DOCSLIB.ORG

Order Book Approach to Price Impact

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Order Book Approach to Price Impact Quantitative Finance, Vol. 5, No. 4, August 2005, 357–364 Order book approach to price impact P. WEBER and B. ROSENOW* Institut fu¨ r Theoretische Physik, Universita¨ t zu Ko¨ ln, D-50923 Germany (Received 16 January 2004; in final form 23 June 2005) Buying and selling stocks causes price changes, which are described by the price impact function. To explain the shape of this function, we study the Island ECN orderbook. In addition to transaction data, the orderbook contains information about potential supply and demand for a stock. The virtual price impact calculated from this information is four times stronger than the actual one and explains it only partially. However, we find a strong anticorrelation between price changes and order flow, which strongly reduces the virtual price impact and provides for an explanation of the empirical price impact function. Keywords: Order book; Price impact; Resiliency; Liquidity In a perfectly efficient market, stock prices change due to to be a concave function of volume imbalance, which the arrival of new information about the underlying com- increases sublinearly for above average volume imbal- pany. From a mechanistic point of view, stock prices ance. However, there is evidence that this concave shape change if there is an imbalance between buy and sell cannot be a property of the true price impact a trader in an orders for a stock. These ideas can be linked by assuming actual market would observe. Firstly, such a concave that someone who trades a large number of stocks might price impact would be an incentive to do large trades have private information about the underlying company, in one step instead of breaking them up into many smaller and that an imbalance between supply and demand trans- ones as is done in practice. Secondly, in the presence mits this information to the market. In this sense, volume of bluffers in the market, the enforcement of a strictly imbalance and stock price changes should be connected linear price impact is the only way in which a market causally, i.e. prices go up if demand exceeds supply and maker or liquidity trader can protect herself against go down if supply exceeds demand. The analysis of huge suffering losses. financial data sets (Takayasu 2002) allows a detailed One possible way to achieve a better understanding of study of the price impact function (Hasbrouck 1991, the average price impact is the analysis of information Hausman et al. 1992, Kempf and Korn 1999, Evans about potential supply and demand stored in the limit and Lyons 2002, Hopman 2002, Plerou et al. 2002, order book. Using this information, one can calculate a Rosenow 2002, Bouchaud et al. 2003, Gabaix et al. virtual or instantaneous price impact, which would be 2003, Lillo et al. 2003, Potters and Bouchaud 2003), caused by a market order matched with limit orders which quantifies the relation between volume imbalance from the order book. This virtual price impact can be and price changes. used as a reference point for an understanding of the In a series of previous studies (Hasbrouck 1991, average price impact of market orders. An analysis of Hausman et al. 1992, Kempf and Korn 1999, Evans the limit order book of the Stockholm Stock Exchange and Lyons 2002, Plerou et al. 2002, Rosenow 2002, (Sandas 2001) suggests that the virtual price impact cal- Bouchaud et al. 2003, Gabaix et al. 2003, Lillo et al. culated from the order book is significantly larger than 2003, Potters and Bouchaud 2003), the average price what is expected from a regression model. Similarly, impact of an imbalance between buy and sell market a difference between hypothetical and actual price impact orders or of individual market orders was studied. was found in Coppejans et al. (2001) and considered as Generally, this average price impact function was found evidence for discretionary trading, e.g. large trades are more likely to be executed when the order book has sufficient depth. In this paper, we calculate both the average price *Corresponding author. Email: [email protected] impact of market orders and the virtual price impact Quantitative Finance ISSN 1469–7688 print/ISSN 1469–7696 online # 2005 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/14697680500244411 358 P. Weber and B. Rosenow Table 1. Summary statistics for the ten stocks studied. All values are calculated for five minute