The competitive effects of US decimalization: Evidence from the US-listed Canadian stocks

This paper analyzes the impact of US decimalization on the Canadian stocks listed on the Toronto Stock Exchange (TSE) and either the New York Stock Exchange (NYSE) or National Association of Securities Dealers Automated Quotation System (Nasdaq) in the US. Using a sample of 126 firms, we find that the US trading of these stocks increases after decimalization, but this increase is not at the expense of TSE volume. Indeed, the TSE volume increases substantially for those securities that are traded on Nasdaq and increases marginally for those securities that are traded on the NYSE. Most of the increase in volume is in retail-sized trades. The bid–ask spreads and the quote depths decline on all exchanges, but by a greater amount in the US than in Canada. The depths on the NYSE decline from being above the TSE depths to well below the TSE depths. We also find that the decline in the TSE spread is directly related to the size of the firm and to the decline in the US spread, and is inversely related to the pre-decimalization ratio of spreads on the US exchange and the TSE. Overall, our results indicate that the US decimalization had the desired positive impact on trading in both the US and Canada, with a decrease in spreads and an increase in retail-sized trading.     

Information Disclosure and Stock Liquidity: Evidence from Borsa Italiana

This article examines the effect of increased corporate information disclosure on stock liquidity. Using the adoption of International Financial Reporting Standards (IFRS) in Italy as a natural experiment we extend previous work examining the effect on one measure of liquidity—bid‐ask spreads—to others, specifically depth and the price impact of transactions (or effective bid‐ask spreads). Consistent with previous research we find that bid‐ask spreads of stocks decline following the introduction of IFRS, which implies that stock liquidity increases for small traders. However, we also provide evidence that depth at the best quotes declines, which challenges the proposition that liquidity increases for large trades following an increase in disclosure. In additional tests, we find that effective bid‐ask spreads of block trades also decline following the introduction of IFRS. Overall, this evidence confirms that stock liquidity for both small and large trades increases following an increase in corporate information disclosure.     

Portfolio risk,adjustment costs,and security dealers' bid/ask spreads

This article notes that dealers' bid/ask spreads should vary directly with their costs of adjusting to inventory imbalances. Thus, well-diversified dealers are expected to quote lower bid/ask spreads on stocks with substantial total risk caused by undiversifiable risk. Furthermore, the effect of systematic risk on bid/ask spreads should be negligible if dealers are compensated for systematic risk by market returns. This article shows that, empirically, bid/ask spreads of OTC stocks are insensitive to the systematic risk of individual stocks—even when only stocks with stable betas are considered. Furthermore, bid/ask spreads are not sensitive to changes in market variance, as would be expected if systematic risk affected spreads. While unsystematic risk affects bid/ask spreads, its effect is pronounced for stocks traded by small, undiversified dealers. If stocks are only traded by large dealers with low diversification costs, unsystematic risk does not affect bid/ask spreads.     

A functional approach to the price impact of stock trades and the implied true price

We propose a functional approach to estimate the instantaneous price impact of a trade and to infer an implied true price. Our model expresses price impact as an S-shaped function of signed volume. It has two parameters, one for price impact and one for liquidity depth. The latter measures the differential impact of small and large trades. The price impact is instantaneous, that is, it occurs at the instant of trade execution. Our specification also permits the price impact of buys and sells to be asymmetric. We compute an implied true price from our model, and we find that it is closer than the quote midpoint to the unobservable true price. Our empirical analysis also shows that the effective spread, which is computed using the midpoint, has an upward bias, and that the implicit transaction costs may be lower than previous estimates.     

