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.     

Technology and liquidity provision: The blurring of traditional definitions

Limit orders are usually viewed as patiently supplying liquidity. We investigate the trading of one hundred Nasdaq-listed stocks on INET, a limit order book. In contrast to the usual view, we find that over one-third of nonmarketable limit orders are cancelled within two seconds. We investigate the role these “fleeting orders” play in the market and test specific hypotheses about their uses. We find evidence consistent with dynamic trading strategies whereby traders chase market prices or search for latent liquidity. We show that fleeting orders are a relatively recent phenomenon, and suggest that they have arisen from a combination of factors that includes improved technology, an active trading culture, market fragmentation, and an increasing utilization of latent liquidity.     

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.     

Anonymity,liquidity and fragmentation

We examine the effects of the removal of broker identifiers from the central limit order book of the Australian Stock Exchange. We find that spreads and order aggressiveness decline, and order book depth increases, with the introduction of anonymous trading. This is consistent with the hypothesis that limit order traders are more willing to expose their orders when they can do so anonymously. Anonymous markets attract order flow from non-anonymous substitute markets, but this effect is only seen in large stocks. Our results suggest that exchanges operating in fragmented markets should consider anonymous trading to improve price competition and liquidity, although some of these benefits may be significant only if the stocks are sufficiently large and liquid.     

Competition from the limit order book and NYSE spreads

Using the New York Stock Exchange’s (NYSE) trades, orders, reports, and quotes data, we investigate whether competition from the limit order book reduces NYSE spreads. We replicate and extend the research design of McInish and Wood (1992). We find that there is a statistically significant reduction in NYSE spreads as a result of limit order competition and that the reduction is significantly greater in the morning than during the remainder of the trading day. We also examine the day-of-the-week differences in limit order placements and find no day of the week pattern in the number or the size of limit order placements.     

Limit Order Trading

We analyze the rationale for limit order trading. Use of limit orders involves two risks: 1) an adverse information event can trigger an undesirable execution, and 2) favorable news can result in a desirable execution not being obtained. On the other hand, a paucity of limit orders can result in accentuated short-term price fluctuations that compensate a limit order trader. Our empirical tests suggest that trading via limit orders dominates trading via market orders for market participants with relatively well balanced portfolios, and that placing a network of buy and sell limit orders as a pure trading strategy is profitable.     

Order Flow and Liquidity around NYSE Trading Halts

We study order flow and liquidity around NYSE trading halts. We find that market and limit order submissions and cancellations increase significantly during trading halts, that a large proportion of the limit order book at the reopen is composed of orders submitted during the halt, and that the market-clearing price at the reopen is a good predictor of future prices. Depth near the quotes is unusually low around trading halts, though specialists and/or floor traders appear to provide additional liquidity at these times. Finally, specialists appear to 'spread the quote' prior to imbalance halts to convey information to market participants.     

On the origin of power-law tails in price fluctuations

We analyse large stock price changes of more than five standard deviations for (i) TAQ data for the year 1997 and (ii) order book data from the Island ECN for the year 2002. We argue that a large trading volume alone is not a sufficient explanation for large price changes. Instead, we find that a low density of limit orders in the order book, i.e. a small liquidity, is a necessary prerequisite for the occurrence of extreme price fluctuations. Taking into account both order flow and liquidity, large stock price fluctuations can be explained quantitatively.     

Order aggressiveness and quantity: How are they determined in a limit order market?

Dealers trading in a limit order market must choose both the order aggressiveness and the quantity for their orders. Since little research has considered how dealers make this trade-off, we empirically investigate how dealers jointly make these decisions in the foreign exchange market using a unique simultaneous equations model. Our model uses an ordered probit model to account for the discrete nature of order aggressiveness and a censored regression model to capture the quantity decision recognizing the clustering of orders at the smallest available quantity, $1 million. Using two currency pairs with very different trading characteristics, we find evidence of a trade-off between order aggressiveness and quantity. We also find a significant role being played by factors related to the levels of information asymmetry and liquidity in the dealers’ choices of both the order aggressiveness and quantity.     

Lifting the Veil: An Analysis of Pre-trade Transparency at the NYSE

We study pre‐trade transparency by looking at the introduction of NYSE's OpenBook service that provides limit‐order book information to traders off the exchange floor. We find that traders attempt to manage limit‐order exposure: They submit smaller orders and cancel orders faster. Specialists' participation rate and the depth they add to the quote decline. Liquidity increases in that the price impact of orders declines, and we find some improvement in the informational efficiency of prices. These results suggest that an increase in pre‐trade transparency affects investors' trading strategies and can improve certain dimensions of market quality.     

Long-run returns following open market share repurchases

Past studies find abnormal returns to buying after repurchase program announcements. We analyze the profitability of trading after both program announcements and individual repurchase trade publication using different trading strategies – market and limit orders. The analysis of trades is possible because of a unique Canadian data set. The highest abnormal returns are earned by companies on their own repurchase trades which benefits the non-tendering shareholders. For the public investor, we find no strategies that, in practice, would earn abnormal returns to buying after program announcements. However, there is qualified evidence of abnormal returns to a limit order strategy following publication of individual repurchase trades.     

