene Stanley, 2003, A theory of power law distributions in financial market fluctuations, Nature 423, 267–70. Garman, Mark, 1976, Market microstructure, Journal of Financial Economics 3, 257–75. Gatev, Evan, William N. Goetzmann and K. Geert Rouwenhorst, 2006, Pairs trading: Performance of a relative-value arbitrage

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price evolution: An application to program trading, Journal of Financial Economics 41, 129–49. 189 190 REFERENCES Hasbrouck, Joel, 2002, Stalking the “efficient price” in market microstructure specifications: an overview, Journal of Financial Markets 5, 329–39. Hasbrouck, Joel, 2005, Trading costs and returns for US equities: The evidence from daily data

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Macey, Jonathan R., and Maureen O’Hara, 1997, The law and economics of best execution, Journal of Financial Intermediation 6, 188–223. Madhavan, Ananth, 2000, Market microstructure: A survey, Journal of Financial Markets 3, 205–58. 191 192 REFERENCES Madhavan, Ananth, and Minder Cheng, 1997, In search of liquidity: Block trades in

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. Odders-White, Elizabeth R., 2000, On the occurrence and consequences of inaccurate trade classification, Journal of Financial Markets 3, 259–86. O’Hara, Maureen, 1995, Market Microstructure Theory (Blackwell, Cambridge, MA). Parlour, Christine A., 1998, Price dynamics in limit order markets, Review of Financial Studies 11, 789–816. Perold, Andre, 1988,

Trading and Exchanges: Market Microstructure for Practitioners

by Larry Harris · 2 Jan 2003 · 1,164pp · 309,327 words

. Emery Real Estate Investment Trusts: Structure, Performance, and Investment Opportunities Su Han Chan, John Erickson, and Ko Wang Trading and Exchanges: Market Microstructure for Practitioners Larry Harris Trading and Exchanges market microstructure for Practitioners LARRY HARRIS Oxford New York Auckland Bangkok Buenos Aires Cape Town Chennai Dar es Salaam Delhi Hong Kong Istanbul Karachi

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, photocopying, recording, or otherwise, without the prior permission of Oxford University Press. Library of Congress Cataloging-in-Publication Data Harris, Larry, 1956– Trading and exchanges : market microstructure for practitioners / Larry Harris p. cm.—(Financial Management Association survey and synthesis series) Includes bibliographical references and index. ISBN 0-19-514470-8 1. Markets

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much better for their contributions. I received my first encouragement as a graduate student at the University of Chicago, long before I knew anything about market microstructure. Professor Arnold Zellner advised me to publish a book based on lectures I would give when I became a professor. Although it seemed unimaginable to

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a colleague at USC. Finally, Jack Treynor was instrumental in helping me appreciate the importance of the zero-sum game in trading. Most principles of market microstructure somehow involve properties of zero-sum games. Several generous sponsors provided financial support for this project. I received “angel financing” from the New York Stock

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to value them, who should trade them, how to design them, or how to issue them. Books about investments and corporate finance examine these questions. Market microstructure is the branch of financial economics that investigates trading and the organization of markets. This field of study has substantially grown in size and importance

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since the October 1987 stock market crash. This book presents the economics of market microstructure in simple English prose. Although some simple mathematics and graphics appear in a few supplementary examples, I fully explain all essential concepts in the main

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this book is to understand how market structure—trading rules and information systems—affect each of these five market characteristics. 1.3 INSTRUMENTS AND MARKETS Market microstructure examines organized trading in instruments. Instruments include common stocks, preferred stocks, bonds, convertible bonds, warrants, options, futures contracts, forward contracts, foreign exchange contracts, swaps, reinsurance

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that benefits other people without compensation. People create negative externalities when they do something that harms other people without penalty. The most important externality in market microstructure is the order flow externality Traders who offer to trade give other traders valuable options to trade for which the offerers are not compensated. The

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arise when agents do what they want to do rather than what their principals want them to do. The most important principal–agent problem in market microstructure involves brokers and their clients. Brokers do not always do what you want them to do, and they may not work as hard on your

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three traditional definitions of market efficiency do not recognize that acquiring and acting on information is costly. The following definition is more sensitive to these market microstructure issues. In an efficient market, prices reflect all information that traders can acquire and profitably trade upon. This definition implicitly incorporates the costs of acquiring

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to control excess volatility. Chapter 29 considers the benefits and consequences of prohibiting insider trading. Interestingly, the most important issues involve labor economics rather than market microstructure. 23 Index and Portfolio Markets Index trading is one of the most important financial innovations of the twentieth century. The nominal dollar value of trading

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AND CRASH EXAMPLES This section describes several bubbles and crashes to illustrate how bubbles and crashes occur. I selected these events because they involve important market microstructure issues. Traders and regulators are very familiar with the most important of these examples. If you intend to work in the markets, you should be

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chapter outline of this book. Since some articles and books cover topics that appear in many of the chapters, the classification is somewhat arbitrary. The market microstructure literature has grown large very quickly. This bibliography therefore is not comprehensive. I included many works because they provide the first clear presentation of a

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of many excellent works from this bibliography therefore reflects more on me than on them. General Works Belonsky, Gail M., and David M. Modest. 1993. Market microstructure: An empirical retrospective. Working paper, Haas School of Business, University of California, Berkeley. Cohen, Kalman J., Steven F. Maier, Robert A. Schwartz, and David K

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. Whitcomb. 1986. The Microstructure of Securities Markets (Prentice-Hall, Englewood Cliffs, NJ). Coughenour, Jay, and Kuldeep Shastri. 1999. Symposium on market microstructure: A review of empirical research. Financial Review 34(4), 1–28. Dalton, John M., ed. 1993. How the Stock Market Works (New York Institute of

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York). Lyons, Richard K. 2002. The Microstructure Approach to Exchange Rates (MIT Press, Cambridge, MA). Madhavan, Ananth. 2000. Market microstructure: A survey. Journal of Financial Markets 3(3), 205–258. O’Hara, Maureen. 1995. Market Microstructure Theory (Basil Blackwell, Cambridge, MA). Schwartz, Robert A. 1991. Reshaping the Equity Markets: A Guide for the 1990s

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Mendelson. 1987. Trading mechanisms and stock returns: An empirical investigation. Journal of Finance 42(3), 533–553. Amihud, Yakov, Haim Mendelson, and Beni Lauterbach. 1997. Market microstructure and securities values: Evidence from the Tel Aviv Stock Exchange. Journal of Financial Economics 45(3), 365–390. Ball, Clifford A., Walter A. Torous, and

