Limit order revisions

This paper empirically examines limit order revisions and cancellations which contribute to a significant portion of the order activity in many order-driven markets. We document that limit orders are more likely to be revised or cancelled if they are large and near the bid-ask quote. We show that order revisions generate net economic benefits to traders. Our evidence shows strong links between these activities and limit order submission risk using bid-ask spread, volatility and post-event return as proxies. We also find that these activities are less intense when the opportunity cost to monitor a stock is high, such as during lunch hours or when stock volume relative to the entire market is low.     

The Effect of Market Structure on the Incentives to Quote Aggressively: An Empirical Study of Nasdaq Market Makers

We use data on Nasdaq stocks to study arguments that preferencing reduces incentives to quote competitively. We examine a market maker's volume as a function of various measures of quoting aggressiveness. We find that more aggressive quoting does indeed result in more business. We also examine the relation between volume and quote aggressiveness as a function of the competitiveness. We find that in less (more) competitive markets, increased quote aggressiveness has a smaller (larger) impact on market share. We argue that preferencing arrangements could be more harmful to public investors in markets where competition is weak.     

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.     

Tick test accuracy in foreign exchange ECN markets

Recent computer quoting activity has increased the allure of the tick test because the quote rule and its variants require matching asynchronous trade and quote records. We find tick test accuracy of 1.2 million forex trades is about 67% which falls to 63% for zerotick trades (half the sample). Accuracy declines as quoted spreads decrease and as time to the previous trade increases. We observe extreme asymmetry for midquote changes, where buyer accuracy is 96% (27%) for up (down) changes, respectively. The quote rule is about 77% accurate. The group tick test is superior to the bulk volume classification method.     

Can quote competition reduce preferenced trading? A reexamination of the SEC’s 1997 order handling rules

In 1997, the SEC implemented the new order handling rules (OHRs) on the NASDAQ. We observe that some uncompetitive positions gained market share without improving quote competitiveness after the implementation of the OHRs. Also observed is a significant decline in the sensitivity of trading volume to quote competitiveness, indicating lower incentive for NASDAQ dealers to engage in quote competition in the post‐OHR regime. We find that positions that gained trading volume without improving quote competitiveness were less competitive and were more closely associated with stocks showing low information asymmetry, which suggests that preferenced trading might be responsible for the decline in the trading volume sensitivity. Examining entries and exits around the periods of adopting OHRs, we observe net entry of uncompetitive positions and net exit of competitive positions, which indicates that preferenced trading crowded out quote competition subsequent to the OHRs. Our findings suggest that forcing intense quote competition alone produced an unwanted effect that preferencing emerged as a more attractive alternative to quote competition.     

The optimal bid-ask price strategies of high-frequency trading and the effect on market liquidity

We propose a model for determining the optimal bid-ask spread strategy by a high-frequency trader (HFT) who has an informational advantage and receives information about the true value of a security. We employ an information cost function that includes volatility and the volume of the asset. Subsequently, we characterize the optimal bid-ask price strategies and obtain a stable bid-ask spread. We assume that orders submitted by low-frequency traders (LFTs) and news events arrive at the market with Poisson processes. Additionally, our model supports the trading of the two-sided quote in one period. We find that more LFTs and a higher exchange latency both hurt market liquidity. The HFT prefers to choose a two-sided quote to gain more profits while cautiously chooses a one-sided quote during times of high volatility. The model generates some testable implications with supporting empirical evidence from the NASDAQ-OMX Nordic Market.     

Market Quote and Spread Component Cost Behavior Around Trading Halts for Stocks Interlisted on the Montreal and Toronto Stock Exchanges

We use intraday quotes and transactions on halted securities that interlisted on the Toronto Stock Exchange and Montreal Exchange to decompose the spreads and examine quote depths. Our results show that order‐processing costs differ for trading halts at the open compared to halts during the rest of the trading day. We find that the adverse‐selection cost component of the spread is higher around trading halts and highest at the trading halt. We also find that print‐media articles that appear within the four‐day window centered on the halt have no impact on the time‐series behavior of the spread cost.     

Short-Sale Restrictions and Price Clustering: Evidence from SEC Rule 201

We provide a novel test of information-based theories of price clustering by examining trade, order, and the National Best Bid and Offer (NBBO) quote price clustering during periods when information is removed from the market. We use a natural experiment of short-sale restrictions resulting from Securities and Exchange Commission (SEC) Rule 201 to more effectively determine the impact of information on price clustering. We find evidence of increased price clustering for trades, orders, and NBBO prices during short-sale restrictions. Overall, our findings indicate that short-sale restrictions harm the price discovery process and lead to a reduction in market efficiency.     

A Unifying Microstructure Framework for Modeling Intraday and Interday Asset Pricing Dynamics: the Case of Exchange Rates

A model of the dynamics of intradaily exchange rates is presented. The current Over‐The‐Counter (OTC) exchange rate is the quote of the quoting bank.Two polar cases are considered: (i) If each bank is able to observe the noises relative to the orders of its own clients, then the OTC exchange rate is shown to obey a random walk with a constant conditional variance. (ii) If each bank is not able to observe the noises relative to the orders of its own clients, the OTC exchange rate is no more a random walk and conditional heteroskedasticity appears.
     

Order Imbalance on Ex-Dividend Days

In this article we test an alternative to the tax‐based explanation for why prices decline on ex‐dividend days by an amount less than the dividend. We examine whether there is order imbalance on cum‐ and ex‐dividend days. We find that on ex‐dividend days, there are more buys than sells in the number of orders, but not in the number of shares ordered, and the imbalance in the number of orders is limited to small orders. We find that the difference between the dividend amount and the ex‐dividend‐day price drop is significantly related to the magnitude of the buy‐sell order imbalance. We find no order imbalance on cum‐dividend days in either the number of orders or the number of shares ordered.     

