Competition,signaling and non-walking through the book: Effects on order choice

We investigate the effects of competition and signaling in a pure order driven market and examine the trading patterns of agents when walking through the book is not allowed. Our results suggest that the variables capturing the cost of a large market order are not informative for an impatient trader under this market mechanism. We also document that the competition effect is not present only at the top of the book but persistent beyond the best quotes. Moreover, it dominates the signaling effect for both a limit order and a market order trader. Finally, we show that institutional investors’ order submission strategies are characterized by only a few pieces of the limit order book information. This is consistent with informed traders placing orders based on their own private valuations rather than the state of the book.     

On the hidden side of liquidity

This article deals with the informativeness of iceberg orders, also known as hidden limit orders (HLOs). Namely, we analyze how the market reacts when the presence of hidden volume in the limit order book is revealed by the trading process. We use high-frequency book and transaction data from the Spanish Stock Exchange, including a large sample of executed HLOs. We show that just when hidden volume is detected, traders on the opposite side of the market become more aggressive, exploiting the opportunity to consume more than expected at the best quotes. However, neither illiquidity nor volatility increases in the short term. Furthermore, the detection of hidden volume has no relevant price impact. Overall, our results suggest that market participants do not attribute any relevant information content to the hidden side of liquidity.     

Price impact and bursts in liquidity provision

This paper analyses brief episodes of high-intensity quote turnover and revision—‘bursts’ in quotes—in the US equity market. Such events occur very frequently, several hundred times a day for actively traded stocks. We find significant price impact associated with these market maker initiated events, about five times higher than during non-burst periods. Bursts in quotes are concurrent with short-lived structural breaks in the informational relationship between market makers and market takers. During bursts, market makers no longer passively impound information from order flow into quotes—a departure from the traditional market microstructure paradigm. Rather, market makers significantly impact prices during bursts in quotes. Further analysis shows that there is asymmetry in adverse selection between the bid and ask sides of the limit order book and only a sub-population of market makers enjoys an informational advantage during bursts. Market makers on the side opposite the burst suffer elevated adverse selection costs, while market makers on the side of the burst realize positive spread, irrespective of the order flow direction. Our results call attention to the need for a new microstructure perspective in understanding modern high-frequency limit order book markets and the quote manipulation strategies at the disposal of the fast market makers.     

Price clustering on the limit-order book: Evidence from the Stock Exchange of Hong Kong

We examine the clustering pattern in trade and quote prices on the electronic limit order book of the Stock Exchange of Hong Kong (SEHK). Earlier research into clustering focuses on transaction prices only. We study clustering on quote prices over a maximum of five queues on the limit order book. We observe an abnormally high frequency of even and integer prices in trade and quote prices for all tick size groups on the SEHK. The deeper quotes display stronger clustering than the best quotes, indicating that the farther away the quotes are from the best queue, the less information they carry. Our analysis further reveals that an extremely fine tick size itself works as a binding constraint to hinder the price resolution process. We also find that short sale prohibition imposed on the majority of stocks listed on the SEHK causes a significant bias in clustering towards the ask side of the limit order book. This implies that a short sale prohibition impairs efficient price discovery in the market.     

Scheduled macro-news effects on a Euro/US dollar limit order book around the 2008 financial crisis

Characteristics of a complete limit order book (LOB) for Euro/US dollar in 2006-09, are asymmetrically affected by scheduled macro news announcements during the financial crisis. Depth is the most responsive characteristic followed by spread, volatility and slope. Depth and volatility respond more to expansion surprises, while spread and slope are more sensitive to recession. The effect of the announcement’s occurrence without surprise is overwhelmingly positive (negative) for depth and volatility (spread) in both regimes. This effect is mitigated by the surprise. More than half of US scheduled news surprises have state dependent depth coefficients, most with opposing signs between recession and expansion. Using all quote levels generates stronger characteristic response, indicating the existence of information outside of the best quotes.     

Multiple-limit trades: empirical facts and application to lead–lag measures

Order splitting is a standard practice in trading: traders constantly scan the limit order book and choose to limit the size of their market orders to the quantity available at the best limit, thereby controlling the market impact of their orders. In this article, we focus on the other trades, multiple-limit trades that go through the best available price in the order book, or ‘trade-throughs’. We provide various statistics on trade-throughs: frequency, volume, intraday distribution, market impact, etc., and present a new method for the measurement of lead–lag parameters between assets, sectors or markets.     

Price Movement Effects on the State of the Electronic Limit‐Order Book

This paper investigates public‐trader order‐placement strategies by examining the relations between the state of the limit‐order book and previous price movements. There is support for an information effect, as traders become more aggressive in buying and more patient in selling after previous positive stock returns. The widening of the bid‐ask spread also causes traders to place less aggressive orders. However, there is no evidence of the options effect on limit‐order trading. This study also reveals that orders at the best quotes react faster and complete the adjustment earlier than orders that are far away from the best quotes.     

