Price Impact in a Latent Order Book

Purpose – The purpose of this paper is to examine the changes in the price impact of trades in the major Korean stock market following the introduction of disclosure to all traders of the top five brokers on the buy-side and the top five brokers on the sell-side of trades in real time for each stock in the KOSDAQ market. Design/methodology/approach – The paper uses several alternative metrics for the price impact of trades. The study applies estimation methodology that accounts for the potential endogeneity of other market quality proxies, which are used as control variables in price impact regressions, by utilizing two-stage-least-square methods with fixed effect specification. Findings – This study finds that the permanent price impact (information effect) of both buyer- and seller-initiated trades increases, which indicates that information is disseminated quicker in a transparent market. Uninformed trades have a larger permanent price impact than informed trades on both the buy and sell sides. The liquidity price effects are found to be mixed for buys and sells. Research limitations/implications – The study supports the current policy of the Korean Exchange to publicly display the five most active broker IDs on both the buy and sell sides, as it attracts both informed and liquidity traders, leading to faster price discovery in a more transparent market. However, a future study which analyzes the change in the market quality in both local markets would provide a complete picture of the effects of the policy. Originality/value – Earlier studies documenting the effect of broker ID disclosure on market quality used effective spreads, market depth or order book imbalance as market quality measures. This study contributes to the existing literature by examining the changes in direct measures of the private information effect and liquidity effect of trades in a stock market – the Korean Stock Exchange – when the other part of the exchange (the KOSDAQ stock market) shifts to public broker ID transparency at the same transparency level.

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The Price Impact of Order Book Events

Journal of Financial Econometrics ◽

10.1093/jjfinec/nbt003 ◽

2013 ◽

Vol 12 (1) ◽

pp. 47-88 ◽

Cited By ~ 140

Author(s):

R. Cont ◽

A. Kukanov ◽

S. Stoikov

Keyword(s):

Price Impact ◽

Order Book

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

Finance and Economics Discussion Series ◽

10.17016/feds.2020.096 ◽

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.

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The cross-interval price impact model and its empirical analysis on cryptocurrency order book

Personal and Ubiquitous Computing ◽

10.1007/s00779-021-01651-z ◽

2021 ◽

Author(s):

Bin Teng ◽

Sicong Wang ◽

Qinghua Ren ◽

Qi Hao ◽

Yufeng Shi

Keyword(s):

Empirical Analysis ◽

Price Impact ◽

Order Book ◽

Impact Model ◽

The Cross

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The price impact of order book events: market orders, limit orders and cancellations

Quantitative Finance ◽

10.1080/14697688.2010.528444 ◽

2012 ◽

Vol 12 (9) ◽

pp. 1395-1419 ◽

Cited By ~ 85

Author(s):

Zoltán Eisler ◽

Jean-Philippe Bouchaud ◽

Julien Kockelkoren

Keyword(s):

Price Impact ◽

Order Book ◽

Limit Orders ◽

Market Orders

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RESILIENT PRICE IMPACT OF TRADING AND THE COST OF ILLIQUIDITY

International Journal of Theoretical and Applied Finance ◽

10.1142/s0219024913500374 ◽

2013 ◽

Vol 16 (06) ◽

pp. 1350037 ◽

Cited By ~ 26

Author(s):

ALEXANDRE ROCH ◽

H. METE SONER

Keyword(s):

Price Impact ◽

Maximization Problem ◽

Order Book ◽

Bid Ask Spread ◽

Price Impacts ◽

Liquidity Costs ◽

Utility Maximization Problem ◽

The Cost

We construct a model for liquidity risk and price impacts in a limit order book setting with depth, resilience and tightness. We derive a wealth equation and a characterization of illiquidity costs. We show that we can separate liquidity costs due to depth and resilience from those related to tightness, and obtain a reduced model in which proportional costs due to the bid-ask spread is removed. From this, we obtain conditions under which the model is arbitrage free. By considering the standard utility maximization problem, this also allows us to obtain a stochastic discount factor and an asset pricing formula which is consistent with empirical findings (e.g., Brennan and Subrahmanyam (1996); Amihud and Mendelson (1986)). Furthermore, we show that in limiting cases for some parameters of the model, we derive many existing liquidity models present in the arbitrage pricing literature, including Çetin et al. (2004) and Rogers and Singh (2010). This offers a classification of different types of liquidity costs in terms of the depth and resilience of prices.

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