Order book approach to price impact

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.     

Market or limit orders?

In this paper we investigate the problem of optimal order placement of an asset listed on an exchange using both market and limit orders in a simple model of market dynamics. We seek to understand under which settings it is optimal to place limit or market orders. Limit orders typically lower transaction costs but increase the risk of incomplete order execution, whereas market orders typically have higher transaction costs but are guaranteed to be executed. Rather than considering order book dynamics to determine if a limit order is executed we rely on price dynamics for this. We look at implementation shortfall in this setup with market impact of trading and propose a dynamic program to find the optimal placement of both market and limit orders for risk-neutral and risk-averse traders. With this we find a bound on the expected cost of trading and show that a trader who behaves optimally should always expect to pay less to trade less. We then solve the dynamic program numerically and examine optimal order placement strategies. We find that the decision between market and limit orders is sensitive to price volatility, risk aversion, and trading costs.     

Limit Order Book as a Market for Liquidity

We develop a dynamic model of a limit order market populatedby strategic liquidity traders of varying impatience. In equilibrium,patient traders tend to submit limit orders, whereas impatienttraders submit market orders. Two variables are the key determinantsof the limit order book dynamics in equilibrium: the proportionof patient traders and the order arrival rate. We offer severaltestable implications for various market quality measures suchas spread, trading frequency, market resiliency, and time toexecution for limit orders. Finally, we show the effect of imposinga minimal price variation on these measures.     

Equilibrium in a Dynamic Limit Order Market

We model a dynamic limit order market as a stochastic sequential game with rational traders. Since the model is analytically intractable, we provide an algorithm based on Pakes and McGuire (2001) to find a stationary Markov‐perfect equilibrium. We then generate artificial time series and perform comparative dynamics. Conditional on a transaction, the midpoint of the quoted prices is not a good proxy for the true value. Further, transaction costs paid by market order submitters are negative on average, and negatively correlated with the effective spread. Reducing the tick size is not Pareto improving but increases total investor surplus.     

Capturing Order Book Dynamics in the Interbank EUR/PLN Spot Market

In this paper, I examine order submissions and cancellations in the Reuters Dealing 3000 Spot Matching System, the main order-driven market for interbank trading of the euro/z?oty (EUR/PLN) currency pair. I generalize the asymmetric autoregressive conditional duration (AACD) model of Bauwens and Giot (2003) with respect to more than two competing risks. With the new multistate AACD model, I examine the timing of different order submissions and cancellations that take place on different sides of the market and vary according to their level of aggressiveness. I investigate different liquidity or information-oriented factors that exert an influence on the dynamics of the limit order book.     

Price discovery in auction markets: a look inside the black box

Opening mechanisms play a crucial role in information aggregationfollowing the overnight nontrading period. This article examinesthe process of price discovery at the New York Stock Exchangesingle-price opening auction. We develop a theoretical modelto explain the determinants of the opening price and test themodel using order-level data. We show that the presence of designateddealers facilitates price discovery relative to a fully automatedcall auction market. This is consistent with specialists extractinginformation from observing the evolution of the limit orderbook. In addition, the specialist's opening trade reflects noninformationalfactors such as price stabilization requirements.     

Market conditions and order-type preference

This paper proposes a methodology to investigate the relative preference of limit orders when changes in market conditions lead to temporary violations of the zero-profit condition. We formulate this preference as a function of the intraday structural price components of information and liquidity, the time variation in which is driven by expectations of an expandable set of measures of time varying market conditions. This is then used to investigate when the equilibrium zero-profit condition might be violated, which would lead to a preference for a particular order type. The resulting theoretical and empirical predictions of advanced microstructural pricing models suggest that limit orders should be preferred during periods of intense market activity, such as periods of high volume and short duration, when the price components are positively correlated, and during periods of less intense activity when the components are negatively correlated.     

Aggressive Orders and the Resiliency of a Limit Order Market

We analyze the resiliency of a pure limit order market by investigating the limit order book (bid and ask prices, spreads, depth and duration), order flow and transaction prices in a window of best limit updates and transactions around aggressive orders (orders that move prices). We find strong persistence in the submission of aggressive orders. Aggressive orders take place when spreads and depths are relatively low, and they induce bid and ask prices to be persistently different after the shock. Depth and spread remain also higher than just before the order, but do return to their initial level within 20 best limit updates after the shock. Relative to the sample average, depths stay around their mean before and after aggressive orders, whereas spreads return to their mean after about twenty best limit updates. The initial price impact of the aggressive order is partly reversed in the subsequent transactions. However, the aggressive order produces a long-term effect as prices show a tendency to return slowly to the price of the aggressive order.We thank Theo Nijman, Erik Theissen, Rob van den Goorbergh, Josef Zechner (editor) and an anonymous referee for valuable comments on an earlier draft as well as seminar participants at the EEA-conference in Venice, the CFS Conference on Market Design in Eltville, CORE, Leuven and Tilburg. The first and last authors gratefully acknowledge financial assistance from FWO-Flanders under contract G.0333.     