Traders’ choice between limit and market orders: evidence from NYSE stocks

In this paper, we examine a trader's order choice between market and limit orders using a sample of orders submitted through NYSE SuperDot. We find that traders place more limit orders relative to market orders when: (1) the spread is large, (2) the order size is large, and (3) they expect high transitory price volatility. A rise in informational volatility appears neither to increase nor decrease the placement of limit orders. We also find that a rise in lagged price volatility decreases the size of spread, which is driven by the increase in the placement of limit orders.     

A Specialist's Quoted Depth and the Limit Order Book

By partitioning quoted depth into the specialist's contribution and the limit order book's contribution, the paper investigates whether specialists manage quoted depth to reduce adverse selection risk. The results show that both specialists and limit order traders reduce depth around information events, thereby reducing their exposure to adverse selection costs. Moreover, specialists' quotes may reflect only the limit order book on the side (or sides) of the market where they believe there is a chance of informed trading. Changes in quoted depth are consistent with specialists managing their inventory as well as having knowledge of the stock's future value.     

The anatomy of buyer–seller dynamics in decentralized markets

In this paper I investigate the nexus between buyer–seller dynamics, financial frictions and market efficiency in decentralized markets. To do so, I introduce financial frictions in a dynamic market with heterogeneous traders. Heterogeneously constrained buyers sequentially enter the market to acquire units of a generic good from heterogeneously endowed sellers. I characterize two closely related classes of equilibria, respectively called homogeneous equilibrium with no entry (HEWNE) and homogeneous equilibrium with entry (HEWE). Both equilibria prescribe a market where only the efficiently endowed type of seller exists in the limit. However, the two equilibria diverge in the specification of agents’ behavior subsequent to trade. In HEWNE, sellers and buyers exit the market upon successful trading. In HEWE, like in supply chains, in every period certain types of buyers replace exiting sellers, thus becoming potential sellers for subsequent waves of buyers. First, I identify the critical role of frictions in steering the complex evolution of market heterogeneity for both classes of equilibria. Secondly, I operationalize the combined study of HEWNE and HEWE to obtain sharp predictions on market efficiency for a range of empirically-relevant situations in which buyer–seller dynamics are decoupled, for example when entry of new sellers is delayed or stopped. Third, I test the theoretical findings against a simulated artificial market.     

The tail risks of FX return distributions: A comparison of the returns associated with limit orders and market orders

This paper measures and compares the tail risks of limit and market orders using Extreme Value Theory. The analysis examines realised tail outcomes using the Dealing 2000-2 electronic broking system based on completed transactions rather than the more common analysis of indicative quotes. In general, limit and market orders exhibit broadly similar tail behaviour, but limit orders have significantly heavier tails and larger tail quantiles than market orders.     

Hidden Liquidity: Some New Light on Dark Trading

Using a laboratory market, we investigate how the ability to hide orders affects traders’ strategies and market outcomes in a limit order book environment. We find that order strategies are greatly affected by allowing hidden liquidity, with traders substituting nondisplayed for displayed shares and changing the aggressiveness of their trading. As traders adapt their behavior to the different opacity regimes, however, most aggregate market outcomes (such as liquidity and informational efficiency) are not affected as much. We also find that opacity appears to increase the profits of informed traders but only when their private information is very valuable.     

Optimal high-frequency trading with limit and market orders

We propose a framework for studying optimal market-making policies in a limit order book (LOB). The bid–ask spread of the LOB is modeled by a tick-valued continuous-time Markov chain. We consider a small agent who continuously submits limit buy/sell orders at best bid/ask quotes, and may also set limit orders at best bid (resp. ask) plus (resp. minus) a tick for obtaining execution order priority, which is a crucial issue in high-frequency trading. The agent faces an execution risk since her limit orders are executed only when they meet counterpart market orders. She is also subject to inventory risk due to price volatility when holding the risky asset. The agent can then also choose to trade with market orders, and therefore obtain immediate execution, but at a less favorable price. The objective of the market maker is to maximize her expected utility from revenue over a short-term horizon by a trade-off between limit and market orders, while controlling her inventory position. This is formulated as a mixed regime switching regular/impulse control problem that we characterize in terms of a quasi-variational system by dynamic programming methods. Calibration procedures are derived for estimating the transition matrix and intensity parameters for the spread and for Cox processes modelling the execution of limit orders. We provide an explicit backward splitting scheme for solving the problem and show how it can be reduced to a system of simple equations involving only the inventory and spread variables. Several computational tests are performed both on simulated and real data, and illustrate the impact and profit when considering execution priority in limit orders and market orders.     