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. Journal of Finance 46(4), 1411–1426. Black, Fischer. 1986. Noise. Journal of Finance 41(3), 529–543. Brennan, Michael J., and Avanidhar Subrahmanyam. 1996. Market microstructure and asset pricing: On the compensation for illiquidity in stock returns. Journal of Financial Economics 41(3), 441–464. Chapter 10: Informed Traders and Market

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–511. Forster, Margaret M., and Thomas J. George. 1992. Anonymity in securities markets. Journal of Financial Intermediation 2(2), 168–206. Garman, Mark B. 1976. Market microstructure. Journal of Financial Economics 3(3), 257–275. Hansch, Oliver, Narayan Y. Naik, and S. Viswanathan. 1998. Do inventories matter in dealership markets? Evidence from

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, 106 ticker symbols, 99–101 transparency, 101–2 market makers, 195, 196, 249, 279, 401–2, 406 market manipulation, 259, 306 market meltdowns. See crashes market microstructure, 3, 4 market-not-held orders, 82, 87, 530 market offer. See best offer market-on-close orders, 83, 84 market-on-open orders, 83

Topics in Market Microstructure

by Ilija I. Zovko · 1 Nov 2008 · 119pp · 10,356 words

Topics in Market Microstructure Ilija I. Zovko Topics in M a rk et Microstru c t u re ! The publication of this book is in part made possible by

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any means (electronic, mechanical, photocopying, recording or otherwise) without the written permission of both the copyright owner and the author of the book. ! Topics in Market Microstructure Academisch Proefschrift ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus prof. dr. D.C. van

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heterogen. and stock returns . . . . . . . . 5.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . 77 77 80 82 94 6 Conclusions 97 vi Chapter 1 Introduction The topic of this thesis is Market microstructure. Market microstructure is an area of finance that studies the dynamics and processes through which investors’ forecasts about future asset values are ultimately translated into the assets

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actions of traders. In even broader terms, research directions that deal with the interrelation between institutional structure, strategic behavior, prices and welfare are all considered market microstructure. The topics investigated in this thesis are also related to the field of Econophysics. Econophysics is a multidisciplinary field where ideas from physics and economics

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. Tsallis, Eds., Springer, 2007. 1 CHAPTER 1. INTRODUCTION ture on heterogeneous agent behavior in finance. The final chapter, Chapter 5, is related to agent heterogeneity, market microstructure, and information content of trades. 1.1 The London Stock Exchange and the LSE data The research in this thesis is based on a dataset

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the literature on random process models of the continuous double auction, which is more closely related to the model we test here. Standard literature The market microstructure literature focusing on the understanding of spread, volatility and market impact in financial markets is theoretically and empirically extensive. The theoretical analyses traditionally use the

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there. This and other things seem interesting for future research. 95 Chapter 6 Conclusions Unifying ideas behind the four chapters comprising this thesis are both market microstructure and an agent based view of trading in financial markets. In the first chapter we have shown that there exist some properties of aggregate trader

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microstructure approach. Journal of Financial and Quantitative Analysis, 36(1):25–51, 2001. 101 BIBLIOGRAPHY D. Eliezer and I. I. Kogan. Scaling laws for the market microstructure of the interdealer broker markets. SSRN eLibrary, 1998. doi: 10.2139/ssrn.147135. R. F. Engle. Autoregressive conditional heteroscedasticity with estimates of the variance of

Handbook of Modeling High-Frequency Data in Finance

by Frederi G. Viens, Maria C. Mariani and Ionut Florescu · 20 Dec 2011 · 443pp · 51,804 words

Results, 227 Application to the S&P Index, 228 219 viii Contents 8.5 Conclusion, 229 References, 230 part Three Analytical Results 233 9 A Market Microstructure Model of Ultra High Frequency Trading 235 Carlos A. Ulibarri and Peter C. Anselmo 9.1 9.2 9.3 9.4 Introduction, 235 Microstructural

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directly. The old Wall Street adage that ‘‘it takes volume to move prices’’ is veriﬁed in this empirical study. Indeed, this relationship was studied using market microstructure models and it was generally found true (Admati and Pﬂeiderer, 1988; Foster and Viswanathan, 1990; Llorente et al., 2002). The advent of electronic trading using

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Anal 1987;22(4):419–438. Taylor S. Modeling ﬁnancial timeseries. John Wiley and Sons, New York; 1986. Part Three Analytical Results Chapter Nine A Market Microstructure Model of Ultra High Frequency Trading C A R LO S A . U L I B A R R I a n d PE T

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G. Viens, Maria C. Mariani, and Ionuţ Florescu. © 2012 John Wiley & Sons, Inc. Published 2012 by John Wiley & Sons, Inc. 235 236 CHAPTER 9 A Market Microstructure Model milliseconds—akin to gathering pennies at the rate of some 1000 times per second (1000 ms) or more. UHFT market activities have received critical

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also assume the stochastic order ﬂow process is noninformative in regard to future price movements and other market parameters. Of course, no abstract model of market microstructure can capture the full complexity of market phenomenon, and the present framework is no exception. Here, we take a pragmatic approach in studying the UHFT

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be treated directly in a more general information-based framework. See O’Hara (2008) for a review of information-based models. 238 CHAPTER 9 A Market Microstructure Model The following lemma describes the limiting probability of k orders arriving in the base time interval [0, t = 1], and thus the essence of

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arbitrage proﬁts to dissipate in the time interval t require μ̄N − γ̄N > 0 or γ̄N − μ̄N > 0. 240 CHAPTER 9 A Market Microstructure Model To model order generation tipping toward UHFT, let N1 = αN denote UHFT dealer-agents and N2 = (1 − α)N all other agents where 0

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also acknowledged to ICASA (Institute of Complex Additive Systems Analysis), New Mexico Institute of Mining and Technology. 242 CHAPTER 9 A Market Microstructure Model REFERENCES Ait-Shalia Y, Yu J. High frequency market microstructure noise estimates and liquidity measures. Ann Appl Stat 2009;1:422–457. Campbell JY, Lo AW, MacKinlay C. The econometrics

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informed trading. J Portfolio Management 2011;37:118–128. Engle RE. The econometrics of ultra-high frequency data. Econometrica 2000;1:1–22. Garman M. Market microstructure. J Financ Econ 1976;3:257–275. Iati R. High frequency trading technology. TABB Group; 2009. Johnson J. Probability and statistics for computer science. John