intervals. Numbers in brackets are in units of the respective standard deviation. AMAT BRCD BRCM CSCO INTC KLAC MSFT ORCL QLGC SEBL Returns G 0.004 0.0053 0.0049 0.0034 0.0034 0.0036 0.0025 0.0039 0.0041 0.005 Gmax 0.026 0.057 0.039 0.037 0.023 0.026 0.021 0.049 0.033 0.066 j j (6.46) (10.77) (8.1) (11.05) (6.86) (7.17) (8.3) (12.74) (8.02) (13.23) Median of |G| 0.0022 0.0028 0.0025 0.0018 0.0018 0.002 0.0013 0.002 0.0022 0.0026 (0.55) (0.53) (0.52) (0.52) (0.55) (0.55) (0.53) (0.51) (0.55) (0.52) Volume imbalance Q 9551.78 9700.37 6196.3 22652.52 16660.35 5862.25 11029.98 16010.11 6513.98 7152.31 Qmax 344657 193633 127274 424923 323016 116790 376168 396978 82540 189966 j j (36.08) (19.96) (20.54) (18.76) (19.39) (19.92) (34.1) (24.8) (12.67) (26.56) Median of |Q| 5958 6069 4086.5 14902.5 11208 3648 7307.5 10100 4078.5 4429 (0.62) (0.63) (0.66) (0.66) (0.67) (0.62) (0.66) (0.63) (0.63) (0.62) Market orders N 52.38 45.31 44.97 58.34 72.62 58.89 83.59 47.41 56.82 35.02 h marketi Nmarket 33.89 36.4 36.83 39.3 49.14 39.92 53.31 37.43 37.72 30.92 Limit orders N 321.27 247.06 233.54 302.6 391.91 344.59 429.8 227.17 303.68 199.99 h addedi Nadded 183.49 156.22 160.86 213.87 232.65 169.97 244.83 165 162.02 156.01 N 286.91 220.13 206.39 256.38 343.71 305.64 376.4 191.54 268.41 177.09 h cancelledi Ncancelled 173.15 144.14 144.22 199.23 220.71 154.95 229.11 152.41 146.79 141.82 from information contained in the Island ECN order and cancellation of limit orders. If a trader is willing to book. By comparing the two, we find that the virtual sell a certain volume (number of shares) of a stock at price impact is more than four times stronger than the a given or higher price, she places a limit sell order. actual one. In order to explain this surprising discrepancy, For buying at a given or lower price, a limit buy order we study time-dependent correlations between order flow is placed. Limit orders are stored in the order book at and returns and find strong evidence for resiliency: the their respective price with the respective volume. The market recovers from random uninformative price shocks sell limit order with the lowest price defines the ask as the flow of limit orders is anticorrelated with returns price Sask for that stock. Similarly, the buy limit order in contrast to the positive correlations between returns with the highest price defines the bid price Sbid. If a buy and market orders. Thus, limit orders placed in response limit order with a price higher than the current ask price is to returns strongly reduce the virtual price impact placed, it is executed immediately against the sell limit and provide for a link between virtual and actual price orders at the ask price, vice versa for sell limit orders at impact. or below the bid price. Such marketable limit orders We include the possible influence of discretionary will be called market orders in the following. Market trading in our analysis by studying the average virtual orders are placed by impatient traders who want to trade price impact, the typical virtual price impact and the vir- immediately. tual price impact calculated from the average order book. The time period we study is the entire year 2002. Each The average virtual price impact is strongly influenced day, we disregard the first five minutes due to the build by low liquidity periods and hence not representative up of the order book. We checked that our results are for the actual impact of market orders. In order to include robust with respect to this choice by removing e.g. the the influence of discretionary trading in a semiquantita- first fifteen minutes and the last five minutes per day. tive way, we base our analysis on the virtual price impact We consider 250 trading days and divide each trading as calculated from the average order book, which is day into 77 intervals of five minute length. weaker than the average and the typical price impact. We chose the 10 most frequently traded stocks for Including the effect of resilience as apparent from the the year 2002, ticker symbols and descriptive statistics above-mentioned anticorrelations, the actual price can be found in table 1. The volume of market buy orders impact can be explained quantitatively. is counted as positive and the volume of market sell We analysed limit order data from the Island ECN, orders as negative, and the volume imbalance Q(t) is NASDAQ’s largest electronic communication network, defined as the sum of all signed market orders placed which comprises about 20% of all trades.
