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.     

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.     

Price Clustering on the Tokyo Stock Exchange

This paper examines price clustering on the Tokyo Stock Exchange (TSE). Regardless of tick and lot size, prices ending in zero and five are the most popular. The TSE has no market makers or direct negotiation between traders; therefore, clustering is not explained by collusion or negotiation. Our evidence supports the attraction hypothesis. Clustering also extends to order book depth. There is evidence of strategic trading behavior as traders place orders one price tick better than zero and five to avoid queuing orders at prices ending in these digits. Strategic trading behavior declined and clustering increased when the market became anonymous.     

Explaining Intraday Pattern of Trading Volume from the Order Flow Data

Based on a comprehensive order flow data from the Taiwan stock market, this study examines directly how the intraday pattern of trading volume is related to the trading behavior of both informed and uninformed traders. The results indicate that both informed and uninformed investors have a strong desire to place orders at the market open and the close. Most of the orders at the market open are conservative and hence are waiting orders for price priority. The findings show that intraday trading volume as well as the real orders from both types of investors are J-shaped. In addition, both information and liquidity trading can explain the intraday pattern of trading volume. However, the impact of liquidity trading on volume is slightly higher than that of information trading.     

The Trading Decision and Market Clearing under Transaction Price Uncertainty

This paper models an individual's trading decision, given: (1) his/her demand function to hold shares of an asset, (2) his/her expectation on what the market clearing price will be, and (3) the design of the market which determines how orders will be translated into trades. The particular market design we consider is the batched trading (periodic call) regime. Assuming investors are distributed according to their propensities to hold shares, we model the aggregation of orders to obtain market clearing values of price and volume and to show the way in which, with trading friction, these solutions differ from Pareto efficient values. The importance of this analysis for various issues concerning market design is noted.     

Implied Lévy volatility

We study the cause of large fluctuations in prices on the London Stock Exchange. This is done at the microscopic level of individual events, where an event is the placement or cancellation of an order to buy or sell. We show that price fluctuations caused by individual market orders are essentially independent of the volume of orders. Instead, large price fluctuations are driven by liquidity fluctuations, variations in the market's ability to absorb new orders. Even for the most liquid stocks there can be substantial gaps in the order book, corresponding to a block of adjacent price levels containing no quotes. When such a gap exists next to the best price, a new order can remove the best quote, triggering a large midpoint price change. Thus, the distribution of large price changes merely reflects the distribution of gaps in the limit order book. This is a finite size effect, caused by the granularity of order flow: in a market where participants place many small orders uniformly across prices, such large price fluctuations would not happen. We show that this also explains price fluctuations on longer timescales. In addition, we present results suggesting that the risk profile varies from stock to stock, and is not universal: lightly traded stocks tend to have more extreme risks.     

Order dynamics: Recent evidence from the NYSE

We examine investor order choices using evidence from a recent period when the NYSE trades in decimals and allows automatic executions. We analyze the decision to submit or cancel an order or to take no action. For submitted orders, we distinguish order type (market vs. limit), order side (buy vs. sell), execution method (auction vs. automatic), and pricing aggressiveness. We find that the NYSE exhibits positive serial correlation in order type on an order-by-order basis, which suggests that follow-on order strategies dominate adverse selection or liquidity considerations at a moment in time. Aggregated levels of order flow also exhibit positive serial correlation in order type, but appear to be non-stationary processes. Overall, changes in aggregated order flow have an order-type serial correlation that is close to zero at short aggregation intervals, but becomes increasingly negative at longer intervals. This implies a liquidity exhaustion–replenishment cycle. We find that small orders routed to the NYSE's floor auction process are sensitive to the quoted spread, but that small orders routed to the automatic execution system are not. Thus, in addition to foregoing price improvement, traders selecting the speed of automatic executions on the NYSE do so with little regard for the quoted cost of immediacy. As quoted depth increases, traders respond by competing on price via limit orders that undercut existing bid and ask prices. Limit orders are more likely and market sells are less likely late in the trading day. These results are helpful in understanding the order arrival process at the NYSE and have potential applications in academics and industry for optimizing order submission strategies.     