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2011. Available at http://ssrn.com/abstract=1686004. Mandelbrot B, Hudson RL. The (mis) behavior of markets. New York: Basic Books; 2004. O’Hara M. Market microstructure theory. Malden, MA: Blackwell Publishing; 2008. Podobnik B et al. ARCH-GARCH approaches to modeling high-frequency ﬁnancial data. Physica 2004;344:216–220. Stigler

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–volatility covariance) can be obtained by the Fourier method. In Section 10.3, the ﬁnite sample properties of the Fourier estimator of integrated volatility under market microstructure noise are studied. Analytic expressions for the bias and the mean squared error (MSE) of the contaminated estimator are derived, and an empirical analysis based

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of the Fourier estimator. In particular, a feasible procedure to design an optimal MSE-based estimator is derived. Section 10.4 analyzes the effects of market microstructure on the Fourier estimator of multivariate integrated volatilities. We prove that with high frequency data, the estimator has a competitive performance even in comparison to

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observations and when the noise component is relevant; in general, it has a better performance even in comparison to the methods speciﬁcally designed to handle market microstructure contaminations. Finally, in Section 10.6, we consider the gains offered by the Fourier estimator over other covariance measures from the perspective of an asset

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(t) as the log-price in equilibrium, that is, the price that would prevail in the absence of market microstructure frictions. The econometrician does not observe the true return series but the returns contaminated by market microstructure effects. Therefore, an estimator of the integrated volatility should be constructed using the contaminated returns. Suppose that

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10 Multivariate Volatility Estimation by Fourier Methods integrated volatility in the hypothesis that the prices are observed without measurement errors. However, in practice, because of market microstructure noise, sampling at the highest frequency leads to a bias problem (Zhou, 1996). Under the hypothesis that 2π/n is the time distance between adjacent

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for estimation, that is, by choosing Ncut small, it is in principle possible to render the Fourier estimator invariant to short-run noise introduced by market microstructure effects. The analysis above suggests to use quote-to-quote returns and try to minimize the MSE as a function of the cutting frequency Ncut

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MSE-based Fourier estimators perform very well in terms of MSE, while having only a slightly higher bias. At higher sampling frequencies, the impact from market microstructure effects becomes more evident and the realized volatility becomes progressively unstable. At the highest frequency, the realized kernels provide the best estimate both in terms

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the Fourier estimator for very high frequencies. Hence, the Fourier method remains a very attractive estimator even in comparison with methods speciﬁcally designed to handle market microstructure contaminations. More speciﬁcally, the Fourier estimator is competitive in terms of MSE for sampling frequencies up to 30 s, while having only a slightly higher

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computation of the covariance of ﬁnancial asset returns: the distortion from efﬁcient 264 CHAPTER 10 Multivariate Volatility Estimation by Fourier Methods prices due to the market microstructure contamination and the so-called Epps effect (Epps, 1979). By means of the Fourier–Fejer summation, as suggested in and Mancino (2009), the Fourier estimator

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is relevant. Compared to other realized volatility measures, the Fourier estimator generally has a better performance, even in comparison with methods speciﬁcally designed to handle market microstructure contaminations, for example, the Zhou (1996) estimator, the two-scale estimator by Zhang et al. (2005), or the realized kernel estimators by Barndorff-Nielsen et

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and Sanfelici (2008). Hence, by choosing a small number of low frequency ordinates, it is in principle possible to render the Fourier estimator invariant to market microstructure effects. We now compare the quality of the forecasts obtained through the Fourier method with that obtained by variants of the realized volatility estimator that

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gains yielded by the Fourier methodology are statistically signiﬁcant and can be economically large, while only the subsampled AO estimator and, for low levels of market microstructure noise, the realized covariance with one lead–lag bias correction and suitable sampling frequency can be competitive. REFERENCES Ait-Sahalia Y, Mancini L. Out of

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value of this coefﬁcient. References 291 Ait-Sahalia Y, Mykland P, Zhang L. How often to sample a continuous-time process in the presence of market microstructure noise. Rev Financ Stud 2005;18:351–416. Andersen T, Bollerslev T. Answering the skeptics: yes, standard volatility models do provide accurate forecasts. Int Econ

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, Frederiksen PH, Nielsen MØ. Comment on P. R. Hansen and A. Lunde: realized variance and market microstructure noise. J Bus Econ Stat 2006;24:173–179. Andersen T, Bollerslev T, Meddahi N. Realized volatility forecasting and market microstructure noise. J Econometrics 2010;160:220–234. Bandi FM, Russel JR. Realized covariation, realized beta and

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microstructure noise. Working Paper, Graduate School of Business, University of Chicago, 2005. Bandi FM, Russel JR. Separating market microstructure noise from volatility. J Financ Econ 2006;79:655

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–692. Bandi FM, Russell JR. Market microstructure noise, integrated variance estimators, and the accuracy of asymptotic approximations. J Econometrics 2011;160(1):145–159. Bandi

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timing using ‘‘realized’’ volatility. J Financ Econ 2001;67:473–509. Grifﬁn JE, Oomen RCA. Covariance measurement in the presence of non-synchronous trading and market microstructure noise. J Econometrics 2011;160(1):58–68. Hayashi T, Yoshida N. On covariance estimation of nonsynchronously observed diffusion processes. Bernoulli 2005;11(2):359

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–379. Hansen PR, Lunde A. Realized variance and market microstructure noise (with discussions). J Bus Econ Stat 2006;24:127–218. Harris FH, de B, McInish TH, Shoesmith GL, Wood RA. Cointegration, error correction, and

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. Mancino ME, Sanfelici S. Covariance estimation and dynamic asset allocation under microstructure effects via Fourier methodology. In: Gregoriou GN, Pascalau R, editors. Financial Econometrics Modeling: Market Microstructure, Factor Models and Financial Risk Measures. London, UK: Palgrave-MacMillan; 2011b:3–32. Martens M. Estimating unbiased and precise realized covariances. EFA 2004 Maastricht Meetings

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, 220 technique for producing, 110 Market index decrease, spread and, 105 Market inefﬁciencies, for small-space and mid-volume classes, 44 Market microstructure effects, 263 Market microstructure, effects on Fourier estimator, 245 Market microstructure contaminations, 273 Market microstructure model, of ultra high frequency trading, 235–242 Market model, 296–297 Market movement, indicators of, 110 Market reaction, to

