Liquidity, analysts, and institutional ownership

In this paper, we investigate the empirical relationship between institutional ownership, number of analysts following and stock market liquidity. We find that firms with larger number of financial analysts following have wider spreads, lower market quality index, and larger price impact of trades. However, we find that firms with higher institutional ownership have narrower spreads, higher market quality index, and smaller price impact of trades. In addition, we show that changes in our liquidity measures are significantly related to changes in institutional ownership over time. These results suggest that firms may alleviate information asymmetry and improve stock market liquidity by increasing institutional ownership. Our results are remarkably robust to different measures of liquidity and measures of information asymmetry.     

The next tick on Nasdaq

The Nasdaq stock market provides information about buying and selling interest in its limit order book. Using a vector autoregressive model of trades and returns, I assess the effect of the entire order book on the next tick. I also determine the influence of individual market makers and electronic networks and find evidence that the identity of market participants can be useful information. Finally, I produce a set of dynamic market price responses to buy and sell orders, and I find that these estimates vary with standard measures of liquidity.     

Information-Based Trading and Price Improvement

Abstract:  In this study we test the information hypothesis of price improvement. Our results show that price improvement is negatively related to both the probability of information-based trading and the price impact of trades. We interpret these results as evidence that liquidity providers selectively offer price improvements according to the information content of trades. We also show that liquidity providers offer greater (and more frequent) price improvements when they are at the NBBO, and for stocks with wider spreads, fewer trades, or smaller trade sizes relative to the quoted depth. Buyer-initiated trades receive smaller (larger) price improvements than seller-initiated trades on the NYSE (NASDAQ).     

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.     

Time and the Price Impact of a Trade

We use Hasbrouck's (1991) vector autoregressive model for prices and trades to empirically test and assess the role played by the waiting time between consecutive transactions in the process of price formation. We find that as the time duration between transactions decreases, the price impact of trades, the speed of price adjustment to trade-related information, and the positive autocorrelation of signed trades all increase. This suggests that times when markets are most active are times when there is an increased presence of informed traders; we interpret such markets as having reduced liquidity.     

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.     

Liquidity shocks and intraday price reaction

Using a global sample of high-frequency data, I investigate how liquidity shocks affect intraday price movements. I find a negative association between liquidity shocks and price impact. This finding remains robust after considering the exogeneity of liquidity shocks, using alternative windows to measure liquidity shocks, and controlling for volume shocks and volatility shocks. Additional tests show that the documented relation stems from idiosyncratic shocks and sell-order shocks. Moreover, I find that liquidity shocks are likely driven by uninformed traders. My evidence suggests that the market requires 30 min to accomplish price adjustments when meeting liquidity shocks.     

Price dispersion in OTC markets: A new measure of liquidity

In this paper, we model price dispersion effects in over-the-counter (OTC) markets to show that, in the presence of inventory risk for dealers and search costs for investors, traded prices may deviate from the expected market valuation of an asset. We interpret this deviation as a liquidity effect and develop a new liquidity measure quantifying the price dispersion in the context of the US corporate bond market. This market offers a unique opportunity to study liquidity effects since, from October 2004 onwards, all OTC transactions in this market have to be reported to a common database known as the Trade Reporting and Compliance Engine (TRACE). Furthermore, market-wide average price quotes are available from Markit Group Limited, a financial information provider. Thus, it is possible, for the first time, to directly observe deviations between transaction prices and the expected market valuation of securities. We quantify and analyze our new liquidity measure for this market and find significant price dispersion effects that cannot be simply captured by bid-ask spreads. We show that our new measure is indeed related to liquidity by regressing it on commonly-used liquidity proxies and find a strong relation between our proposed liquidity measure and bond characteristics, as well as trading activity variables. Furthermore, we evaluate the reliability of end-of-day marks that traders use to value their positions. Our evidence suggests that the price deviations from expected market valuations are significantly larger and more volatile than previously assumed. Overall, the results presented here improve our understanding of the drivers of liquidity and are important for many applications in OTC markets, in general.     