Load more

Recommended publications
  • Effects of the Limit Order Book on Price Dynamics
    Effects of the Limit Order Book on Price Dynamics Tolga Cenesizoglu Georges Dionne Xiaozhou Zhou November 2014 CIRRELT-2014-60 Effects of the Limit Order Book on Price Dynamics Tolga Cenesizoglu1, Georges Dionne1,2,*, Xiaozhou Zhou1,2 1 Department of Finance, HEC Montréal, 3000 Côte-Sainte-Catherine, Montréal, Canada H3T 2A7 2 Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) Abstract. In this paper, we analyze whether the state of the limit order book affects future price movements in line with what recent theoretical models predict. We do this in a linear vector autoregressive system which includes midquote return, trade direction and variables that are theoretically motivated and capture different dimensions of the information embedded in the limit order book. We find that different measures of depth and slope of bid and ask sides as well as their ratios cause returns to change in the next transaction period in line with the predictions of Goettler, Parlour, and Rajan (2009) and Kalay and Wohl (2009). Limit order book variables also have significant long term cumulative effects on midquote return, which is stronger and takes longer to be fully realized for variables based on higher levels of the book. In a simple high frequency trading exercise, we show that it is possible in some cases to obtain economic gains from the statistical relation between limit order book variables and midquote return. Keywords. High frequency limit order book, high frequency trading, high frequency transaction price, asset price, midquote return, high frequency return. Results and views expressed in this publication are the sole responsibility of the authors and do not necessarily reflect those of CIRRELT.
    [Show full text]
  • Portfolio Management Under Transaction Costs
    Portfolio management under transaction costs: Model development and Swedish evidence Umeå School of Business Umeå University SE-901 87 Umeå Sweden Studies in Business Administration, Series B, No. 56. ISSN 0346-8291 ISBN 91-7305-986-2 Print & Media, Umeå University © 2005 Rickard Olsson All rights reserved. Except for the quotation of short passages for the purposes of criticism and review, no part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior consent of the author. Portfolio management under transaction costs: Model development and Swedish evidence Rickard Olsson Master of Science Umeå Studies in Business Administration No. 56 Umeå School of Business Umeå University Abstract Portfolio performance evaluations indicate that managed stock portfolios on average underperform relevant benchmarks. Transaction costs arise inevitably when stocks are bought and sold, but the majority of the research on portfolio management does not consider such costs, let alone transaction costs including price impact costs. The conjecture of the thesis is that transaction cost control improves portfolio performance. The research questions addressed are: Do transaction costs matter in portfolio management? and Could transaction cost control improve portfolio performance? The questions are studied within the context of mean-variance (MV) and index fund management. The treatment of transaction costs includes price impact costs and is throughout based on the premises that the trading is uninformed, immediate, and conducted in an open electronic limit order book system. These premises characterize a considerable amount of all trading in stocks.
    [Show full text]
  • The Order Book As a Queueing System: Average Depth and Influence of the Size of Limit Orders Ioane Muni Toke
    The order book as a queueing system: average depth and influence of the size of limit orders Ioane Muni Toke To cite this version: Ioane Muni Toke. The order book as a queueing system: average depth and influence of the size of limit orders. Quantitative Finance, Taylor & Francis (Routledge), 2015, 15 (5), pp.795-808. 10.1080/14697688.2014.963654. hal-01006410 HAL Id: hal-01006410 https://hal.archives-ouvertes.fr/hal-01006410 Submitted on 16 Jun 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. The order book as a queueing system: average depth and influence of the size of limit orders Ioane Muni Toke ERIM, University of New Caledonia BP R4 98851 Noumea CEDEX, New Caledonia [email protected] Applied Maths Laboratory, Chair of Quantitative Finance Ecole Centrale Paris Grande Voie des Vignes, 92290 Chˆatenay-Malabry, France [email protected] Abstract In this paper, we study the analytical properties of a one-side order book model in which the flows of limit and market orders are Poisson processes and the distribution of lifetimes of cancelled orders is exponen- tial.
