Undisclosed orders and optimal submission strategies in a limit order market

Reserve orders enable traders to hide a portion of their orders and now appear in most electronic limit order markets. This paper outlines a theory to determine an optimal submission strategy in a limit order book, in which traders choose among limit, market, and reserve orders and simultaneously set price, quantity, and exposure. We show that reserve orders help traders compete for the provision of liquidity and reduce the friction generated by exposure costs. Therefore, total gains from trade increase. Large traders always benefit from reserve orders, whereas small traders benefit only when the tick size is large.     

Why Do Traders Split Orders?

We examine factors that influence decisions by U.S. equity traders to execute a string of orders, in the same stock, in the same direction, around the same time. Order splitting is more likely to occur when traders submit larger‐size orders and when market depth and trading activity are lower. Order splitters demand liquidity more and pay higher trading costs, but their overall performance is better. When controlling for execution time, split orders are more informative than single orders. Our results suggest that order splitting arises from a variety of factors, including informational differences, order and trader characteristics, and market conditions.     

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.     

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.     

Is faster or slower trading better? An examination of order type execution speed and costs

We examine order type execution speed and costs for US equity traders. Marketable orders that execute slower exhibit lower execution costs. Those who remove liquidity faster and pay higher trading costs transact in smaller size, spread trading across more venues, take more liquidity, and are better informed. Nonmarketable limit orders that execute slower exhibit greater adverse selection; and larger, uninformed traders who concentrate their trading in fewer venues submit them. Our findings suggest that slowing down the trading process, when faster options exist, can benefit certain market participants who seek to cross the bid–ask spread.     

Hide-and-Seek in the Market: Placing and Detecting Hidden Orders

This paper investigates why traders hide their orders and howother traders respond to hidden depth. Using a logit model,we provide empirical findings suggesting that traders use hiddenorders to manage both exposure risk and picking off risk. Usingprobit models, we show that hidden depth increases order aggressiveness.Our interpretation of this empirical evidence is threefold.First, hidden depth detection is possible and frequent. Second,when traders detecthidden volume at the best opposite uote,they strategically adjust their order submission to seize theopportunity for depth improvement. Third, traders' responsewhen hidden depth is detected suggests either that they do notassociate hidden orders with informed trading or that the riskof trading with an informed trader is widely offset by the opportunityfor depth improvement.     

The Determinants of Execution Costs in Short‐Term Money Markets

Prior research attributes the observed negative relation between execution costs and trade size in opaque markets to two factors—information asymmetry and broker‐client relationships. We provide evidence that a trader's ex ante transaction price information and the relationship traders have with their brokers are both significant determinants of a trader's execution costs in an opaque market; however, traders who establish strong relationships with their brokers will achieve a greater reduction in execution costs than traders with ex ante transaction price information. We also find evidence that trade size has little explanatory power after controlling for a trader's ex ante transaction price information and broker‐client relationships.     

Stealth trading: The case of the Tokyo Stock Exchange

The stealth trading hypothesis asserts that informed traders trade strategically by breaking up their orders so as to more easily hide among the liquidity traders. Using data for the Tokyo Stock Exchange (TSE), a pure order-driven market, we find evidence that price changes are driven by small- and medium-size trades, with small trades making the greatest contribution to price change relative to their contribution to trading volume. We also find that large trades explain a greater portion of the cumulative price change on high volatility days. Hence, our results support the stealth trading hypothesis for the TSE.     

A dynamic limit order market with fast and slow traders

This paper considers the role of high-frequency trading in a dynamic limit order market. Fast traders? ability to revise their quotes quickly after news arrivals helps to reduce the inefficiency that is rooted in the risk of being picked off, which increases trade. However, their presence induces slow traders to strategically submit limit orders with a lower execution probability, thereby reducing trade. Because speed is a source of market power, it enables fast traders to extract rents from other market participants and triggers a costly arms race that reduces social welfare. The model generates a number of testable implications concerning the effects of high-frequency trading in limit order markets.     

EX‐DIVIDEND DAY PRICE BEHAVIOR OF EXCHANGE‐TRADED FUNDS

We document that for exchange‐traded funds (ETFs), the price falls on average by the dividend amounts on the ex‐dividend day, and there are significantly positive abnormal volumes. This is because trading in ETFs entails lower transaction costs and lower risk than trading in equity closed‐end funds (CEFs) and individual stocks. Similar results are also found for equity CEFs. However, regression analyses indicate that transaction costs and risk are indeed negligible for ETFs but not for equity CEFs and that risk remains important for a sample of stocks matched based on transaction costs. Overall, the results support the short‐term traders hypothesis.     

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.     

The Impact of Pennies on the Market Quality of the Toronto Stock Exchange

Using detailed order flow data from the Toronto Stock Exchange, this paper finds no evidence that a smaller tick size lessens market liquidity for either small or large traders. Rather, there is evidence of lower trading costs, faster time to order execution, and greater price continuity. Consistent with a penny tick allowing a finer pricing grid search, there is an increase in the number of Change Former Orders and cancellations.     

PIP Transactions,Price Improvement,Informed Trades and Order Execution Quality

This study focuses on innovations in order execution processes within the context of the Boston Option Exchange (BOX). More specifically, it examines the impact of the Price Improvement Process (PIP) on options quoted, effective and realised proportional spreads. We consider the PIP as a mechanism that allows the market maker to ‘internalise’ the transaction. We show that PIP transactions are associated with wider bid/ask proportional quoted spreads than non‐PIP transactions, in spite of the temporary narrowing of the effective proportional spread during PIP. We identify informed traders by focusing on the direction of trade. Using an original data set, we show that PIP transactions follow signals in the form of buy/sell orders by informed traders. We also show that PIP is a mechanism that allows the market maker to internalise a position in the same direction as that of the informed trader. We conclude that PIP does not improve the efficiency of the market but simply allows the market maker to benefit at the expense of uninformed traders.     

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 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.     

Trader see,trader do: How do (small) FX traders react to large counterparties’ trades?

We show that information about the counterparty of a trade affects the future trading decisions of individual traders. The effect is such that traders tend to reverse their order flow in line with the better-informed counterparties. Informed traders primarily incorporate their own private as well as publicly available information into prices, whereas uninformed traders mainly magnify the effect of the informed. This pattern of interaction among traders extends to different order types: traders treat their own and others’ market orders as more informative than limit orders.     

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.     

Speed,distance, and electronic trading: New evidence on why location matters

We examine the execution quality of electronic stock traders who are geographically dispersed throughout the United States. Traders who are located near market central computers in the New York City area experience faster order execution. Moreover, the time to execute orders rises as a trader’s actual distance (mileage) to NYC widens. In electronic market settings, data transfer limitations and transmission slowdowns result in geographically-dispersed electronic traders having different access to trading speed. We find that speed differences are costly to traders and that speed-advantaged traders engage in strategies that are more conducive to speed.     

Order submission strategies,liquidity supply,and trading in pennies on the New York Stock Exchange

We use NYSE system order data to conduct a controlled experiment examining changes in trader behavior, displayed liquidity supply, and execution quality around the reduction in the minimum price variation to $0.01. Although traders do not substantially reduce their use of traditional limit orders in favor of market orders or non-displayed orders, they do decrease limit order size and cancel limit orders more frequently after decimals than before. These changes in order submission strategy appear to result in less displayed liquidity throughout the limit order book more than 15 cents from the quote midpoint. This reduction in displayed liquidity, however, does not manifest itself in poor execution quality. Even for large system orders, traditional execution quality is not worse with decimals than with fractions.     

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.