scholarly journals The Impact of Iceberg Orders in Limit Order Books

2017 ◽  
Vol 07 (03) ◽  
pp. 1750007 ◽  
Author(s):  
Stefan Frey ◽  
Patrik Sandås
Keyword(s):  
Informed Trading ◽  
Price Impact ◽  
Order Book ◽  
Limit Order ◽  
Reduced Order ◽  
Market Participants ◽  
Limit Orders ◽  
Order Books ◽  
Limit Order Books ◽  
The Impact

We examine the impact of iceberg orders on the price and order flow dynamics in limit order books. Iceberg orders allow traders to simultaneously hide a large portion of their order size and signal their interest in trading to the market. We show that when market participants detect iceberg orders they tend to strongly respond by submitting matching market orders consistent with iceberg orders facilitating the search for latent liquidity. The greater the fraction of an iceberg order that is executed, the smaller is its price impact consistent with liquidity rather than informed trading. The presence of iceberg orders is associated with increased trading consistent with a positive liquidity externality, but the reduced order book transparency associated with iceberg orders also creates an adverse selection cost for limit orders that may partly offset any gains.

scholarly journals Instantaneous Order Impact and High-Frequency Strategy Optimization in Limit Order Books

2017 ◽  
Vol 03 (02) ◽  
pp. 1850001 ◽  
Author(s):  
Federico Gonzalez ◽  
Mark Schervish
Keyword(s):  
High Frequency ◽  
Stock Exchange ◽  
Markov Chain Model ◽  
Optimal Order ◽  
Order Book ◽  
Chain Model ◽  
Limit Order ◽  
Limit Orders ◽  
Order Books ◽  
The Impact

We propose a limit order book (LOB) model with dynamics that account for both the impact of the most recent order and volume imbalance. To model these effects jointly we introduce a discrete Markov chain model. We then find the policy for optimal order choice and control. The optimal policy derived uses limit orders, cancellations and market orders. It looks to avoid non-execution and adverse selection risk simultaneously. Using ultra high-frequency data from the NASDAQ stock exchange we compare our policy with other submission strategies that use a subset of all available order types and show that ours significantly outperforms.

Limit order books: a systematic review of literature

2020 ◽  
Vol 12 (4) ◽  
pp. 505-541
Author(s):  
Abhinava Tripathi ◽  
Vipul Vipul ◽  
Alok Dixit
Keyword(s):  
Financial Markets ◽  
Information Dissemination ◽  
Future Research ◽  
Order Book ◽  
Limit Order ◽  
Content Type ◽  
Research Areas ◽  
Order Books ◽  
Intelligent Models ◽  
Limit Order Books

Purpose This study aims to provide a systematic literature review of the research study in the area of limit order book (LOB) mechanism of trading and its implications for market efficiency. The study attempts to document the recent theoretical developments and empirical findings from the literature exhaustively and identifies the research gaps for future research. Design/methodology/approach The study uses seven reputable databases to select 2,514 research studies spanning over 1981-2018 (finally compressed to a pool of 103 articles, based on relevance and impact). The study uses bibliometric network visualization and text analytics to categorize and examine the literature. The chosen articles are compiled and analyzed to provide a comprehensive account of the current research on LOBs. Findings The recent LOB literature is summarized on various criteria as follows: sub-areas, the types of economies and markets, methodologies and the LOB measures. The review identifies a dearth of studies on the LOBs in emerging markets. It suggests the potential research areas as intraday studies in emerging LOB markets; application of market indicators based on deeper levels of LOB, beyond the best prices; market fragmentation, order routing decision and its impact on order execution quality; optimal display of LOB levels; liquidity dynamics in quote-driven markets vis-à-vis LOB markets; effect of high-frequency trading on market microstructure; application of advanced techniques (e.g. machine learning models, zero-intelligent models); relationship between the trading speed, order aggressiveness, shape and resilience of the order book and informed trading; and information content of the auxiliary order submission strategies, including cancellation, amendments and hidden orders. Originality/value For the past 15 years, to the best of the knowledge, a comprehensive review of the literature on LOBs has not been published. The financial markets have transformed significantly over this period, driven by the adoption of LOBs, low latency trading and technological advancements in information dissemination. This article provides an extensive collection and classification of the literature on LOBs. This would be useful for the practitioners, future researchers and academics in the area of financial markets.