More on a statistical analysis of log-periodic precursors to financial crashes

In this paper, we extend the existing empirical evidence on the relationship between the state of the limit order book (LOB) and order choice. Our contribution is twofold: first, we propose a sequential ordered probit (SOP) model which allows studying patient and impatient traders’ choices separately; second, we consider two pieces of LOB information, the best quotes and the book beyond the best quotes. We find that both pieces of LOB information explain the degree of patience of an incoming trader and, afterwards, its order choice. Nonetheless, the best quotes concentrate most of the explanatory power of the LOB. The shape of the book beyond the best quotes is crucial in explaining the aggressiveness of patient (limit order) traders, while impatient (market order) traders base their decisions primarily on the best quotes. Patient traders’ choices depend more on the state of the LOB on the same side of the market, while impatient traders mostly look at the state of the LOB on the opposite side. The aggressiveness of both types of traders augments with the inside spread. However, patient (impatient) traders submit more (less) aggressive limit (market) orders when the depth of the own (opposite) best quote and the length of the own (opposite) side of the book increase. We also find that higher depth away from the best ask (bid) quote may signal that this quote is ‘too low (high)’, causing incoming impatient buyers (sellers) to be more aggressive and incoming patient sellers (buyers) to be more conservative.     

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.     

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.     

Does high-frequency trading actually improve market liquidity? A comparative study for selected models and measures

The increasing volume of messages sent to the exchange by algorithmic traders stimulates a fierce debate among academics and practitioners on the impacts of high-frequency trading (HFT) on capital markets. By comparing a variety of regression models that associate various measures of market liquidity with measures of high-frequency activity on the same dataset, we find that for some models the increase in high-frequency activity improves market liquidity, but for others, we get the opposite effect. We indicate that this ambiguity does not depend only on the stock market or the data period, but also on the used HFT measure: the increase of high-frequency orders leads to lower market liquidity whereas the increase in high-frequency trades improves liquidity. We hypothesize that the observed decrease in market liquidity associated with an increasing level of high-frequency orders is caused by a rise in quote volatility.     

Commonality in Liquidity: Evidence from an Order-Driven Market Structure

Events such as the 1997 East Asian financial crisis indicate that individual firm liquidity is strongly influenced by marketwide factors. Previous market microstructure research, however, focuses almost exclusively on the firm‐specific attributes of liquidity. Our study follows the recent shift in emphasis toward commonality by examining systematic liquidity in an order‐driven market structure. Using data from the Stock Exchange of Hong Kong, we show that commonality in liquidity includes both market and industry components, and is pervasive across size‐sorted portfolios. We also find a significant market and industry component in individual firms' order flow. In contrast to quote‐driven results, we do not find a positive relation between firm size and sensitivity to changes in marketwide bid‐ask spreads.     

Price Clustering Asymmetries in Limit Order Flows

We explore the relation between limit order price clustering and price efficiency. We find that executed sell limit orders cluster more frequently on round increments than buy limit orders and that this asymmetry in clustering is consistent with the well‐documented asymmetry in price response to marketable orders. In addition, we find that the degree of clustering is positively related to volatility and that asymmetry in clustering depends on whether stock prices are rising or falling—sell limit orders cluster more frequently as prices are rising, although buy limit orders cluster more as prices are declining. Our results indicate that predictable patterns in limit order pricing contribute to short‐run deviations from price efficiency.     

Trader Anonymity, Price Formation and Liquidity

Using data from the Frankfurt Stock Exchange we analyze price formationand liquidity in a non-anonymous environment with similarities to thefloor of the NYSE. Our main hypothesis is that the non-anonymity allows the specialist to assess the probability that atrader trades on the basis of private information. He uses this knowledgeto price discriminate. This can be achieved by quoting a large spread and granting price improvement to traders deemed uninformed.Consistent with our hypothesis we find that price improvement reflects loweradverse selection costs but does not lead to a reduction in the specialist's profit. Further, the quote adjustmentfollowing transactions at the quoted bid or ask price is more pronounced than the quote adjustment aftertransactions at prices inside the spread. Our results indicate that anonymity comes at the cost ofhigher adverse selection risk.     

Specialists,Limit‐Order Traders,and the Components of the Bid‐Ask Spread

This study compares the components of the bid‐ask spread estimated from quotes that reflect the trading interest of specialists with those estimated from limit‐order quotes and all available quotes for a sample of New York Stock Exchange (NYSE) stocks. The results show that the adverse selection component of the spread estimated from specialist quotes is significantly smaller than the corresponding figures from limit‐order quotes and entire quotes. We interpret this as evidence that NYSE specialists transfer at least a part of adverse selection costs to outsiders through the discretionary use of limit orders. Our results show that the estimation/interpretation of the components of the spread using quote data that include both specialist and limit‐order interests is problematic.     

Wealth Transfer Effects of Analysts' Misleading Behavior

We investigate a sample of 50 firm‐events, identified in the Global Research Analysts Settlement, in which analysts were discovered to have acted misleadingly ex post. In this setting, analysts' incentives caused them to issue public disclosures that differed from their private beliefs. We document that these firms' institutional holdings decline significantly during the period in which the analysts issued misleading disclosures. During this period daily small‐size trades (a proxy for individual investors) are dominated by buy orders while daily large‐size trades (a proxy for institutional investors) are dominated by sell orders. Short interest increases during the event period, consistent with the idea that sophisticated investors are selling. Our estimates of investors' trading losses show that individual investors lost about two and a half times the amount lost by institutions. Overall, the results suggest a wealth transfer from individuals to institutions that is likely attributable to analysts' misleading behavior.