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.     

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).     

Trade size clustering and the cost of trading at the London Stock Exchange

For the London Stock Exchange, this paper investigates differences in trading costs between market maker (off-book) and order book trades, in the context of clustering in trade sizes and prices. We report several substantial findings. Even after controlling for differences in trade size, the realised spread measure is lower for off-book trades. For the order book, trade size clustering is not associated with differences in transaction costs nor with differences in the information content of trades. For the off-book market, trades in clustered (popular) sizes carry significantly more information than non-clustered trades. Despite the significant differences in the price impact estimates between the order book and off-book, we show that traders placing large orders off-book are still better off than trading via the order book as they benefit from a large discount from the current midpoint price. Additionally, we highlight that price and size clustering tend to occur simultaneously rather than being substitutes in this market setting.     

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.     

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.     

Transaction duration and asymmetric price impact of trades—Evidence from Australia

This paper investigates the asymmetric price impact of buyer and seller initiated trades and the informational role of the trade duration. Using trade data from the Australian Stock Exchange (ASX), our results indicate that buyer initiated trades increase the ask price more than the bid price, and seller initiated trades decrease the bid price more than the ask price. The transaction duration is modeled in a Box-Cox ACD framework. The unexpected portion of the duration is found to play a more significant role in causing price impact in both purchases and sales than the expected duration. A trade shortly after the previous trade results in higher price impact than one after a long period. We found evidence that increased trading activity is associated with larger price impact, therefore implying a higher degree of information-based trading.     

Which trades move prices in emerging markets?: Evidence from China's stock market

This paper extends Barclay and Warner's [Barclay, M.J. and J.B. Warner (1993), ‘Stealth trading and volatility: which trades move prices?’, Journal of Financial Economics, vol. 34, pp. 281–306.] original work on stealth trading by analysing which trades move price for the emerging Chinese stock market. A large block trade/manipulation hypothesis is proposed in addition to the stealth and public information hypotheses examined by Barclay and Warner. Using high-frequency data the results show that while medium and large-size trades are associated with disproportionately large, overall, cumulative stock price changes, it is the large-size trades (in terms of the number of transactions) which have the largest effect on cumulative price increases. Thus, while there is some support for stealth trading in the Chinese market, there are other effects in operation such as large block trades/price manipulation.     

Ripples through markets: Inter-market impacts generated by large trades

This paper uses a sample of large trades executed on the London Stock Exchange's SEAQ-I market for European cross-traded firms to investigate their impact on home market prices when parallel markets suffer from information frictions. I find that (a) large London trades produce price impacts in home markets even though no timely information is published, (b) market makers appear to pre- and post-position their inventories by splitting orders across markets, and (c) the price discovery process across markets changes significantly around large trades with the foreign market making a significantly bigger contribution to price discovery at this time, even though information opaqueness exists.     

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.     

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.

Universal price impact functions of individual trades in an order-driven market

The trade size ω has a direct impact on the price formation of the stock traded. Econophysical analyses of transaction data for the US and Australian stock markets have uncovered market-specific scaling laws, where a master curve of price impact can be obtained in each market when stock capitalization C is included as an argument in the scaling relation. However, the rationale of introducing stock capitalization in the scaling is unclear and the anomalous negative correlation between price change r and trade size ω for small trades is unexplained. Here we show that these issues can be addressed by taking into account the aggressiveness of orders that result in trades together with a proper normalization technique. Using order book data from the Chinese market, we show that trades from filled and partially filled limit orders have very different price impacts. The price impact of trades from partially filled orders is constant when the volume is not too large, while that of filled orders shows power-law behavior r?～?ω^α with α?≈?2/3. When returns and volumes are normalized by stock-dependent averages, capitalization-independent scaling laws emerge for both types of trades. However, no scaling relation in terms of stock capitalization can be constructed. In addition, the relation α?=?α_ω/α _r is verified for some individual stocks and for the whole data set containing all stocks using partially filled trades, where α_ω and α _r are the tail exponents of trade sizes and returns. These observations also enable us to explain the anomalous negative correlation between r and ω for small-size trades.     