GENERAL SEMI-MARKOV MODEL FOR LIMIT ORDER BOOKS

2017 ◽  
Vol 20 (03) ◽  
pp. 1750019 ◽  
Author(s):  
ANATOLIY SWISHCHUK ◽  
TYLER HOFMEISTER ◽  
KATHARINA CERA ◽  
JULIA SCHMIDT
Keyword(s):  
Markov Model ◽  
Arbitrary Number ◽  
Limit Order Book ◽  
Numerical Results ◽  
Order Book ◽  
Limit Order ◽  
Price Changes ◽  
Diffusion Limits ◽  
Order Books ◽  
Limit Order Books

The paper considers a general semi-Markov model for limit order books with two states that incorporates price changes that are not fixed to one tick. Furthermore, we introduce an even more general case of the semi-Markov model for limit order books that incorporates an arbitrary number of states for the price changes. For both cases, the justifications, diffusion limits, implementations and numerical results are presented for different limit order book data: Apple, Amazon, Google, Microsoft, Intel on 21 June 2012 and Cisco, Facebook, Intel, Liberty Global, Liberty Interactive, Microsoft, Vodafone from 3 November 2014 to 7 November 2014.

scholarly journals Stochastic simulation framework for the limit order book using liquidity-motivated agents

2015 ◽  
Vol 02 (02) ◽  
pp. 1550013 ◽  
Author(s):  
Efstathios Panayi ◽  
Gareth W. Peters
Keyword(s):  
Limit Order Book ◽  
Model Parameters ◽  
Order Book ◽  
Model Framework ◽  
Modeling Framework ◽  
Limit Order ◽  
Trading Agents ◽  
Order Books ◽  
Real Market ◽  
Limit Order Books

In this paper, we develop a new form of simulation model for limit order books based on heterogeneous trading agents, whose motivations are liquidity driven. These agents are abstractions of real market participants, expressed in a stochastic model framework. We develop an efficient way to perform statistical calibration of the model parameters on Level 2 limit order book data from Chi-X, based on a combination of indirect inference and multi-objective optimization. We then demonstrate how such a modeling framework can be of use in testing exchange regulations, as well as informing brokerage decisions and other trading based scenarios.

Liquidity and Information in Limit Order Markets

2019 ◽  
Vol 55 (6) ◽  
pp. 1792-1839 ◽  
Author(s):  
Ioanid Roşu
Keyword(s):  
Dynamic Model ◽  
Informed Trading ◽  
Price Impact ◽  
Limit Order ◽  
Limit Order Markets ◽  
Bid Ask Spread ◽  
Limit Orders ◽  
Informed Traders ◽  
Market Orders

How does informed trading affect liquidity in limit order markets, where traders can choose between market orders (demanding liquidity) and limit orders (providing liquidity)? In a dynamic model, informed trading overall helps liquidity: A higher share of informed traders i) improves liquidity as proxied by the bid–ask spread and market resiliency, and ii) has no effect on the price impact of orders. The model generates other testable implications, and suggests new measures of informed trading.

scholarly journals Price Discovery in the U.S. Treasury Cash Market: On Principal Trading Firms and Dealers

2020 ◽  
Vol 2020 (095) ◽  
pp. 1-36
Author(s):  
James Collin Harkrader ◽  
◽  
Michael Puglia ◽  
Keyword(s):  
Theoretical Models ◽  
Limit Order Book ◽  
Price Impact ◽  
Market Liquidity ◽  
Order Book ◽  
Limit Order ◽  
Limit Orders ◽  
Cash Transaction ◽  
Informational Advantage