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UHFT volume, 235 Ulibarri, Carlos A., xiv, 235 Ultra high frequency traders, 235 Ultra high frequency trading (UHFT). See also UHFT entries impacts of, 236 market microstructure model of, 235–242 Unbounded parabolic domain, 352 Unconditional default probability, 79, 89 Uniform convergence, 374 Unit-root stationarity tests, results of, 135 Unit-root

Alpha Trader

by Brent Donnelly · 11 May 2021

achieve these goals. Let’s go to Chapter 8, where we will go into some depth on a topic I have always found supremely fascinating: market microstructure. 99. Leung, Woon Sau and Wong, Gabriel and Wong, Woon K., “Social-Media Sentiment, Portfolio Complexity, and Stock Returns” (2019). 100. A Macro Tourist is

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partner with a market maker you trust. This concludes our discussion of liquidity and how activity varies by time of day. The last facet of market microstructure we will cover is volatility. First row of x-axis label is GMT, second row is NYC time 4. Volatility The next step in understanding

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your market’s microstructure, be on the lookout for changes and stay abreast of developments that might change how your market behaves. Simple changes to market microstructure can often lead to significant changes in your P&L and can turn profitable strategies into duds. Now, let’s move to the next step

Finding Alphas: A Quantitative Approach to Building Trading Strategies

by Igor Tulchinsky · 30 Sep 2019 · 321pp

sources of alpha signals, through characteristics related to the intraday dynamics of the traded assets. Collectively, these properties of the asset market define the market microstructure. Research in market microstructure, as its name suggests, aims to capture the structure of investors by separating distinct classes that differ in their behavior or motivation for trading

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notion of the illiquidity premium that stands for the positive relationship between expected returns and liquidity, with examples for potential alphas. Third, we present mainstream market microstructure models and their implications for asset prices. Finding Alphas: A Quantitative Approach to Building Trading Strategies, Second Edition. Edited by Igor Tulchinsky et al. and

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WorldQuant Virtual Research Center. © 2020 Tulchinsky et al., WorldQuant Virtual Research Center. Published 2020 by John Wiley & Sons, Ltd. 208 Finding Alphas DATA IN MARKET MICROSTRUCTURE Capital markets can be broadly classified as two types, based on their trading structure. In quote-driven markets, specialists – market participants, also known as market

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between spreads and return volatility leads us to question its theoretical basis – that is, how spreads are determined and, in particular, how market microstructure is involved in these patterns. MARKET MICROSTRUCTURE AND EXPECTED RETURNS Apart from allowing us to model the dynamics of liquidity, intraday data enables analysis of the interaction among market participants

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(quarterly or yearly). To avoid these disadvantages, alternative methods, such as the dynamic measure of the probability of informed trading (DPIN), have been introduced into market microstructure. According to Chang et al. (2014), DPIN succeeds at capturing a similar structure by using a much quicker method to calculate the probability of informed

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measures, indicating a higher unexplained variance for stocks with a higher probability of informed trading. In addition to highlighting cross-sectional effects in excess returns, market microstructure can contribute to alpha research through time- series analysis. As alpha quality is highly dependent on the magnitude of drawdowns, the ability to predict and

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avoid at least the largest negative shocks is essential. The predictive nature of the market microstructure Intraday Data in Alpha Research215 dynamics often can be useful for this purpose. According to Easley et al. (2011), the “flash crash” of May

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active managers. An extended period of low interest rates has allowed the proliferation of cheaply financed strategies, while the rise of passive investing has created market microstructure distortions, in part as a result of rising ownership concentration. Fortunately, new index constructions (and associated funds) also have proliferated in recent years, allowing investors

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Arbitrage and Short Sales Restrictions: Evidence from the Options Markets.” Journal of Financial Economics 74, no. 2: 305–342. Ormos, M. and Timotity, D. (2016a) “Market Microstructure During Financial Crisis: Dynamics of Informed and Heuristic-Driven Trading.” Finance Research Letters 19: 60–66. Pástor, L. and Stambaugh, R. (2003) “Liquidity Risk and

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inputs, for design 25–26 integer effect 138 intermediate variables 115 Index297 intraday data 207–216 expected returns 211–215 illiquidity premium 208–211 market microstructures 208 probability of informed trading 213–215 intraday trading 217–222 alpha design 219–221 liquidity 218–219 vs. daily trading 218–219 intrinsic risk

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macroeconomic correlations 153 manual searches, pre-automation 119 margin 28 market commentary sites 181–182 market effects index changes 225–228 see also price changes market microstructure 207–216 expected returns 211–215 illiquidity premium 208–211 probability of informed trading 213–215 types of 208 material adverse change (MAC) clause 198

Analysis of Financial Time Series

by Ruey S. Tsay · 14 Oct 2001

Appendix A. Some RATS Programs for Nonlinear Volatility Models, 168 Appendix B. S-Plus Commands for Neural Network, 169 5. High-Frequency Data Analysis and Market Microstructure 175 5.1 Nonsynchronous Trading, 176 5.2 Bid-Ask Spread, 179 5.3 Empirical Characteristics of Transactions Data, 181 5.4 Models for Price

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networks and shows various applications of nonlinear models in finance. Chapter 5 is concerned with analysis of high-frequency financial data and its application to market microstructure. It shows that nonsynchronous trading and bid-ask bounce can introduce serial correlations in a stock return. It also studies the dynamic of time duration

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of Financial Time Series. Ruey S. Tsay Copyright  2002 John Wiley & Sons, Inc. ISBN: 0-471-41544-8 CHAPTER 5 High-Frequency Data Analysis and Market Microstructure High-frequency data are observations taken at fine time intervals. In finance, they often mean observations taken daily or at a finer time scale. These

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available primarily due to advances in data acquisition and processing techniques, and they have attracted much attention because they are important in empirical study of market microstructure. The ultimate high-frequency data in finance are the transaction-by-transaction or trade-by-trade data in security markets. Here time is often measured

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some historical perspective of high-frequency financial study. High-frequency financial data are important in studying a variety of issues related to trading process and market microstructure. They can be used to compare the efficiency of different trading systems in price discovery (e.g., the open out-cry system of NYSE and

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prices of an asset do not form an equally spaced time series. The time duration between trades becomes important and might contain useful information about market microstructure (e.g., trading intensity). 2. Discrete-valued prices: The price change of an asset from one transaction to the next only occurs in multiples of