Specialist participation and limit orders

We present a market microstructure model to examine specialist's strategic participation decisions in a security market where there are noise traders, limit order traders, an insider and a specialist. We argue that the specialist's participation rate depends on the depth of the limit book and its uncertainty. In particular, the specialist has incentives to trade against the market trend when the limit book depth is low and to trade with the market trend when the depth is high. Moreover, the specialist's participation rate is positively related to the limit book depth uncertainty and the asset price volatility, but is negative related to the average trading volume. We also discuss the specialist's participation strategies under the NYSE regulation that prohibits the specialist from trading with the market trend.     

Modelling and forecasting liquidity supply using semiparametric factor dynamics

We model the dynamics of ask and bid curves in a limit order book market using a dynamic semiparametric factor model. The shape of the curves is captured by a factor structure which is estimated nonparametrically. Corresponding factor loadings are modelled jointly with best bid and best ask quotes using a vector error correction specification. Applying the framework to four stocks traded at the Australian Stock Exchange (ASX) in 2002, we show that the suggested model captures the spatial and temporal dependencies of the limit order book. We find spill-over effects between both sides of the market and provide evidence for short-term quote predictability. Relating the shape of the curves to variables reflecting the current state of the market, we show that the recent liquidity demand has the strongest impact. In an extensive forecasting analysis we show that the model is successful in forecasting the liquidity supply over various time horizons during a trading day. Moreover, it is shown that the model's forecasting power can be used to improve optimal order execution strategies.     

Order aggressiveness as a metric to assess the usefulness of accounting information

Motivated by the availability of high-frequency data on trading activity, this paper proposes the use of order aggressiveness as a metric to evaluate the usefulness of accounting information. I test, through an analysis of order aggressiveness, whether earnings announcements of firms listed on the Italian Stock Exchange limit order book have information content. I estimate an ordered probit relating order aggressiveness to unexpected earnings and to three market determinants of aggressiveness. Consistent with the theory on the choice between limit and market orders, I find that order aggressiveness increases with the absolute value of unexpected earnings. The results provide evidence on the extent to which the information contained in earnings is used by traders.     

Limit order books and trade informativeness

In the microstructure literature, information asymmetry is an important determinant of market liquidity. The classic setting is that uninformed dedicated liquidity suppliers charge price concessions when incoming market orders are likely to be informationally motivated. In limit order book (LOB) markets, however, this relationship is less clear, as market participants can switch roles, and freely choose to immediately demand or patiently supply liquidity by submitting either market or limit orders. We study the importance of information asymmetry in LOBs based on a recent sample of 30 German Deutscher Aktienindex (DAX) stocks. We find that Hasbrouck's (1991) measure of trade informativeness Granger causes book liquidity, in particular that required to fill large market orders. Picking-off risk due to public news-induced volatility is more important for top-of-the book liquidity supply. In our multivariate analysis, we control for volatility, trading volume, trading intensity and order imbalance to isolate the effect of trade informativeness on book liquidity.     

Quasi-centralized limit order books

A quasi-centralized limit order book (QCLOB) is a limit order book (LOB) in which financial institutions can only access the trading opportunities offered by counterparties with whom they possess sufficient bilateral credit. In this paper, we perform an empirical analysis of a recent, high-quality data set from a large electronic trading platform that utilizes QCLOBs to facilitate trade. We argue that the quote-relative framework often used to study other LOBs is not a sensible reference frame for QCLOBs, so we instead introduce an alternative, trade-relative framework, which we use to study the statistical properties of order flow and LOB state in our data. We also uncover an empirical universality: although the distributions that describe order flow and LOB state vary considerably across days, a simple, linear rescaling causes them to collapse onto a single curve. Motivated by this finding, we propose a semi-parametric model of order flow and LOB state for a single trading day. Our model provides similar performance to that of parametric curve-fitting techniques but is simpler to compute and faster to implement.     

DOES SPEED MATTER? THE ROLE OF HIGH-FREQUENCY TRADING FOR ORDER BOOK RESILIENCY

We analyze limit order book resiliency following liquidity shocks initiated by large market orders. Based on a unique data set, we investigate whether high-frequency traders are involved in replenishing the order book. Therefore, we relate the net liquidity provision of high-frequency traders, algorithmic traders, and human traders around these market impact events to order book resiliency. Although all groups of traders react, our results show that only high-frequency traders reduce the spread within the first seconds after the market impact event. Order book depth replenishment, however, takes significantly longer and is mainly accomplished by human traders’ liquidity provision.