We explore the following question: does the trading activity of registered dealers on Treasury interdealer broker (IDB) platforms differ from that of principal trading firms (PTF), and if so, how and to what effect on market liquidity? To do so, we use a novel dataset that combines Treasury cash transaction reports from FINRA’s Trade Reporting and Compliance Engine (TRACE) and publicly available limit order book data from BrokerTec. We find that trades conducted in a limit order book setting have high permanent price impact when a PTF is the passive party, playing the role of liquidity provider. Conversely, we find that dealer trades have higher price impact when the dealer is the aggressive party, playing the role of liquidity taker. Trades in which multiple firms (whether dealers or PTFs) participate on one or both sides, however, have relatively low price impact. We interpret these results in light of theoretical models suggesting that traders with only a “small” informational advantage prefer to use (passive) limit orders, while traders with a comparatively large informational advantage prefer to use (aggressive) market orders. We also analyze the events that occurred in Treasury markets in March 2020, during the onset of the COVID-19 pandemic.

scholarly journals Order Flows and Limit Order Book Resiliency on the Meso-Scale

2017 ◽  
Vol 03 (03n04) ◽  
pp. 1850006 ◽  
Author(s):  
Kyle Bechler ◽  
Mike Ludkovski
Keyword(s):  
Price Change ◽  
Weighted Average ◽  
Order Book ◽  
Trade Imbalance ◽  
Limit Order ◽  
Tick Data ◽  
Order Books ◽  
Limit Order Books ◽  
Shape Metrics ◽  
Meso Scale

We investigate the behavior of limit order books (LOBs) on the meso-scale motivated by order execution scheduling algorithms. To do so, we carry out empirical analysis of the order flows from market and limit order submissions, aggregated from tick-by-tick data via volume-based bucketing, as well as various LOB depth and shape metrics. We document a nonlinear relationship between trade imbalance and price change, which however can be converted into a linear link by considering a weighted average of market and limit order flows. We also document a hockey-stick dependence between trade imbalance and one-sided limit order flows, highlighting numerous asymmetric effects between the active and passive sides of the LOB. To address the phenomenological features of price formation, we construct regression models to identify the most significant predictors, confirming the predictive power of limit order flows. Another finding is that the deeper LOB shape, rather than just the book imbalance, is more relevant on this timescale. The empirical results are based on analysis of six large-tick assets from Nasdaq.

scholarly journals Testing Stylized Facts of Bitcoin Limit Order Books

2019 ◽  
Vol 12 (1) ◽  
pp. 25 ◽  
Author(s):  
Matthias Schnaubelt ◽  
Jonas Rende ◽  
Christopher Krauss
Keyword(s):  
Time Scales ◽  
Order Book ◽  
Methodological Framework ◽  
Limit Order ◽  
Data Set ◽  
Volatility Clustering ◽  
Trade Size ◽  
Us Dollar ◽  
Order Books ◽  
Limit Order Books

The majority of electronic markets worldwide employ limit order books, and the recently emerging exchanges for cryptocurrencies pose no exception. With this work, we empirically analyze whether commonly observed empirical properties from established limit order exchanges transfer to the cryptocurrency domain. Based on the literature, we establish a structured methodological framework to conduct analyses in a systematic and comprehensive way. We then present results from a unique and extensive limit order data set acquired from major cryptocurrency exchanges for the currency pair Bitcoin to US Dollar. We recover many observations from mature markets, such as a symmetry between the average ask and the average bid side of the order book, autocorrelation in returns on the smallest time scales only, volatility clustering and the timing of large trades. We also observe some idiosyncrasies: The distributions of trade size and limit order prices deviate from commonly observed patterns. Also, we find limit order books to be relatively shallow and liquidity costs to be relatively high when compared to established markets.

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