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College, Oxford University. Stoll, H., and Whaley, R. (1990), “Stock market structure and volatility,” Review of Financial Studies, 3, 37–71. Wood, R. A. (2000), “Market microstructure research databases: History and projections,” Journal of Business & Economic Statistics, 18, 140–145. Zhang, M. Y., Russell, J. R., and Tsay, R. S. (2001), “A

High-Frequency Trading

by David Easley, Marcos López de Prado and Maureen O'Hara · 28 Sep 2013

of Mathematical Sciences 43 4 High-Frequency Trading in FX Markets Anton Golub, Alexandre Dupuis, Richard B. Olsen Olsen Ltd 65 5 Machine Learning for Market Microstructure and High-Frequency Trading Michael Kearns and Yuriy Nevmyvaka University of Pennsylvania 6 A “Big Data” Study of Microstructural Volatility in Futures Markets Kesheng Wu

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W. Purcell professor of finance at the Johnson Graduate School of Management, Cornell University. Her research focuses on market microstructure, and she is the author of numerous journal articles as well as the book Market Microstructure Theory. Maureen serves on several corporate boards, and is chairman of the board of ITG, a global agency

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of market micro-structure, leveraging the methodology developed at OLSEN. Anton previously worked at the Manchester Business School as a researcher on high-frequency trading, market microstructure and flash crashes. In 2012, he was invited to participate in an international project on computerised trading funded by the ix i i i i

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manager and head of groups at SAC Capital, Bank of America and Lehman Brothers. He has also published extensively on topics in algorithmic trading and market microstructure, and is a visiting scientist in the computer and information science department at the University of Pennsylvania. Yuriy holds a PhD in computer science from

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i i “Easley” — 2013/10/8 — 11:31 — page xiii — #13 i i ABOUT THE AUTHORS supports the global execution services business, and focuses on market microstructure and electronic trading research and development. Michael joined Bank of America in 2004 as an equity derivatives quant, after spending three years at Bear Stearns

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short intervals of time, prices are not the random walks so beloved by the efficient market hypothesis, but can instead be predictable artefacts of the market microstructure. Thus, the paradox: billions are invested in HFT research and infrastructure, topics that LF traders do not even recognise as an issue. Given their dissimilar

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to a low latency, multi-venue marketplace, where large block trading has become rare and liquidity is always at a near critical point. This new market microstructure is the result of high-frequency algorithmic trading, defined as the execution of orders via a computerised, rules-based trading system. From a tool of

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. Then we review the trading algorithms that are used in the industry and, based on Schmidt (2011) and Masry (2013), we assess their impact on market microstructure. Market venues The currency market is a complex system of organised exchanges. At the centre of the market there are two inter-dealer electronic broking

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TRADING the market quality. The agent-based model has shown to be able to reproduce several empirical features of the high-frequency dynamics of the market microstructure: negative autocorrelation in returns, clustering of trading activity (volatility, traded volume and bid–ask spread), non-linear response of the price change to the traded

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90 — #110 i i i i i i i i “Easley” — 2013/10/8 — 11:31 — page 91 — #111 i i 5 Machine Learning for Market Microstructure and High-Frequency Trading Michael Kearns and Yuriy Nevmyvaka University of Pennsylvania In this chapter, we give an overview of the uses of machine learning

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for high-frequency trading (HFT) and market microstructure data and problems. Machine learning is a vibrant subfield of computer science that draws on models and methods from statistics, algorithms, computational complexity, artificial intelligence

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machine learning to the 92 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 93 — #113 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING problem of predicting directional price movements, again from limit order data for equities. Using similar but additional state features as in

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the reinforcement learning investigation, we seek models that can predict relatively near-term price movements (as measured by the bid–ask midpoint) from market microstructure signals. Again, the primary challenge is in the engineering or development of these signals. We show that such prediction is indeed modestly possible, but it

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strategies (momentum or reversion, 94 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 95 — #115 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING directional or liquidity provision, etc). However, most of the more technical treatments of HFT seem to agree that the data driving

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microstructure data and order 96 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 97 — #117 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING book reconstruction, if we are in a state where v is small and t is large (thus we have bought most

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all T steps. At 98 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 99 — #119 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING the end of the trading period, if there is any remaining volume v, a market order for the remaining shares is

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our buy order for 100 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 101 — #121 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING Figure 5.2 Sample policies learned by RL Optimal action Optimal action (a) 15 10 5 0 1 876 432 5

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combined with a strongly 102 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 103 — #123 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING Table 5.1 Reduction in implementation shortfall obtained by adding features to (v, t) Feature(s) added Bid–ask spread Bid

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, as measured by implementation 104 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 105 — #125 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING shortfall), and showed that machine learning methodology could provide important tools for such efforts. It is of course natural to ask

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state space, the cumulative 106 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 107 — #127 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING Correlation with action (+1 = buy,–1 = sell) Figure 5.4 Correlations between feature values and learned policies 0.20 0.10

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. (c) Smartprice versus all. 108 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 109 — #129 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING Figure 5.5 Continued. 3500 (d) 3000 2500 2000 1500 1000 500 0 0 5 10 15 20 5 10 15

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, a sign of mild 110 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 111 — #131 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING Figure 5.6 Learned policies depend strongly on timescale (b) Action learned (×10–6) Action learned (×10–8) (a) 3 2

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dynamic state-dependent strategy. 112 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 113 — #133 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING The discussion so far highlights the two aspects that must be considered when applying machine learning to high-frequency data: the

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that price changes are larger than spreads, giving us higher margins. However, as we have seen, the longer the holding period, the less directly informative market microstructure aspects seem to become, making prediction more difficult. Second, we could trade with limit orders, hoping to avoid paying the spread. This is definitely a

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learning approach does not 114 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 115 — #135 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING offer any easy paths to profitability. Markets are competitive, and finding sources of true profitability is extremely difficult. That being said

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) are no longer available. 116 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 117 — #137 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING (ii) Upon submitting an order to a dark pool, all we learn is whether our order has been (partially) executed, not

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our “next” share to 118 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 119 — #139 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING whichever pool has the highest marginal probability of executing that share, conditioned on the allocation made so far. In this manner

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no accounting for the 120 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 121 — #141 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING 0.110 0.105 0.100 0.095 0.090 0.085 0.080 0.075 0.070 0.065 0

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case our learning algorithm outperforms this simple heuristic. CONCLUSION We have presented both the opportunities and challenges of a machine learning approach to HFT and market microstructure and considered problems of both pure execution, over both time and venues, and predicting directional movements in search of profitability. These were illustrated via three