Price Movers on the Stock Exchange of Thailand: Evidence from a Fully Automated Order‐Driven Market

This study examines which trade sizes move stock prices on the Stock Exchange of Thailand (SET), a pure limit order market, over two distinct market conditions of bull and bear. Using intraday data, the study finds that large‐sized trades (i.e., those larger than the 75th percentile) account for a disproportionately large impact on changes in traded and quoted prices. The finding remains even after it has been subjected to a battery of robustness checks. In contrast, the results of studies conducted in the United States show that informed traders employ trade sizes falling between the 40th and 95th percentiles ( Barclay and Warner, 1993 ; Chakravarty, 2001 ). Our results support the hypothesis that informed traders in a pure limit order market, such as the SET, where there are no market makers, also use larger‐size trades than those employed by informed traders in the United States.     

Orderbook demand for corporate bonds

We examine the determinants of investor demand for corporate bond offerings using novel data on the primary market orderbook size. We find that credit risk and bond market presence are significant in explaining investor demand. These effects are more pronounced during the crisis periods including the global financial crisis and eurozone crisis as well as during the postcrisis periods. Our results also highlight the size of the bond investor order depends on information asymmetry costs and the benefit of diversifications, as investor demand is lower for new issuers as well as very frequent issuers. The levels of investor demand have important economic consequences for bond issuers as high investor demand shortens the time to subsequent bond issues and potentially reduces the firm's cost of capital at issuance.     

Volatility,Depth, and Order Composition: Evidence from a Pure Limit Order Futures Market

This paper investigates market behaviors (such as volatility, depth, and volume) and order-flow decomposition in a pure limit order futures market, the Taiwan Futures Exchange. The results are different from those in equity markets due to relatively high adverse selection costs in futures markets. We show that a volatility (depth) increase is followed by a depth (volatility) decrease; a market order increase (decrease) subsequently induces higher (lower) volatility; and a limit order increase (decrease) results in more (less) market orders and limit orders. When the upside (downside) volatility rises, buyers decrease (increase) subsequent limit bid orders, and sellers increase (decrease) limit ask orders.     

Market making with minimum resting times

We show how the supply of liquidity in order-driven markets is affected if limit orders (LOs) are forced to rest in the limit order book for a minimum resting time (MRT) before they can be cancelled. The bid-ask spread increases as the MRT increases because market makers (MMs) increase the depth of their LOs to protect them from being picked off by other traders. We also show that the expected profits of the MMs increase when the MRT increases. The intuition is as follows. As the MRT increases, there are two opposing forces at work. One, the longer the MRT, the more likely the LOs are to be filled and, on average, shares are sold at a loss. Two, because the depth of the posted LOs increases, the probability that the LO is picked off by other traders before the end of the MRT decreases. The net effect is that a longer MRT leads to a higher expected profit. We also show that the depth of LOs increases when the volatility of the price of the asset increases. Also, the depth of LOs increases when the arrival rate of market orders increases because it is less likely that LOs will be picked off by the end of the MRT. Finally, our model also makes predictions about the overall liquidity of the market. We show that MMs choose to supply the minimum amount of shares per LO allowed by the exchange because expected profits are maximised when liquidity provided is lowest.     

Cost of Transacting and Expected Returns in the Nasdaq Market

This article empirically examines the liquidity premium predicted by the Amihud and Mendelson (1986) model using Nasdaq data over the 1973–1990 period. The results support the model and are much stronger than for the New York Stock Exchange (NYSE), as reported by Chen and Kan (1989) and Eleswarapu and Reinganum (1993) . I conjecture that the stronger evidence on the Nasdaq is due to the dealers' inside spreads on the Nasdaq being a better proxy for the actual cost of transacting than the quoted spreads on the NYSE, since the Nasdaq dealers do not face competition from limit orders or floor traders.     

High-Frequency Trading and Market Performance

We study the consequences of, and potential policy responses to, high-frequency trading (HFT) via the tradeoff between liquidity and information production. Faster speeds facilitate HFT, with consequences for this tradeoff: Information production decreases because informed traders have less time to trade before HFTs react, but liquidity (measured by the bid-ask spread) improves because informational asymmetries decline. HFT also pushes outcomes inside the frontier of this tradeoff. However, outcomes can be restored to the frontier by replacing the limit order book with one of two alternative mechanisms: delaying all orders except cancellations or implementing frequent batch auctions.     