Informational linkage and price discovery between China's futures and spot markets: Evidence from the US–China trade dispute

This study examines the effects of the US–China trade dispute on the informational linkages and price discovery between China's futures and spot markets. Using the daily price data of four assets representing the real and financial sectors in China during 2016–2019, empirical findings suggest that the futures–spot correlations for the stock index, copper, and corn markets have increased significantly during the trade dispute. In contrast, sharp declines in the dynamic correlations between gold futures and spot markets, as gold is a safe haven asset, are observed during the event window. During uncertainty disturbance (i.e., the trade dispute), the futures–spot cointegrated relationships in the gold and corn markets are found to adjust more quickly and efficiently, whereas the correction speeds of the market deviations for the stock index and copper market are moderately slower. With the intensive integration of market expectations with uncertainty shocks, the economic shocks of trade disputes tend to remarkably improve the pricing efficiency of China's futures markets, except for the gold futures market. China's spot markets, however, seem to be more sensitive to the noise trades and information disturbances arising from the trade dispute.     

TRADE SIZE AND PRICE CLUSTERING: THE CASE OF SHORT SALES AND THE SUSPENSION OF PRICE TESTS

In this article, we compare trade size and price clustering of short sales with regular trades. We find that short sales cluster less on round sizes and round prices than do nonshort trades. When price tests are suspended, both trade size and price clustering markedly increase for short sales although the difference between shorts and nonshorts remains significant during the postsuspension period. These results are consistent with the idea that because of execution uncertainty caused by price tests, short sellers are less concerned with cognitive processing costs, negotiations costs, and the costs associated with revealing information through trade sizes.     

Managers' Trading Around Stock Repurchases

We analyze personal open market trades by managers around stock repurchases by tender offer. With the exception of Dutch auction offers, managers trade their firm's shares prior to repurchase announcements as though repurchases convey favorable inside information to outsiders. Prior to fixed price repurchase offers that do not follow takeover-related events, managers increase their buying and reduce their selling of their firm's shares. Prior to repurchases that follow takeover-related events, only a decrease in selling is found. No abnormal trading precedes Dutch auction repurchase offers.     

Financial Intermediation and the Costs of Trading in an Opaque Market

Municipal bonds trade in opaque, decentralized broker-dealermarkets in which price information is costly to gather. We analyzea database of trades between broker-dealers and customers inmunicipal bonds. These data were only released to the publicwith a lag; the market was opaque. Dealers earn lower averagemarkups on larger trades, even though dealers bear a higherrisk of losses with larger trades. We estimate a bargainingmodel and compute measures of dealer’s bargaining power.Dealers exercise substantial market power. Our measures of marketpower decrease in trade size and increase in the complexityof the trade for the dealer. (JEL G0, G24)     

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.     

INTRADAY STEALTH TRADING: WHICH TRADES MOVE PRICES DURING PERIODS OF HIGH VOLUME?

Research documents a U-shaped intraday pattern of returns. We examine which trade sizes drive the U-shaped pattern and find that intraday price changes from larger trades exhibit a U-shaped pattern whereas price changes from smaller trades show a reverse U-shaped pattern. We argue that price changes from smaller trades are higher during the middle of the day because informed investors break up their trades to disguise their information when intraday volume is low. Price changes from larger trades are likely higher at the beginning and end of the day because high volume allows informed investors to increase their trade size without revealing their information to the market.     

Are the Insider Trades of a Large Institutional Investor Informed?

We use a unique data set to consider whether a large institution's (Fidelity funds) insider trades are informed. Theoretical studies of large informed traders suggest that their information advantage could be greater for buy trades than sell trades, be short‐ or long‐lived, and be exploited by varying the pace of trade execution. Although there is evidence of each of these, Fidelity seems to be informed only for quickly executed buy trades. Other trades outperform a stock market index but not a four‐factor return model. This performance profile is consistent with Fidelity's fees, which depend on performance compared to an index.     

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.     

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.