Correlated Trading and Returns

A German broker's clients place similar speculative trades and therefore tend to be on the same side of the market in a given stock during a given day, week, month, and quarter. Aggregate liquidity effects, short sale constraints, the systematic execution of limit orders (coordinated through price movements) or the correlated trading of other investors who pick off retail limit orders do not fully explain why retail investors trade similarly. Correlated market orders lead returns, presumably due to persistent speculative price pressure. Correlated limit orders also predict subsequent returns, consistent with executed limit orders being compensated for accommodating liquidity demands.     

Fluctuations and response in financial markets: the subtle nature of ‘random’ price changes

Using trades and quotes data from the Paris stock market, we show that the random walk nature of traded prices results from a very delilcated interplay between two opposite tendencies: long-range correlated market orders that lead to super-diffusion (or persistence), and mean revrting limit orders that lead to sub-diffusion (or anti-persistence). We define and study a model where the price, at any instant, is the result of the impact of all past trades, mediated by a non-constant ‘propagator’ in time that describes the response of the market to a single trade. Within this model, the market is shown to be, in a precise sense, at a critical point, where the price is purely diffusive and the average response function almost constant. We find empirically, and discuss theoretically, a fluctuation-response relation. We also discuss the fraction of truly informed market orders, that correctly anticipate short-term moves, and find that it is quite small.     

Price Discovery without Trading: Evidence from Limit Orders

We analyze the contribution to price discovery of market and limit orders by high‐frequency traders (HFTs) and non‐HFTs. While market orders have a larger individual price impact, limit orders are far more numerous. This results in price discovery occurring predominantly through limit orders. HFTs submit the bulk of limit orders and these limit orders provide most of the price discovery. Submissions of limit orders and their contribution to price discovery fall with volatility due to changes in HFTs’ behavior. Consistent with adverse selection arising from faster reactions to public information, HFTs’ informational advantage is partially explained by public information.     

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.     

Can order exposure be mandated?

In this paper, we examine whether the hidden portion of limit orders represents depth that would be revealed if traders were not allowed to hide it, and the associated market quality implications. Specifically, we examine the decisions by the Toronto Stock Exchange to first abolish the use of hidden limit orders in 1996, and then reintroduce them in 2002. We find that quoted depth does not change following either decision, suggesting that the hidden portion of orders represents depth that would otherwise not be exposed. Using confidential order data for the period following the reintroduction of hidden limit orders, we find that total inside depth increases. For both events, volume does not change and the usage of the limit order book increases if hidden limit orders are allowed. This suggests that if traders are required to expose their orders they will not exit the market, but instead will switch to using market orders. We also find evidence to suggest that informed traders use hidden limit orders to minimize price impact if the probability of non-execution is small.     

When spread bites fast – Volatility and wide bid-ask spread in a mixed high-frequency and low-frequency environment

This research focuses on the impact High-Frequency Trading has on price volatility when bid-ask spread is wide. The theoretical part introduces a set of equations and presents an Agent Based Model implemented via a computer-based simulation. The wide spread leads to the appearance of unusual phenomena caused by the relative speed difference between the fast and slow traders. The latter agents tend to quote limit orders that look irrational, as they are distant more than one tick from the top-of-book. The same relative speed difference causes slow traders to post market orders that execute at price worse than originally intended. Both these abnormal orders tend to increase local volatility. Other results found by the simulation are an increase in global volatility (computed both as the difference of maximum less minimum price and as standard deviation of price distribution) and in volatility at sub-second timescales. These occurrences penalise slower traders and affect market stability. All the results are consistent both under quiet and stressed market conditions. The results found are then compared with audit trail data to verify the soundness of theory against practice.     