THE DYNAMICS OF QUOTED LIQUIDITY AROUND LARGE TRADES ON THE NYSE

Large trades have potentially disruptive effects on the continuation of subsequent trade. If the large trade executes against volume from limit orders or specialist quotations, continued trade may be unavailable until new liquidity enters the market. Evidence presented in this paper indicates that large trades on the NYSE are followed by decreases in quoted liquidity, which last for an average of fifteen minutes. Both the decreases in quoted liquidity and the time to its subsequent return are related to trade-specific factors. This evidence suggests that not all large trades have the same effect on the continuation of trade.     

Bid ask spread in a competitive market with institutions and order size

Large orders, particularly from institutions, are quite common these days and hence there is interest to know if institutional trading has any bearing on the price effect associated with large trades. Recent empirical studies contradict earlier evidence of negative price effect on selling large blocks and find no price effect associated with large trades. Existing theoretical framework suggests a monotonic and increasing adverse price effect for large trades, where the motivation for a large trade is private information. We model a trading system where pure information, information-liquidity, and pure liquidity traders trade small and large sizes. The pure information traders strategically choose an order size. Institutions trade only large sizes because of their low execution costs for large trades; they are information-liquidity traders whose ability to use an information signal to determine their trades is subject to a binding liquidity constraint. We show that in such a market a separating equilibrium where trade size is informative does not exist and hence there is no price effect for large trades. Trade size may be revealing only if there is a buy sell asymmetry (large buy size is not equal to large sell size) or the corresponding price effect is asymmetric (price effect due to a large buy is not equal to that of a large sell). Further for a pooling equilibrium to exist, where trade size is not informative, the width of the market denoted by the ratio of order size (large size/small size) needs to be small, while the shallowness (inverse depth) of the market denoted by the ratio between pure information and institutional trades and the information signal needs to be stronger (higher). Our results on bid and ask prices and spread confirm recent empirical evidence on price effect of large and institutional trades found in the literature.

Large option trades, market makers, and limit orders

This article focuses on the difference between market makersand limit orders in their role as suppliers of liquidity. Forboth sources of liquidity I analyze the price behavior of stocksand options around large option trades and I estimate the premiumpaid by the initiator of the large trade. My findings suggestthat limit orders for options are 'picked off' after adversechanges in the underlying stock price. Furthermore, I find thatfor these transactions there is a permanent change in quotationsin the direction of the transaction. After transactions wheremarket makers supply liquidity, quotes tend to return to theirpretrade level.     

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.     

1-share orders and trades

1-share trades are the most common odd lot trade size, accounting for 9.62% of all odd lot transactions and 3.65% of all trades on NASDAQ in 2012. While 50.41% of 1-share trades result from broken orders, 34.89% of 1-share trades are intentional. We provide substantial evidence that traders use 1-share trades to “ping” for hidden liquidity. In particular, our results indicate that 1-share trades are disproportionately aggressive and also execute against hidden liquidity more than any other odd lot trade size. We also find a relative increase in trading immediately following a 1-share trade. Our results are in line with Clark-Joseph (2014), who suggests that traders may use small, unprofitable trades to detect information from other traders. Specifically, 1-share trades represent the minimum cash outlay necessary to trade, while simultaneously producing the smallest possible effects on a market maker's inventory, and in turn, a security's price.     

Further analysis of the liquidity and information components of institutional orders: Active versus passive funds

Previous research examining the price impact of institutional trading concludes that index funds incur higher liquidity costs due to the higher demand for trading immediacy. However, this conclusion has only been inferred by comparing the total price impact of active and index funds. This study extends the literature by decomposing the price impact of both active and index funds' trades into liquidity (temporary) and information (permanent) components. Index fund trades incur higher liquidity costs and generate lower returns than active funds' trades. Indeed, the evidence presented in this study reveals the execution costs of index funds' trades are entirely liquidity-driven.     