Do Limit Orders Alter Inferences about Investor Performance and Behavior?

Individual investors lose money around earnings announcements, experience poor posttrade returns, exhibit the disposition effect, and make contrarian trades. Using simulations and trading records of all individual investors in Finland, I find that these trading patterns can be explained in large part by investors' use of limit orders. These patterns arise mechanically because limit orders are price‐contingent and suffer from adverse selection. Reverse causality from behavioral biases to order choices does not appear to explain my findings. I propose a simple method for measuring a data set's susceptibility to this limit order effect.     

How do individual investors trade?

This paper examines how high-frequency trading decisions of individual investors are influenced by past price changes. Specifically, we address the question as to whether decisions to open or close a position are different when investors already hold a position compared with when they do not. Based on a unique data set from an electronic foreign exchange trading platform, OANDA FXTrade, we find that investors’ future order flow is (significantly) driven by past price movements and that these predictive patterns last up to several hours. This observation clearly shows that for high-frequency trading, investors rely on previous price movements in making future investment decisions. We provide clear evidence that market and limit orders flows are much more predictable if those orders are submitted to close an existing position than if they are used to open one. We interpret this finding as evidence for the existence of a monitoring effect, which has implications for theoretical market microstructure models and behavioral finance phenomena, such as the endowment effect.     

Noise trading and the price formation process

This study proposes the dispersion in daily net initiated order flow across brokers as a proxy for the level of noise trading in a stock, and applies this proxy to test some basic implications of market microstructure theory. We use data from the Australian Stock Exchange, a computerized limit order market where price, quantity, and broker identity for each incoming order are shown on broker screens. We find daily movements in our noise measure are positively associated with trading volume and market depth, and negatively related to the bid-ask spread. We find monthly movements in our noise measure are negatively associated with the probability of informed trading, and positively correlated with the arrival rate of uninformed traders. We also find the sensitivity of stock prices to net initiated order flow decreases in the level of noise trading. In addition we find that, after controlling for noise trading, the sensitivity of stock prices to net initiated order flow is significantly greater on Mondays. These empirical results consistently support the implications of various models of market microstructure, suggesting that our proxy provides useful information as a daily measure of noise trading.     

Whose trades convey information? Evidence from a cross-section of traders

This paper contributes empirically to our understanding of informed traders. It analyzes traders’ characteristics in a foreign exchange electronic limit order market via anonymous trader identities. We use six indicators of informed trading in a cross-sectional multivariate approach to identify traders with high price impact. More information is conveyed by those traders’ trades which—simultaneously—use medium-sized orders (practice stealth trading), have large trading volume, are located in a financial center, trade early in the trading session, at times of wide spreads and when the order book is thin.     

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.     

Individuals’ Trading Prior to Earnings Announcements

This paper investigates the relationship between individuals’ net trading and stock price movements before and after annual earnings announcements for the Taiwan Stock Exchange. We conduct an event study on the effects of pre‐event individual trade imbalances on pre‐ and post‐announcement abnormal returns. With a unique and comprehensive dataset, we accurately classify executed orders by aggressiveness of order price. The evidence indicates that while individuals, as a group, are not informed about impending earnings announcements, individuals who place aggressive orders are informed as their net trading coincides with contemporaneous and future stock returns. Aggressive individuals lose their edge during the financial crisis. More importantly, the advantage (disadvantage) for individuals who adopt aggressive (passive) orders weakens when foreign institutions own concentrated equity in firms. We also find that net individual trading contains information about abnormal returns that either past returns or volume does not subsume. Controlling for past returns, trading volume and volatility, or using an alternative measure of net individual trading does not change our conclusions.     

A detailed investigation of the disposition effect and individual trading behavior: a panel survival approach

This article uses a panel survival approach to analyze the trading behavior of foreign exchange traders. We concentrate on a detailed characterization of the shape of the disposition effect over the entire profit and loss regions. In doing so, we investigate the influence of a number of trading characteristics on the impact of the disposition effect. These trading characteristics include: special limit order strategies, trading success, size and the experience of our investors. Our main findings are that (i) the disposition effect has a nonlinear shape. For small profits and losses we find an inverted disposition effect, while for larger ones, the usual positive disposition effect emerges. (ii) The inverted disposition effect is driven to a great extend by patient and cautious investors closing their positions with special limit orders (take-profit and stop-loss). The normal positive disposition effect is found to be intensified for impatient investors closing their positions actively with market orders. (iii) We show that unsuccessful investors reveal a stronger inverse disposition effect. (iv) Evidence that bigger investors are less prone to the disposition effect than smaller investors is also found.     

A simulation analysis of the microstructure of double auction markets

Abstract

We introduce an order-driven market model with heterogeneous agents trading via a central order matching mechanism. Traders set bids and asks and post market or limit orders according to exogenously fixed rules. We investigate how different trading strategies may affect the dynamics of price, bid-ask spreads, trading volume and volatility. We also analyse how some features of market design, such as tick size and order lifetime, affect market liquidity. The model is able to reproduce many of the complex phenomena observed in real stock markets.     

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.