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to apply machine learning 122 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 123 — #143 i i MACHINE LEARNING FOR MARKET MICROSTRUCTURE AND HIGH-FREQUENCY TRADING to a challenging, changing domain). But, applied tastefully and with care, the approach can be powerful and scalable, and is arguably

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A “BIG DATA” STUDY OF MICROSTRUCTURAL VOLATILITY IN FUTURES MARKETS (Easley et al 1996; Lee and Ready 1991). The most popular method used in the market microstructure literature is the tick rule (Lee and Ready 1991). This method relies on the sequential order of trades. However, due to high-frequency trading, there

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and L. Zhang, 2005, “How Often to Sample a ContinuousTime Process in the Presence of Market Microstructure Noise”, Review of Financial Studies 18(2), pp. 351–416. Amihud, Y., H. Mendelson and M. Murgia, 1990, “Stock Market Microstructure and Return Volatility: Evidence from Italy”, Journal of Banking and Finance 14(2), pp. 423–40

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, hence the advent of high-frequency trading. Easley et al (2011a) have argued that these changes are related to a number of new trends in market microstructure. One area where this competition is particularly intense is in liquidity provision. In this new era of high-frequency trading, liquidity is provided by computers

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–100. Hasbrouck, J., 1995, “One Security, Many Markets: Determining the Contributions to Price Discovery”, Journal of Finance 50, pp. 1175–99. Hasbrouck, J., 2007, Empirical Market Microstructure. New York: Oxford University Press. Hendershott, T., and A. J. Menkveld, 2011, “Price Pressures”, Manuscript, VU University Amsterdam. Kraus, A., and H. Stoll, 1972, “Price

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, monitoring, 16 and seasonal effects, avoiding, 16 and smart brokers, 16 see also high-frequency trading M machine learning: for high-frequency trading (HFT) and market microstructure, 91–123, 100, 101, 103, 104, 107, 108–9, 111, 117, 121 and high-frequency data, 94–6 and optimised execution in dark pools via

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reinforcement learning for optimised trade execution, 96–104 and smart order routing in dark pools, 115–22 Market Information Data Analytics System (MIDAS), 215–16 market microstructure: machine learning for, 91–123, 100, 101, 103, 104, 107, 108–9, 111, 117, 121 and high-frequency data, 94–6 and optimised execution in

Advances in Financial Machine Learning

by Marcos Lopez de Prado · 2 Feb 2018 · 571pp · 105,054 words

to the production chain. The values could be tabulated or hierarchical, aligned or misaligned, historical or real-time feeds, etc. Team members are experts in market microstructure and data protocols such as FIX. They must develop the data handlers needed to understand the context in which that data arises. For example, was

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one of the most multi-disciplinary areas of research, and this book reflects that fact. Understanding the various sections requires a practical knowledge of ML, market microstructure, portfolio management, mathematical finance, statistics, econometrics, linear algebra, convex optimization, discrete math, signal processing, information theory, object-oriented programming, parallel processing, and supercomputing. Python has

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to an IID Gaussian distribution) than sampling by tick bars. Another reason to prefer volume bars over time bars or tick bars is that several market microstructure theories study the interaction between prices and volume. Sampling as a function of one of these variables is a convenient artifact for these analyses, as

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to sample more frequently when new information arrives to the market. In this context, the word “information” is used in a market microstructural sense. As we will see in Chapter 19, market microstructure theories confer special importance to the persistence of imbalanced signed volumes, as that phenomenon is associated with the presence of informed

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price series Variable St could be based on any of the features we will discuss in Chapters 17–19, like structural break statistics, entropy, or market microstructure measurements. For example, we could declare an event whenever SADF departs sufficiently from a previous reset level (to be defined in Chapter 17). Once we

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decision. As we saw in Chapter 11, carrying out a flawless WF simulation is a daunting task that requires extreme knowledge of the data sources, market microstructure, risk management, performance measurement standards (e.g., GIPS), multiple testing methods, experimental mathematics, etc. Unfortunately, there is no generic recipe to conduct a backtest. To

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between this notion of concentration and entropy is due to the generalized mean, which we discussed in Chapter 18, Section 18.7. 18.8.4 Market Microstructure Easley et al. [1996, 1997] showed that, when the odds of good news / bad news are even, the probability of informed trading (PIN) can be

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could have worked with a vector of holdings, should the covariance matrix had been computed on price changes. CHAPTER 19 Microstructural Features 19.1 Motivation Market microstructure studies “the process and outcomes of exchanging assets under explicit trading rules” (O'Hara [1995]). Microstructural datasets include primary information about the auctioning process, like

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makes microstructural data one of the most important ingredients for building predictive ML features. 19.2 Review of the Literature The depth and complexity of market microstructure theories has evolved over time, as a function of the amount and variety of the data available. The first generation of models used solely price

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-century financial markets. 19.6 Additional Features from Microstructural Datasets The features we have studied in Sections 19.3 to 19.5 were suggested by market microstructure theory. In addition, we should consider alternative features that, although not suggested by the theory, we suspect carry important information about the way market participants

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conclude that on timescales of less than a few hours, the persistence of order flow is overwhelmingly due to splitting rather than herding. Given that market microstructure theory attributes the persistency of order flow imbalance to the presence of informed traders, it makes sense to measure the strength of such persistency through

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19.5. 19.7 What Is Microstructural Information? Let me conclude this chapter by addressing what I consider to be a major flaw in the market microstructure literature. Most articles and books on this subject study asymmetric information, and how strategic agents utilize it to profit from market makers. But how is

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. Focardi, and C. Jonas (2011): “High-frequency trading. Methodologies and market impact.” Review of Futures Markets, Vol. 19, pp. 7–38. Hasbrouck, J. (2007): Empirical Market Microstructure, 1st ed. Oxford University Press. Hasbrouck, J. (2009): “Trading costs and returns for US equities: Estimating effective costs from daily data.” Journal of Finance, Vol

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. 259–283. NANEX (2011): “Strange days: June 8, 2011—NatGas Algo.” NANEX blog. Available at www.nanex.net/StrangeDays/06082011.html. O'Hara, M. (1995): Market Microstructure, 1st ed. Blackwell, Oxford. O'Hara, M. (2011): “What is a quote?” Journal of Trading, Vol. 5, No. 2 (Spring), pp. 10–15. Parkinson, M