The Components of the Bid‐Ask Spread: the Case of the Athens Stock Exchange

We analyse the components of the bid‐ask spread in the Athens Stock Exchange (ASE), which was recently characterised as a developed market. For large and medium capitalisation stocks, we estimate the adverse selection and the order handling component of the spreads as well as the probability of a trade continuation on the same side of either the bid or the ask price, using the Madhavan et al. (1997) model. We extend it by incorporating the traded volume and we find that the adverse selection component exhibits U‐shape patterns, while the cost component pattern depends on the stock price. For high priced stocks, the usual U‐shape applies, while for low‐priced ones, it is an increasing function of time, mainly due to the order handling spread component. Furthermore, the expected price change and the liquidity adjustment to Value‐at‐Risk that is needed are higher in the low capitalisation stocks, while the most liquid stocks are the high priced ones. Moreover, by estimating the Madhavan et al. (1997) model for two distinct periods we explain why there are differences in the components of the bid‐ask spread.     

Order imbalance and liquidity supply: Evidence from the bubble burst of Nasdaq stocks

In this study we analyze the effect of order imbalance on the quotation behavior of Nasdaq market makers. We find that Nasdaq market makers use both price and quantity quotes when dealing with order imbalances. However, order imbalance affects only price movement, not spreads. We also find that Nasdaq market makers quote more shares and compete more intensively on bid-side (ask-side) when public sells (buys) exceed public buys (sells). These suggest that market makers increase liquidity supply when order imbalances exist. More interestingly, we show that both market conditions and price movements affect investors' trading behavior.     

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.     

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.     

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.     

Competition For Order Flow, Market Quality, And Price Discovery In The Nasdaq 100 Index Tracking Stock

We investigate competition for order flow, market quality, and price discovery in the Nasdaq 100 Index Tracking Stock (QQQ). The QQQ, an AMEX‐listed, exchange‐traded fund, is the most actively traded security in the U.S. equities market. On July 31, 2001, the NYSE began trading the QQQ, marking the first time it traded securities of companies it does not list. The greatest volume of trading takes place on electronic communication networks (ECNs), following by trading on the AMEX and the NYSE. Most of the block trades are executed on the AMEX, where the bid‐ask spreads are narrower. We find that ECNs contribute the most to the price‐discovery process. The spreads on all trading platforms have decreased and market quality and price discovery have improved since QQQ shares have traded on the NYSE.     

TRADING COSTS AND QUOTE CLUSTERING ON THE NYSE AND NASDAQ AFTER DECIMALIZATION

We examine execution costs and quote clustering on the New York Stock Exchange (NYSE) and NASDAQ using 517 matching pairs of stocks after decimalization. We find that the mean spread of NASDAQ stocks is greater than the mean spread of NYSE stocks when spreads are equally weighted across stocks, and the difference is greater for smaller stocks. In contrast, the mean NASDAQ spread is narrower than the mean NYSE spread when spreads are volume weighted, and the difference is statistically significant for large stocks. Both NYSE and NASDAQ stocks exhibit high degrees of quote clustering on nickels and dimes, and quote clustering has a significant effect on spreads in both markets.     

Daily institutional trades and stock price volatility in a retail investor dominated emerging market

We examine the short-run dynamic relation between daily institutional trading and stock price volatility in a retail investor-dominated emerging market. We find a significantly negative relation between volatility and institutional net trading that is mainly due to the unexpected institutional trading. The price volatility–institutional trade relation differs for institutional buys and institutional sells, and for small and large stocks. Institutional investors herd-trade in large stocks, but do not systematically engage in positive-feedback trading. We argue that the net impact of informational and noninformational institutional trades determines the relation between volatility and institutional trading, and that the relation is negative when informational trading by institutions prevails.     

Trade Transparency and the London Stock Exchange

On the London Stock Exchange the publication of large trades is delayed to give the market-maker concerned time to unwind the change in their inventory. Using trade and quotations data on 42 stocks for two years, a number of different effects are investigated. These include the association between trade size and the traded bid-ask spread, the inventory control policies of individual market-makers around large trades, the size and speed of the price impact of large trades whose publication is delayed, and the effects of delayed publication on the volume and spreads of the traded equity options market.     

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.     