    [Show full text]
  • Informed Traders and Limit Order Markets$
    ARTICLE IN PRESS Journal of Financial Economics 93 (2009) 67–87 Contents lists available at ScienceDirect Journal of Financial Economics journal homepage: www.elsevier.com/locate/jfec Informed traders and limit order markets$ Ronald L. Goettler a,Ã, Christine A. Parlour b, Uday Rajan c a Booth School of Business, University of Chicago, USA b Haas School of Business, University of California, Berkeley, USA c Ross School of Business, University of Michigan, Ann Arbor, USA article info abstract Article history: We consider a dynamic limit order market in which traders optimally choose whether to Received 1 January 2008 acquire information about the asset and the type of order to submit. We numerically Received in revised form solve for the equilibrium and demonstrate that the market is a ‘‘volatility multiplier’’: 17 June 2008 prices are more volatile than the fundamental value of the asset. This effect increases Accepted 6 August 2008 when the fundamental value has high volatility and with asymmetric information Available online 5 April 2009 across traders. Changes in the microstructure noise are negatively correlated with JEL classification: changes in the estimated fundamental value, implying that asset betas estimated from G14 high-frequency data will be incorrect. C63 & 2009 Published by Elsevier B.V. C73 D82 Keywords: Limit order market Informed traders Endogenous information acquisition Computational game 1. Introduction Understanding dynamic order choice under asym- metric information is important because rational agents Many financial markets around the world, including use different trading strategies for different assets. The the Paris, Stockholm, Shanghai, Tokyo, and Toronto stock characteristics of an asset (such as the volatility of exchanges, are organized as limit order books.
    [Show full text]
  • Single Stock Dividend Futures
    EURONEXT DERIVATIVES SINGLE STOCK DIVIDEND FUTURES What are Single Stock Why trade Single Stock Who are Single Stock Dividend Futures? Dividend futures? Dividend Futures for? Single Stock Dividend Futures are Access new trading opportunities Investors who want to hedge risk exchange-traded derivatives contracts by taking long or short positions associated with dividend exposure, that allow investors to take positions on Single Stock Dividend Futures, diversify their trading portfolio and on dividend payments. or trade them against the dividend reduce its volatility exposure. index futures (CAC 40® or AEX Index®). Market Makers Euronext’s new Single Stock Dividend Futures complement its existing dividend index future offering (CAC 40 Dividend BNP Paribas Index Future and AEX Dividend Index Future), giving market Yanis Escudero participants additional investment strategies. [email protected] +44 (0) 207 595 1691 Dividends are a key component for equity and equity derivatives holders and are mostly used as a hedging tool. However, dividends are also becoming an asset Gabriel Messika class of their own: a dividend investment can be seen as corresponding to an [email protected] equity investment, while often proving more resilient and less volatile than stocks. +44 (0) 207 595 1819 Why trade Single Stock Dividend Futures? Nicolas Certner Benefit from an efficient hedging tool helping you to manage your [email protected] dividend exposure +44 (0) 207 595 595 1342 Diversify your portfolio by investing
    [Show full text]
  • Dark Pools and High Frequency Trading for Dummies
    Dark Pools & High Frequency Trading by Jay Vaananen Dark Pools & High Frequency Trading For Dummies® Published by: John Wiley & Sons, Ltd., The Atrium, Southern Gate, Chichester, www.wiley.com This edition first published 2015 © 2015 John Wiley & Sons, Ltd, Chichester, West Sussex. Registered office John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or trans- mitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor men- tioned in this book.