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entropy to backtesting in cross-validation in econometrics and ensemble methods in entropy features in feature importance in hyper-parameter tuning with cross-validation in market microstructure theories and three sources of errors in Monte Carlo simulations machine learning asset allocation and sequential bootstraps evaluation using Multi-product series ETF trick for

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems

by Irene Aldridge · 1 Dec 2009 · 354pp · 26,550 words

vii Bid-Ask Bounce 120 Modeling Arrivals of Tick Data 121 Applying Traditional Econometric Techniques to Tick Data 123 Conclusion 125 CHAPTER 10 Trading on Market Microstructure: Inventory Models 127 Overview of Inventory Trading Strategies 129 Orders, Traders, and Liquidity 130 Proﬁtable Market Making 134 Directional Liquidity Provision 139 Conclusion 143 CHAPTER

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11 Trading on Market Microstructure: Information Models 145 Measures of Asymmetric Information 146 Information-Based Trading Models 149 Conclusion 164 CHAPTER 12 Event Arbitrage 165 Developing Event Arbitrage Trading Strategies

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of High-Frequency Strategies Typical Holding Period Strategy Description Automated liquidity provision Quantitative algorithms for optimal pricing and execution of market-making positions <1 minute Market microstructure trading Identifying trading party order ﬂow through reverse engineering of observed quotes <10 minutes Event trading Short-term trading on macro events <1 hour Deviations

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into hedge fund structures once they are successful. Currently, four classes of trading strategies are most popular in the high-frequency category: automated liquidity provision, market microstructure trading, event trading, and deviations arbitrage. Table 1.1 summarizes key properties of each type. Developing high-frequency trading presents a set of challenges previously

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the highest frequency, with position-holding periods of one minute or less. Chapter 11 looks into a class of high-frequency strategies known as the market microstructure models, with typical holding periods seldom exceeding 10 minutes. Chapter 12 details strategies capturing abnormal returns around ad hoc events such as announcements of economic

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hourly intervals. In a way, technical analysis was a precursor of modern microstructure theory. Even though market microstructure applies at a much higher frequency and with a much higher degree of sophistication than technical analysis, both market microstructure and technical analysis work to infer market supply and demand from past price movements. Much of

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commodities trading analyzes and matches available supply and demand. Various facets of the fundamental analysis are active inputs into many high-frequency trading models, alongside market microstructure. For example, event arbitrage consists of trading the momentum response accompanying the price adjustment of the security in response to new fundamental information. The date

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necessary for back tests and transition into production Computing “horsepower” Tick data – Most informative Market depth – Necessary for the highestfrequency strategies, liquidity provision – Desirable for market microstructure strategies Real-time streaming data – Broker-dealer data – Reference data (e.g., Reuters) FIGURE 3.4 The process for development of econometric models for highfrequency

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data differs dramatically from low-frequency data. Utilization of tick data creates a host of opportunities not available at lower frequencies. CHAPTER 10 Trading on Market Microstructure Inventory Models ational expectations and the efficient markets hypotheses imply that, following a relevant news release, market prices adjust instantaneously. From the perspective of a

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also shapes beliefs of market participants through a form of collective bargaining process. The discipline that studies the price formation process is known as market microstructure. Trading on market microstructure is the holy grail R 127 128 HIGH-FREQUENCY TRADING Time (GMT) 7:00:01 6:00:01 5:00:01 4:00:01

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unemployment news, recorded on July 8, 2009 at hourly (top panel) and tick-by-tick (bottom panel) frequencies. of high-frequency trading. The idea of market microstructure trading is to extract information from the observable quote data and trade upon that extracted information in order to obtain gains. Holding periods for positions

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in market microstructure trading can vary in duration from seconds to hours. The optimal holding period is influenced by the transaction costs faced by the trader. A gross

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several basis points (1 basis point = 1 bp = 1 pip = 0.01%), at most. To make such trading viable, the expected gain has Trading on Market Microstructure 129 to surpass the transaction costs. In an institutional setting (e.g., on a proprietary trading desk of a broker-dealer), a trader will often

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range anywhere from 3 bps to 30 bps per trade, mandating strategies that call for longer holding periods. According to Lyons (2001), the field of market microstructure encompasses two general types of models—inventory models and information models. Information models are concerned with the process of impounding information into prices in response

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providing liquidity. Examples of such models include Rock (1996), Glosten (1994), and Seppi (1997). The longer the waiting time until order execution, the Trading on Market Microstructure 131 higher was the expected compensation to liquidity providers who did not change their limit order specifications once they submitted the orders. The assumptions behind

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orders. Cao, Hansch, and Wang (2004) find cointegration of different orders in the limit order book, supporting the existence of value traders. Trader Types in Market Microstructure Trading Harris (1998) identifies three types of traders: 1. Informed traders, who possess material information about an impend- ing market move 2. Liquidity traders (also

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security. Private information can include analyses from paid-for news sources, like Bloomberg, not yet available to the general public, and superior forecasts based on market microstructure. Informed traders are often high-frequency money managers and other proprietary traders with superior access to information and skill in assessing immediate market situations. The

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a market for a single security, so that no substitute securities can be traded in place of an overpriced or illiquid security. 133 Trading on Market Microstructure Order aggressiveness Liquidity traders (Uninformed Value traders traders) Low security price (bids) Informed traders Market price Liquidity traders (Uninformed traders) Value traders High security price

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limit order to sell just above market in the high-priced market, and then reversing the positions once transaction costs were overcome. 135 Trading on Market Microstructure Garman (1976) was the first to investigate the optimal market-making conditions through modeling temporary imbalances between buy and sell orders. These imbalances are due

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—say a dollar—happens when a buyer of the security arrives. As before, the arrival of a buyer willing to buy at 137 Trading on Market Microstructure price pa happens with probability λa . As a result, the market maker’s probability of gaining a dollar is pa . Similarly, the market maker’s

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b are determined from a partial differential equation with the security price, s, trader’s inventory, q, and time, t, as inputs. 139 Trading on Market Microstructure The optimal limit bid price, b, and limit ask price, a, are then determined as follows: 1 λb b = r − ln 1 − γ γ λb

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100 150 FIGURE 10.3 Comparison of performance of inventory, best bid/best ask, and symmetric strategies per Avellaneda and Stoikov (2008). 141 Trading on Market Microstructure 180 Inventory strategy Symmetric strategy 160 140 120 100 80 60 40 20 0 –50 50 0 100 FIGURE 10.3 (Continued) Panel a): market