Intraday Patterns in the Cross‐section of Stock Returns

Motivated by the literature on investment flows and optimal trading, we examine intraday predictability in the cross‐section of stock returns. We find a striking pattern of return continuation at half‐hour intervals that are exact multiples of a trading day, and this effect lasts for at least 40 trading days. Volume, order imbalance, volatility, and bid‐ask spreads exhibit similar patterns, but do not explain the return patterns. We also show that short‐term return reversal is driven by temporary liquidity imbalances lasting less than an hour and bid‐ask bounce. Timing trades can reduce execution costs by the equivalent of the effective spread.     

Pricing and information content of block trades on the Shanghai Stock Exchange

We study the pricing mechanisms and information content of block trades on the Shanghai Stock Exchange (SSE) for the six year period from 2003 to 2009.There is an average of about 4% block discount, which is large in magnitude and statistically significant, reflecting compensation for locating counterparties and the cost of negotiating terms. We also examine permanent price impacts of the trades and find that discount block trades (DBTs) have significant negative permanent price impacts for various periods extended up to 60 trading days after the block trades. Conversely, premium block trades (PBTs) have small and statistically insignificant negative permanent price impacts, suggesting that buyers do not possess valuable private information. Finally, we classify the trades into buys and sells using a set of stricter rules and note similar results to those of DBTs and PBTs. Of additional note, block sells on stocks with expirations of restricted shares seem to have significant information content. As these trades are more likely to be originated from insiders, our results suggest that they strategically time the sale of these shares to maximize gains.     

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.     

The determinants of the price impact of block trades: further evidence

This article extends previous literature which examines the determinants of the price impact of block trades on the Australian Stock Exchange. As previous literature suggests that liquidity exhibits intraday patterns, we introduce time of day dummy variables to explore time dependencies in price impact. Following theoretical developments in previous literature, the explanatory power of the bid–ask spread, a lagged cumulative stock return variable and a refined measure of market returns are also examined. The model estimated explains approximately 29 per cent of the variation in price impact. Block trades executed in the first hour of trading experience the greatest price impact, while market conditions, lagged stock returns and bid–ask spreads are positively related to price impact. The bid–ask spread provides most of the explanatory power. This suggests that liquidity is the main driver of price impact.     

A Regime-Level Empirical Model of the Specialist Quote Revision Process

NYSE trading is a continuous auction process distinguished by order flow imbalances and non-coincident revision of the bid and the ask. To deal with the aggregation problem presented by non-coincident revision of the quotes, we propose a regime-level empirical model and use it to test the Brock and Kleidon queuing theory of a continuous auction. Using transactions data for IBM for calendar year 1988, Harris, McInish, and Chakravarity (1995) performed a Hausman-type specification test that confirmed the exogeneity of order flow volumes at the bid and the ask. Extending their work, this paper estimates two simultaneous autoregressive ask and bid equations for 50 randomly selected stocks and contrasts thinly traded and volatile stocks. The results support Brock and Kleidon's distinguishing implication—namely, exogenous increases in trading volume raise the ask and lower the bid. Although some queuing system characteristics prove endogenous in thinly traded stocks and in especially volatile stocks, exogenous order flow volume continues to increase spreads across the 50 stock sample. The paper draws conclusions about the appropriate specification of endogeneity, cross-equation disturbances from the bid to the ask, and cross-equation queuing information flows.     

Trader Anonymity, Price Formation and Liquidity

Using data from the Frankfurt Stock Exchange we analyze priceformation and liquidity in a non-anonymous environment withsimilarities to the floor of the NYSE. Our main hypothesis isthat the non-anonymity allows the specialist to assess the probabilitythat a trader trades on the basis of private information. Heuses this knowledge to price discriminate. This can be achievedby quoting a large spread and granting price improvement totraders deemed uninformed. Consistent with our hypothesis wefind that price improvement reflects lower adverse selectioncosts but does not lead to a reduction in the specialist's profit.Further, the quote adjustment following transactions at thequoted bid or ask price is more pronounced than the quote adjustmentafter transactions at prices inside the spread. Our resultsindicate that anonymity comes at the cost of higher adverseselection risk. JEL Classification: G10.