Limit Orders, Depth, and Volatility: Evidence from the Stock Exchange of Hong Kong

We investigate the role of limit orders in the liquidity provision in a pure order-driven market. Results show that market depth rises subsequent to an increase in transitory volatility, and transitory volatility declines subsequent to an increase in market depth. We also examine how transitory volatility affects the mix between limit orders and market orders. When transitory volatility arises from the ask (bid) side, investors will submit more limit sell (buy) orders than market sell (buy) orders. This result is consistent with the existence of limit-order traders who enter the market and place orders when liquidity is needed.     

Ripples through markets: Inter-market impacts generated by large trades

This paper uses a sample of large trades executed on the London Stock Exchange's SEAQ-I market for European cross-traded firms to investigate their impact on home market prices when parallel markets suffer from information frictions. I find that (a) large London trades produce price impacts in home markets even though no timely information is published, (b) market makers appear to pre- and post-position their inventories by splitting orders across markets, and (c) the price discovery process across markets changes significantly around large trades with the foreign market making a significantly bigger contribution to price discovery at this time, even though information opaqueness exists.     

The submission of limit orders or market orders: The role of timing and information in the Reuters D2000-2 system

Recent work in the market microstructure literature suggests that the speed with which orders arrive in the market impacts traders' order submission decisions. In this study we use an asymmetric autoregressive conditional duration (ACD) model to empirically investigate the influence on the submission of limit and market orders of changes in the time between the past submissions of different types of orders, changes in the slope of the limit order book, and changes in price uncertainty. We find that the expected time between the arrivals of successive orders in the foreign exchange market depends on the previous type of order submitted and the slope on both sides of the order book. Price uncertainty (volatility) plays a secondary role after accounting for the impact of changes in the slope of the order book. Lastly, we find that there are fundamental changes in the level of information contained in the submission of orders at the opening and closing of markets.     

Effects of Price Regulations and Dark Pools on Financial Market Stability: An Investigation by Multiagent Simulations

We built an artificial market model and investigated the impact of large erroneous orders on financial market price formations. Comparing the case of consented large erroneous orders in the short term with that of continuous small erroneous orders in the long term, if amounts of orders are the same, we found that the orders induced almost the same price fall range. We also analysed effects of price variation limits for erroneous orders and found that price variation limits that employ a limitation term shorter than the time erroneous orders exist effectively prevent large price fluctuations. We also investigated effects of up-tick rules, adopting the trigger method that the Japan Financial Services Agency adopted in November 2013. We also investigated whether dark pools that never provide any order books stabilize markets or not using the model including one lit market, which provides all order books to investors, and one dark pool. We found that markets become more stable as the dark pool is increasingly used. We also found that using the dark pool more reduces the market impacts. However, if other investors’ usage rates of dark pools become too large, investors must use the dark pool more than other investors to avoid market impacts. When a tick size of a lit market is larger, dark pools are more useful to avoid market impacts. These results suggest that dark pools stabilize markets when the usage rate is under some threshold and negatively affect the market when the usage rate is over that threshold. Our simulation results suggest the threshold might be much larger than the usage rate in present real financial markets. This study is the first to discuss whether or not several concrete and actually adoptable regulations, including those that have never been employed (e.g. price variation limits with various parameters), could prevent large fluctuations of market prices, including those beyond our experience, using artificial market simulations, and to discuss quantitatively how spreading of dark pools beyond our experience could affect market price formations using the artificial market simulations. In short, this study is the first study to comprehensively discuss how regulations and financial systems beyond our experience could affect price formations using the artificial market simulations. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

Upstairs Market for Principal and Agency Trades: Analysis of Adverse Information and Price Effects

This paper directly tests the hypothesis that upstairs intermediation lowers adverse selection cost. We find upstairs market makers effectively screen out information-motivated orders and execute large liquidity-motivated orders at a lower cost than the downstairs market. Upstairs markets do not cannibalize or free ride off the downstairs market. In one-quarter of the trades, the upstairs market offers price improvement over the limit orders available in the consolidated limit order book. Trades are more likely to be executed upstairs at times when liquidity is lower in the downstairs market.