Insider Trading and Financing Constraints

Insider trading may alleviate financing constraints by conveying value‐relevant information to the market (the information effect) or may exacerbate financing constraints by impairing market liquidity and distorting insiders’ incentives to disclose value‐relevant information (the confidence effect). We examine the significance of these two contrasting effects by investigating the link between insider trading and financing constraints as measured by the investment‐cash flow sensitivity. We find that, overall insider trading exacerbates financing constraints; however the information effect dominates the confidence effect for insider purchases. Only trades by executive directors are significantly related to financing constraints.     

From Implicit to Explicit: The Impact of Disclosure Requirements on Hidden Transaction Costs

This paper provides evidence that disclosing corporate bond investors' transaction costs (markups) affects the size of the markups. Until recently, markups were embedded in the reported transaction price and not explicitly disclosed. Without explicit disclosure, investors can estimate their markups using executed transaction prices. However, estimating markups imposes information processing costs on investors, potentially creating information asymmetry between unsophisticated investors and bond‐market professionals. We explore changes in markups after bond‐market professionals were required to explicitly disclose the markup on certain retail trade confirmations. We find that markups decline for trades that are subject to the disclosure requirement relative to those that are not. The findings are pronounced when constraints on investors' information processing capacity limit their ability to be informed about their markups without explicit disclosure.     

You can’t always get what you want: Trade-size clustering and quantity choice in liquidity

This paper examines whether investors care more about trading their exact quantity demands at some times than at others. Using a new data set of foreign exchange transactions, I find that customers trade more precise quantities at quarter-end, as evidenced by less trade-size clustering. Customers trade more odd lots and fewer round lots, while the number of trades and total volume are not significantly changed. I also find that the price impact of order flow is greater when customers care more about trading precise quantities. This work sheds new light on trade-size clustering and offers a potential explanation for time-series and cross-sectional variation in common liquidity measures.     

Strategic trading and learning about liquidity

Many practitioners point out that the speculative profits of institutional traders are eroded by the difficulty in gauging the price impact of their trades. In this paper, we develop a model of strategic trading where speculators face such a dilemma because of incomplete information about time-varying market liquidity. Unlike the competitive market makers that they trade against, informed traders do not know the distribution of liquidity (“noise”) trades. Instead, they have to learn about liquidity from past prices and trading volume. This learning implies that strategic trades and market statistics such as informational efficiency are path-dependent on past market outcomes. Our paper also has normative implications for practitioners.     

Stock dividends in China: signalling or liquidity explanations?

We test alternative hypotheses on a sample of Chinese stock dividends. The inverse Mills ratio, a signal about future performance, is positively related to announcement returns but does not predict higher future performance. Analysts do not revise their earnings forecasts after the announcement date. Our results are more consistent with liquidity‐based theories. We find that managers choose higher stock dividend ratios if share prices deviate more from the industry‐wide average. Increases in proportional spreads, depth, and the number of trades and decreases in average trade size, and price impact suggest greater participation of liquidity and small investors following stock dividends.     

IPO Trading without Market Makers or Underwriter Price Support

We investigate the trading behavior and liquidity supply of Chinese initial public offerings (IPOs) that trade in an order‐driven market system with pure limit order books where no market makers or price support is allowed. We find large trades and quoted depths dominate the first day of trading, but this pattern quickly reverses as small trades and quoted depths are more prevalent on subsequent trading days. Quoted depths are positively related to the number of shares offered in the IPO and trade size, but are negatively related to underpricing. Trade size and transaction immediacy are positively related, and large and positive (negative) order imbalance is associated with more aggressive buys (sells). Finally, long‐run performance is not related to initial order imbalance. Overall, our results suggest that despite underwriters not participating in the IPO aftermarket, liquidity provision evolves very quickly and price discovery is immediately reflected in prices.