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markets. In addition, Kavajecz and Odders-White (2004) find that indicators based on moving averages help identify the skewness of the order book. Trading on Market Microstructure 143 When a short-run moving average rises above a long-run moving average, the buy-side liquidity pool in the limit order book moves

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activity creates value to investors who wish to reallocate their portfolios in response to changes in their personal valuations of assets. CHAPTER 11 Trading on Market Microstructure Information Models nventory models, discussed in Chapter 10, propose ways in which a market maker can set limit order prices based on characteristics of the

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-ask spread is computed as twice the difference between the latest trade price and the midpoint between the quoted bid and ask 147 Trading on Market Microstructure prices, divided by the midpoint between the quoted bid and ask prices. The effective spread, therefore, produces a measure that is virtually identical to the

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) + α(1 − δ) exp(−(ω + µ)T) exp(−ωT) B! S! (ωT) B ((ω + µ)T) S + αδ exp(−ωT) exp(−(ω + µ)T) B! S! Trading on Market Microstructure 149 INFORMATION-BASED TRADING MODELS Trading on Information Contained in Bid-Ask Spreads Liquidity-providing market participants (or market makers) use bid-ask spreads as

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Bid t * Time Sell FIGURE 11.2 Inventory costs. Askt Mid t Bid t * Time Sell FIGURE 11.3 Asymmetric information (adverse selection). Trading on Market Microstructure 151 order. If bid-ask spreads were to compensate the dealer for the risks associated with holding excess inventory, then any changes in prices would

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thus gone from a half to two-thirds. After sunrise the next day, the child adds another white marble, and the probability 153 Trading on Market Microstructure (and thus the degree of belief) goes from two-thirds to three-quarters. And so on. Gradually, the initial belief that the sun is just

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obtain the following probability of the buy order being an indication of the buy order resulting from higher true value V ask : 155 Trading on Market Microstructure Pr(buy order|Vask = 0.6738) = 50 percent∗ 100 percent +50 percent∗ 50 percent (11.14) = 75 percent. The probability of the buy order resulting

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counterparties are better informed than the dealer himself. As a result, dealers that can differentiate between informed and uninformed customers charge higher spreads Trading on Market Microstructure 157 on trades with informed customers and lower spreads on trades with uninformed customers. Mende, Menkhoff, and Osler (2006) note that in foreign exchange markets

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-term profits. Anand, Chakravarty, and Martell (2005) find that on the NYSE, institutional limit orders perform better than limit orders placed by 159 Trading on Market Microstructure individuals, orders at or better than market price perform better than limit orders placed inside the bid-ask spread, and larger orders outperform smaller orders

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any direct order flow at all, but can partially infer the order flow information from market data provided by their brokers using a Trading on Market Microstructure 161 complex and costly mechanism. Because the order flow is not available to everyone, those who possess full order flow information are in a unique

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.00339*** 0.00322*** 0.00701*** 0.00204 0.00342** *** , ** and * denote 99.9 percent, 95 percent, and 90 percent statistical signiﬁcance, respectively. 163 Trading on Market Microstructure Order Flow Is Not Directly Observable Order flow is not necessarily transparent to all market participants. For example, executing brokers can directly observe buy-and

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Specifications, Security Characteristics, and the Cross-Section of Expected Stock Returns.” Journal of Financial Economics 49, 345–373. Brennan, M.J. and A. Subrahmanyam, 1996. “Market Microstructure and Asset Pricing: On the Compensation for Illiquidity in Stock Returns.” Journal of Financial Economics 41, 441–464. Brock, W.A., J. Lakonishok and B

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T. Wang, 2007. “Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach.” The Review of Financial Studies 20, 41–81. Garman, Mark, 1976. “Market Microstructure.” Journal of Financial Economics 3, 257–275. Garman, M.B. and M.J. Klass, 1980. “On the Estimation of Security Price Volatilities from Historical Data

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Finance 46, 179–207. Hasbrouck, J., 2005. “Trading Costs and Returns for US Equities: The Evidence from Daily Data.” Working paper. Hasbrouck, J., 2007. Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press. Hasbrouck, J. and G. Saar, 2002. “Limit Orders and Volatility in a Hybrid Market

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. Odders-White, H.R. and K.J. Ready, 2006. “Credit Ratings and Stock Liquidity.” Review of Financial Studies 19, 119–157. O’Hara, Maureen, 1995. Market Microstructure Theory. Blackwell Publishing, Malden, MA. Orphanides, Athanasios, 1992. “When Good News Is Bad News: Macroeconomic News and the Stock Market.” Board of Governors of the

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Bid-ask bounce, tick data and, 120–121 Bid-ask spread: interest rate futures, 40–41 inventory trading, 133, 134–139 limit orders, 67–68 market microstructure trading, information models, 146–147, 149–157 post-trade analysis of, 288 tick data and, 118–120 Bigan, I., 183 Bisiere, Christophe, 12 INDEX BIS

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news, event arbitrage, 174 Inefficiency. See Market efficiency Information-gathering software, 25 Information leakage, 79 Information spillovers, large-to-small, 196–197 Information trading. See Market microstructure trading, information models Informed traders, inventory trading and, 132 “In Praise of Bayes” (The Economist), 152–153 In-sample back-test, 219 Institutional clients, 10

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markets, 40–41 International Securities Exchange (ISE), 9 Intra-day data, 4 Intra-day position management, 21–22 Intra-trading benchmarks, 297 Inventory trading. See Market microstructure trading, inventory models Investment delay costs, 288–289 Investors, as market participants, 24 Island, 12 Jagannathan, Ravi, 180 Jain, P., 163 Jang, Hasung, 68 Jennings

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, 133 Market efficiency: predictability and, 78–79 profit opportunities and, 75–78 testing for, 79–89 MarketFactory, 25 Market impact costs, 290–293 Market microstructure trading, 4, 127–128 Market microstructure trading, information models, 129, 145–164 asymmetric information measures, 146–148 INDEX bid-ask spreads, 149–157 order aggressiveness, 157–160 order flow

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, 160–163 Market microstructure trading, inventory models, 127–143 liquidity provision, 133–134, 139–143 order types, 130–131 overview, 129–130 price adjustments, 127–128 profitable market making

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Trading platform, 31 Trading software, 25 Trading strategy accuracy (TSA) back-testing method, 222–231 Trailing stop, 267 Transaction costs: information-based trading, 149–151 market microstructure trading, inventory models, 128–129 market versus limit orders, 62–63 portfolio optimization, 206–208 post-trade analysis of, 283–295 Transparent execution costs, 34