    [Show full text]
  • Order-Book Modelling and Market Making Strategies
    Order-book modelling and market making strategies Xiaofei Lu∗1 and Fr´ed´ericAbergely1 1Chaire de finance quantitative, Laboratoire MICS, CentraleSup´elec,Universit´eParis Saclay May 22, 2018 Abstract Market making is one of the most important aspects of algorithmic trading, and it has been studied quite extensively from a theoretical point of view. The practical implementation of so- called "optimal strategies" however suffers from the failure of most order book models to faithfully reproduce the behaviour of real market participants. This paper is twofold. First, some important statistical properties of order driven markets are identified, advocating against the use of purely Markovian order book models. Then, market making strategies are designed and their performances are compared, based on simulation as well as backtesting. We find that incorporating some simple non-Markovian features in the limit order book greatly improves the performances of market making strategies in a realistic context. Keywords : limit order books, Markov decision process, market making 1 Introduction Most modern financial markets are order-driven markets, in which all of the market participants display the price at which they wish to buy or sell a traded security, as well as the desired quantity. This model is widely adopted for stock, futures and option markets, due to its superior transparency. In an order-driven market, all the standing buy and sell orders are centralised in the limit order book (LOB). An example LOB is given in Figure 1, together with some basic definitions. Orders in the LOB are generally prioritized according to price and then to time according to a FIFO rule.
    [Show full text]
  • Flights to Safety
    Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. Flights to Safety Lieven Baele, Geert Bekaert, Koen Inghelbrecht, and Min Wei 2014-46 NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. References in publications to the Finance and Economics Discussion Series (other than acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers. Flights to Safety* Lieven Baele1 Geert Bekaert2 Koen Inghelbrecht3 Min Wei4 June 2014 Abstract Using only daily data on bond and stock returns, we identify and characterize flight to safety (FTS) episodes for 23 countries. On average, FTS days comprise less than 3% of the sample, and bond returns exceed equity returns by 2.5 to 4%. The majority of FTS events are country-specific not global. FTS episodes coincide with increases in the VIX and the Ted spread, decreases in consumer sentiment indicators and appreciations of the Yen, Swiss franc, and US dollar. The financial, basic materials and industrial industries under-perform in FTS episodes, but the telecom industry outperforms. Money market instruments, corporate bonds, and commodity prices (with the exception of metals, including gold) face abnormal negative returns in FTS episodes. Hedge funds, especially those belonging to the “event-driven” styles, display negative FTS betas, after controlling for standard risk factors.
    [Show full text]
  • Nasdaq Market Center Systems Description
    I. Introduction and Background The Nasdaq Market Center is a fully integrated order display and execution system for all Nasdaq National Market securities, Nasdaq SmallCap Market securities, and securities listed on other markets. The Nasdaq Market Center is a voluntary, open- access system that accommodates diverse business models and trading preferences. In contrast to traditional floor-based auction markets, Nasdaq has no single specialist through which transactions pass, but rather uses technology to aggregate and display liquidity and make it available for execution. The Nasdaq Market Center allows market participants to enter unlimited quotes and orders at multiple price levels. Quotes and orders of all Nasdaq Market Center participants are integrated and displayed via data feeds to market participants and other Nasdaq data subscribers. Nasdaq Market Center participants are able to access the aggregated trading interest of all other Nasdaq Market Center participants in accordance with an order execution algorithm that adheres to the principle of price-time priority.1 II. Nasdaq Market Participants The Nasdaq Market Center accommodates a variety of market participants. Market makers are securities dealers that buy and sell securities at prices displayed in Nasdaq for their own account (principal trades) and for customer accounts (agency trades). Market makers actively compete for investor orders by displaying quotations representing their buy and sell interest – plus customer limit orders – in securities quoted in the Nasdaq Market Center. By standing ready to buy and sell shares of a company’s stock, market makers provide to Nasdaq-listed companies and other companies quoted in the Nasdaq Market Center a valuable service.