IPO Trading without Market Makers or Underwriter Price Support

We investigate the trading behavior and liquidity supply of Chinese initial public offerings (IPOs) that trade in an order‐driven market system with pure limit order books where no market makers or price support is allowed. We find large trades and quoted depths dominate the first day of trading, but this pattern quickly reverses as small trades and quoted depths are more prevalent on subsequent trading days. Quoted depths are positively related to the number of shares offered in the IPO and trade size, but are negatively related to underpricing. Trade size and transaction immediacy are positively related, and large and positive (negative) order imbalance is associated with more aggressive buys (sells). Finally, long‐run performance is not related to initial order imbalance. Overall, our results suggest that despite underwriters not participating in the IPO aftermarket, liquidity provision evolves very quickly and price discovery is immediately reflected in prices.     

The Development of Secondary Market Liquidity for NYSE-Listed IPOs

For NYSE‐listed IPOs, limit order submissions and depth relative to volume are unusually low on the first trading day. Initial buy‐side liquidity is higher for IPOs with high‐quality underwriters, large syndicates, low insider sales, and high premarket demand, while sell‐side liquidity is higher for IPOs that represent a large fraction of outstanding shares and have low premarket demand. Our results suggest that uncertainty and offer design affect initial liquidity, though order flow stabilizes quickly. We also find that submission strategies are influenced by expected underwriter stabilization and preopening order flow contains information about both initial prices and subsequent returns.     

Liquidity provision with limit orders and a strategic specialist

This article presents a microstructure model of liquidity provisionin which a specialist with market power competes against a competitivelimit order book. General solutions, comparative statics andexamples are provided first with un-informative orders and thenwhen order flows are informative. The model is also used toaddress two optimal market design issues. The first is the effectof 'tick' size - for example, eighths versus decimal pricing- on market liquidity. Institutions trading large blocks havea larger optimal tick size than small retail investors, butboth prefer a tick size strictly greater than zero. Second,a hybrid specialist/limit order market (like the NYSE) providesbetter liquidity to small retail and institutional trades, buta pure limit order market (like the Paris Bourse) may offerbetter liquidity on mid-size orders.     

Do Proprietary Algorithmic Traders Withdraw Liquidity during Market Stress?

We investigate the role of proprietary algorithmic traders in facilitating liquidity in a limit order market. Using order‐level data from the National Stock Exchange of India, we find that proprietary algorithmic traders increase limit order supply following periods of both high short‐term stock‐specific volatility and extreme stock price movement. Even following periods of high marketwide volatility, they do not decrease their supply of liquidity. We define orders from high‐frequency traders as a subclass of orders from proprietary algorithmic traders that are revised in less than three milliseconds. The behavior of high‐frequency trading mimics the behavior of its parent class. This is inconsistent with the theory that fast traders leave the market when stress situations arise, although their limit‐order‐supplying behavior becomes weaker when the increase in short‐term volatility is more informational than transitory. Agency algorithmic traders and nonalgorithmic traders behave opposite to proprietary algorithmic traders by reducing the supply of liquidity during stress situations. The presence of faster traders in the market possibly instills the fear of adverse selection in them. We document that the order imbalance of agency algorithmic traders is positively related to future short‐term returns, whereas the order imbalance of proprietary algorithmic traders is negatively related to future short‐term returns.     

Risk Aversion, Transparency, and Market Performance

Using a model of market making with inventories based on Biais (1993), we find that investors obtain more favorable execution prices, and they hence invest more, when markets are fragmented. In our model, risk-averse dealers use less aggressive price strategies in more transparent markets (centralized) because quote dissemination alleviates uncertainty about the prices quoted by other dealers and, hence, reduces the need to compete aggressively for order flow. Further, we show that the move toward greater transparency (centralization) may have detrimental effects on liquidity and welfare.     

Is the Electronic Open Limit Order Book Inevitable?

Under fairly general conditions, the article derives the equilibrium price schedule determined by the bids and offers in an open limit order book. The analysis shows: (1) the order book has a small-trade positive bid-ask spread, and limit orders profit from small trades; (2) the electronic exchange provides as much liquidity as possible in extreme situations; (3) the limit order book does not invite competition from third market dealers, while other trading institutions do; (4) If an entering exchange earns nonnegative trading profits, the consolidated price schedule matches the limit order book price schedule.