The dynamics of quote adjustments

Liquidity providers on the NYSE make faster quote adjustments towards equilibrium spreads and depths than they do on NASDAQ. Liquidity providers in both markets make faster spread and depth adjustments for stocks with more frequent trading, greater return volatility, higher prices, smaller market capitalizations, and smaller trade sizes. We find that stocks with greater information-based trading and in more competitive trading environments exhibit faster quote adjustments. The speed of quote adjustment is faster after decimalization in both markets. These results are robust and not driven by differences in stock attributes between the two markets or time periods. Overall, our results indicate that stock attributes, market structure, and tick size exert a significant impact on the speed of quote adjustment.     

Information-based trading,price impact of trades,and trade autocorrelation

In this study we show that both the price impact of trades and serial correlation in trade direction are positively and significantly related to the probability of information-based trading (PIN). The positive relation remains significant even after controlling for the effects of stock attributes. Higher trading activity (i.e., shorter intervals between trades) induces both larger price impact and stronger positive serial correlation in trade direction. The effect of time interval between trades on quote revision is stronger for stocks with higher PIN values. These results provide direct empirical support for the information models of trade and quote revision.     

Making a Market with Spreads and Depths

Abstract:   In this paper we study the quote revision behavior of NASDAQ market makers by analyzing inter‐temporal changes in their spread and depth quotes. Using individual dealer quote and trade data for a sample of 2,319 stocks, we find that NASDAQ dealers make more frequent revisions in depths than in spreads and the extent of liquidity management is greater for stocks of smaller companies, lower‐priced stocks, and stocks with larger trade sizes and fewer number of transactions. We show that intraday variation in the number of quote revisions follows the U‐shaped pattern, indicating that the extent of liquidity management is greater during the early and late hours of trading than during midday.     

Dealer attention,the speed of quote adjustment to information,and net dealer revenue

Using trade and quote data from the NYSE, we examine the relation between dealer attention, dealer revenue, and the probability of informed trade. We find that dealer revenue net of losses to better-informed traders in NYSE stocks is positively related to the speed at which quotes adjust to full information levels. The speed of quote adjustment is faster for stocks with greater dealer attention, as measured by a stock’s relative prominence at its post and panel location on the NYSE floor. The level of dealer attention in turn is positively related to a stock’s probability of information-based trading. The results are consistent with a theoretical model we derive in which dealers trade multiple securities and must optimally allocate their limited attention to monitoring order flow to minimize losses to better-informed traders.     

Intraday asymmetric liquidity and asymmetric volatility in FTSE-100 futures market

In this study, we use both quote and trade data for the FTSE-100 futures for 2001–2004 in order to examine asymmetric volatility in the context of extreme sells. We define extreme sells as ask quotes that involve large percentages of total depth, selling orders executed at prices much closer to bids than to asking prices, and consecutive sell-initiated trades. Sell trades tend to demand higher liquidity than buys, while extreme trading conditions demand more liquidity than non-extreme ones. In extreme sells, liquidity demand surpasses supply. We show that asymmetric liquidity (quote demand vs. supply) better explains the asymmetric volatility observed in high-frequency data than trade information does. Ask-depth share plays a dominant role in asymmetric volatility, while order flow (sell-initiated volume share) makes a far smaller contribution.     

Informed and uninformed trading on the Australian dollar

Using a high-frequency data set of the spot Australian/US dollar, this study examines the distribution of quotes, spreads, and returns across the trading day. By identifying the direction of trade and the subsequent quote returns from contributing banks, the segmented nature of the market into market-makers and informed and uninformed traders is investigated. The results suggest that the economic gain possible from private information is maximised over 2 to 5 quotes and is rapidly eroded by 20 quotes (about 2 min later during busy trading times) as other new information enters the market. Also, the analysis is revealing of discontinuities in trading and the volatility of pricing across the trading day.     

Short selling,margin trading,and the incorporation of new information into prices

Utilizing daily data on Chinese stocks' short selling and margin trading activities and intraday stock trade and quote data, we find a positive association between the degree of information efficiency of stock prices and the intensity of short selling and margin trading. Short selling (margin buying) escalates during the 5 days immediately before significant negative (positive) information events, which suggests short sellers (margin buyers) anticipate forthcoming news. Using the adverse selection component of the bid–ask spread as a proxy, we find that short selling and margin trading are associated with an improved information environment. Taken together, our empirical evidence supports the conjecture that short selling and margin trading in the Chinese market help stock prices incorporate new information more efficiently. Utilizing the unique Chinese regulation, we also examine the role of brokerages authorized for such trading and document a non-linear relation between pricing efficiency and the number of authorized brokerages.     

Quote Competitiveness, 100‐share Quotes,and Decimalization

The finance literature documents a significant post‐decimalization decline in intermarket quote competitiveness. We show that this result is due primarily to the decline in 100‐share NBBO‐matching quotes posted by NASDAQ. After decimalization, such quotes decline by more than 90%. At that time, the intermarket trading system (ITS) regards 100‐share quotes as meaningless; these quotes are exempt from the trade‐through rule and are generally ignored by ITS participants. We therefore argue that the major decline in quote competitiveness observed by previous research is largely nominal, and that decimalization had a much lesser impact on quote competitiveness than previously believed.     