    [Show full text]
  • Electronic Communications Networks and Liquidity on the Nasdaq
    Electronic Communications Networks and Liquidity on the Nasdaq James P. Weston Jones School of Management Rice University Background / Motivation • Electronic Communications Networks have been growing exponentially • Technological change has reduced search costs – leading to disintermediation • ECN’s are fast, cheap, and anonymous Background / Motivation • How has the growth of trading over ECNs affected: – Competition on the Nasdaq? • Trading costs • market concentration – Liquidity on the Nasdaq ? • Spreads •Depths Previous Problems on Nasdaq • Evidence of Imperfect Competition on Nasdaq – Christie and Schultz (JF 1994) – Weston (JF 2000) • New Regulations help ECNs • How have ECNs affected Competition ? What are ECNs ? • Electronic trading systems that use automated software to match buyers and sellers – Either internally or – Routed to Nasdaq • Currently regulated as brokers What makes ECNs different? • Unlike Traditional Nasdaq dealers, ECNs are AGENTS • ECNs commit no capital • ECNs charge an agent’s fee (Nasdaq dealers cannot charge such a fee) • Face no inventory / information costs What drives the growth of ECNs ? • Investors trade Directly • Electronic limit order book • Fast execution • Anonymity ECNs are anonymous • Nasdaq dealers may know the identity of the counter-party • ECN trading is completely anonymous • Great benefit to institutional investors ECNs increase transparency • Display orders off of the inside quotes • Allows investors to access limit order book • However – not a C.L.O.B. Major Players • Instinet (Reuters)
    [Show full text]
  • Guide to Going Public Strategic Considerations Before, During and Post-IPO Contents
    Guide to going public Strategic considerations before, during and post-IPO Contents 1 | Guide to going public Foreword For private companies seeking to raise capital and capital-held companies considering their strategic options for funding for provide exits for their shareholders, an initial public growth, including a public listing. We have professionals with extensive, offering (IPO) can be a superior route and strategic proven experience with domestic and international capital markets. Our professionals have deep knowledge of your industry, which allows us to option to funding growth and to access deep pools of create interdisciplinary teams that will steer you onward, through and liquidity. While challenging markets will come and go, beyond your IPO and support your plans for growth. it’s the companies that are fully prepared that will We are confident this Guide to going public will give you an initial overview best be able to create value and fully leverage the and checklists of the key phases in going public from a global perspective. IPO windows of opportunity whenever they are open. It is based on EY insights from many IPO transactions, to help you begin Companies that have completed a your IPO value journey, so that you are well prepared to transform your successful IPO know the process private company into a successful public company that continually delivers The companies that involves the complete transformation value to its shareholders. With more than 30 years’ experience helping of the people, processes and culture companies prepare, grow and adapt to life as a public entity, we are well- are fully prepared will be of the organization from a private suited to take you on your IPO journey providing tailored support and enterprise to a public one.
    [Show full text]
  • Market Microstructure Steven Skiena
    Lecture 24: Market Microstructure Steven Skiena Department of Computer Science State University of New York Stony Brook, NY 11794–4400 http://www.cs.sunysb.edu/∼skiena Types of Buy/Sell Orders Brokers can typically perform the following buy/sell orders for exchange traded assets: • Market orders request the trade happen immediately at the best current price. • Limit orders demand a given or better price at which to buy or sell the asset. Nothing happens unless a matching buyer or seller is found. • Stop or stop-loss order becomes a market order when a given price is reached by the market on the downside. This enables an investor to minimize losses in a market reversal, but does not guarantee the given price. • Market-if-Touched order (MIT) becomes a market order when a given price is reached by the market on the upside. This enables an investor to take profits when they are available, but does not guarantee them the given price. The volume and distribution of stop and limit orders in principle contains information about future price movements. Theory argues against making such orders as giving away an option for no payoff, however, such orders are useful particularly for modest-sized investments. The computational and financial details of trading are called market microstructure. Market Order Book The priority queues of open limit orders form the order book. http://www.tradingday.com/cgi-bin/stock quotes charts.cgi?s=MSFT Properties of the Order Book Orders with the same price are prioritorized by arrival time. The difference between bid and ask is called the spread.
    [Show full text]