Informed trading and order type

Each trader must choose between a limit order, a market order, or using a floor broker. We hypothesize that informed investors will: (1) concentrate their trading in floor broker orders and (2) sometimes trade patiently. Consistent with our hypotheses, empirical results suggest that most informed trading occurs through orders executed by floor brokers and that informed floor brokers are sometimes patient. Regardless of their patience, however, quote revisions following trade executions are consistent with the hypothesis that markets recognize that floor traders are more likely to be informed than other traders. As a result, informed trading moves equilibrium security values.     

Evaluation of the biases in execution cost estimation using trade and quote data

We use order data to assess the accuracy of execution cost estimation with trade and quote data. For our sample, estimates of the effective spread overstate actual execution costs by up to 17%. The biases result from errors in the inference of the trade direction and errors in the assignment of the benchmark quote. We find the accuracy of two popular trade direction algorithms improve marginally when trades are not lagged 5 seconds. Evaluation of the biases in execution cost measurement reveal the Ellis et al. (Journal of Financial and Quantitative Analysis (2000) 529) trade direction algorithm, combined with assigning benchmark quotes contemporaneous with trades, provides the least amount of bias. In general, biases are lower for relative effective spread estimates than effective spread estimates.     

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.     

Impact of MiFID II tick-size regime on equity markets—Evidence from the LSE

This study investigates the impact of MiFID II on the London Stock Exchange. We find that a tick-size reduction leads to lower bid–ask spreads, lower trade values, reduced cost of trading at and beyond the best bid-offer, an acceleration of quote updates, an increase in aggressive trades and a reduction in price impact. Increased tick size widens spreads and increases trading costs. Step functions reveal that liquidity adjusts opposite to the tick change. To determine if impacts are proportional, we identify potential functions that predict cost changes with tick updates, implying that traders adjust their trade sizes according to the new tick levels.     

Measuring the impact of option market activity on the stock market: Bivariate point process models of stock and option transactions

I apply the bivariate Autoregressive Conditional Duration model of Engle and Lunde [2003. Trade and quotes: a bivariate point process. Journal of Financial Econometrics 1, 159–188] to stock and option market transactions. The first model uses option trades and stock trades. Shocks to option trade/option trade durations have a significant impact on option trade/stock trade durations. Higher implied volatility, larger stock and option market order imbalances, larger stock trades, larger spreads, smaller depths in the stock market and faster trading in the stock and option markets are all associated with faster trading in both markets. In the second model, option trade/option trade timing leads option trade/stock quote timing and several information-related stock and option market covariates impact the expected inter-market event durations.     

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.     

Short sales and speed of price adjustment: Evidence from the Hong Kong stock market

We present empirical evidence that short sales contribute to market efficiency by increasing the speed of price adjustment to not only private/public firm-specific information but also market-wide information. Shortable stocks are characterized by weaker trade continuity and stronger quote reversals. They adjust faster to new information than non-shortable counterparts. These findings remain robust even in an “up” market condition in which short sales are not binding. The amount of information incorporated in each trade is also significantly higher for shortable than non-shortable stocks in both “up” and “down” market conditions. After controlling for firm size, trading volume, liquidity, price and option trading, short sales stand out as one of the significant factors that speed up the price adjustment.     

Adverse selection costs,trading activity and price discovery in the NYSE: An empirical analysis

This paper studies the role that trading activity plays in the price discovery process of a NYSE-listed stock. We measure the expected information content of each trade by estimating its permanent price impact. It depends on observable trade features and market conditions. We also estimate the time required for quotes to incorporate all the information content of a particular trade. Our results show that price discovery is faster after risky trades and also at the extreme intervals of the session. The quote adjustment to trade-related shocks is progressive and this causes risk persistency and unusual short-term market conditions.     

Trades and Quotes: A Bivariate Point Process

This article formulates a bivariate point process to jointlyanalyze trade and quote arrivals. In microstructure models,trades may reveal private information that is then incorporatedinto new price quotes. This article examines the speed of thisinformation flow and the circumstances that govern it. A jointlikelihood function for trade and quote arrivals is specifiedin a way that recognizes that an intervening trade sometimescensors the time between a trade and the subsequent quote. Modelsof trades and quotes are estimated for eight stocks using Tradeand Quote database (TAQ) data. The essential finding for thearrival of price quotes is that information flow variables,such as high trade arrival rates, large volume per trade, andwide bid–ask spreads, all predict more rapid price revisions.This means prices respond more quickly to trades when informationis flowing so that the price impacts of trades and ultimatelythe volatility of prices are high in such circumstances.     

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.     

Does Algorithmic Trading Improve Liquidity?

Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock Exchange automated quote dissemination in 2003, and we use this change in market structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade‐related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes.     

Autocorrelation and partial price adjustment

Stock return autocorrelation contains spurious components—the nonsynchronous trading effect (NT) and bid–ask bounce (BAB)—and genuine components—partial price adjustment (PPA) and time-varying risk premia (TVRP). We identify a portion that can unambiguously be attributed to PPA, using three key ideas: theoretically signing and/or bounding the components; computing returns over disjoint subperiods separated by a trade to eliminate NT and greatly reduce BAB; and dividing the data period into disjoint subperiods to obtain independence for statistical power. Analyzing daily individual and portfolio return autocorrelations in sixteen years of NYSE intraday transaction data, we find compelling evidence that PPA is a major source of the autocorrelation.