Information Content Of Trades Research Articles

Measuring Price Impact and Information Content of Trades in a Time-Varying Setting

Measuring Price Impact and Information Content of Trades in a Time-Varying Setting

The Information Content of Order Executions at Low-Latency

This paper studies the liquidity of assets traded on NASDAQ as measured by the impact that order executions have on future market prices over very short horizons. After deriving a testable implication of Glosten and Milgrom (1985), high-frequency data from a six month sample of all stocks traded on NASDAQ are applied to the model. Empirical results characterize the average information content across asset executions during the sample period. The most active stocks fit the predictions of the GM model tested here, even at very low latency. Less active stocks do not reject the model, although there is a significant loss of power. Evidence is consistent with the hypothesis that some of this lack of power is due to the censoring that comes from the minimum tick size. Using the structure of the GM model, the information content of executed orders can be characterized. Average information content of trades decreases in the log of message frequency, consistent with the hypothesis that prices become more efficient and individual trades are less likely to alter market prices as the amount of activity in the asset increases. By this measure, frequency trade increases liquidity for the average trader in the average asset. Regression results suggest that the assets for which executions convey the most information are those that are infrequently messaged, have a high price and a low market capitalization. This statistical model has substantial predictive power for the 100 most frequently messaged stocks.

Online Appendix to "Heterogeneous Information Content of Global FX Trading"

This Appendix collects all tables and figures related to the paper. The paper studies the information content of trades in the world's largest over-thecounter market, the foreign exchange (FX) market. The results are derived from a comprehensive order flow dataset distinguishing between different groups of market participants and covering a broad cross-section of currency pairs. Our findings show that both the contemporary and permanent price impact are heterogeneous across agents, time, and currency pairs, supporting the asymmetric information theory. A trading strategy based on the permanent price impact capturing superior information generates high returns even after accounting for risk, transaction costs, and other common risk factors documented in the FX literature.

Heterogeneous Information Content of Global FX Trading

This paper studies the information content of trades in the world's largest over-thecounter market, the foreign exchange (FX) market. The results are derived from a comprehensive order flow dataset distinguishing between different groups of market participants and covering a broad cross-section of currency pairs. Our findings show that both the contemporary and permanent price impact are heterogeneous across agents, time, and currency pairs, supporting the asymmetric information theory. A trading strategy based on the permanent price impact capturing superior information generates high returns even after accounting for risk, transaction costs, and other common risk factors documented in the FX literature.

The bonding hypothesis and the home market liquidity of Chinese cross-listed stocks

We investigate liquidity, transaction costs and information content in trades of mainland Chinese stocks that are cross-listed on the Hong Kong stock market. Most majority of cross-listed Chinese stocks issue shares in the Hong Kong market and domestic shares at the same time, or even issue the former prior to the issuance of the latter. We find supportive evidence of the bonding hypothesis. Cross-listed stocks are more liquid, have lower transaction costs and less information content in trades than those non-cross-listed. These improvements remain significant after controlling for known liquidity determinants.

Optimal VWAP Tracking

We consider the problem of finding a strategy that tracks the volume weighted average price (VWAP) of a stock, a key measure of execution quality for large orders used by institutional investors. We obtain the optimal, dynamic, VWAP tracking strategy in closed form in a model with general price and volume dynamics and show that it can be extended to incorporate proportional transaction costs. We build a model of intraday volume using the Trade and Quote dataset to empirically test the strategy, both without trading costs and when trading has temporary effects that include the bid-ask spread and depth of the order book, and permanent effects that reflect the potential information content of trades. We find that the implementation cost of the strategy we propose is lower than the cost charged by brokerage houses.

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.

The Delta- and Vega-Related Information Content of Trades in Stock and Option Markets

The Delta- and Vega-Related Information Content of Trades in Stock and Option Markets

Is Information Risk Really a Determinant of Security Returns? Evidence from TORQ

Using a sample of NYSE securities, this article investigates whether there is an information risk premium in securities return. The author uses two proxies for information— probability of information trading (PIN) and institutional trading—and determines how those impact securities return after controlling for other known risk factors. Intraday transactions data from TORQ are used to compute PIN, denoting information content in trades from the maximum-likelihood estimates of the parameters of a mixture model. The main finding is that only beta and volatility, denoted by price inverse and standard deviation of returns, respectively, are the primary determinants of security returns, but PIN and institutional trading, proxies for information risk, are not priced in security returns. Beta and volatility are derived from market prices and hence fall under price risk. Turnover, a likely proxy for both liquidity and price risk, is also a significant determinant of return. Contrary to the findings of Easley et al. [2002], this article shows that information risk is not a significant determinant of security return. <b>TOPICS:</b>VAR and use of alternative risk measures of trading risk, statistical methods, exchanges/markets/clearinghouses

Short sale constraints and the information content of trading

We examine changes in the information content of trading when short sale constraints between prohibition and restriction exist on a stock exchange. This is made possible by a unique institutional arrangement at the Stock Exchange of Hong Kong. It maintains a list of stocks which can be sold short under regulations. Stocks not on the list are prohibited from short selling. The list is revised on a quarterly basis based on predetermined criteria. We find that the probability of information-based trading (PIN) significantly increases when a stock is added to the list. Further analysis shows that this is mainly because uninformed traders are driven out of the market. Elimination of uninformed traders also causes the aggregate trading volume to decrease rather than increase. In comparison, the PIN does not change when a stock is dropped from the list. We also find that market liquidity, measured by volatility and bid–ask spreads, slightly decreases when a stock is added to the list and significantly increases when a stock is dropped from the list. Possible explanations are discussed.

How Markets Slowly Digest Changes in Supply and Demand

Publisher Summary This chapter discusses the new approach to the classic problem of tâtonnement —the dynamic process through which markets seek to reach equilibrium. The foundation of this approach is based on several empirical observations about financial markets. The most important of which is long memory in the fluctuations of supply and demand. This is exhibited in the placement of trading orders and corresponds to long-term, slowly decaying positive correlations in the initiation of buying versus selling. It is observed in all the stock markets studied so far at very high levels of statistical significance. It appears that the primary cause of this long memory is the incremental execution of large hidden trading orders. The fact that the long memory of order flow must coexist with market efficiency has a profound influence on price formation, causing dynamic adjustments of liquidity that are strongly asymmetric between buyers and sellers. This has important consequences for market impact. This work has also important consequences about the interpretation and effect of information in financial markets. In particular, the explanation for market impact is that the shape of the impact function is determined by differences in the information content of trades.

How Informed are the Smart Guys? Evidence from Short-Term Institutional Trading Prior to Major Events

We examine the information content of trading by examining institutional investors' ability to trade in the correct direction in the days immediately preceding large value-relevant events. Prior to takeovers, institutional investors are actually small (insignificant) net sellers and there is no evidence that their trading can predict takeover premiums. Institutional trading in the five and ten days prior to earnings announcements is uninformative of earnings announcement returns. For large positive and negative price moves other than takeovers and earnings announcements, we find institutional selling ahead of large up-moves, and institutional buying ahead of large negative returns. We further examine short-term institutional net buying unconditionally and find that it is uninformative of future one- and two-week stock returns. In contrast, we find that institutional trading on and after earnings announcements is profitable. Overall, our findings suggest that aggregate institutional profits may primarily stem from their ability to process publicly available information rather than their ability to extract private information.

Risk, Uncertainty, and the Perceived Threat of Terrorist Attacks: The Advisory System that Cried Wolf?

Information provided by the U.S. Department of Homeland Security regarding potential terrorist attacks significantly affects the U.S. equity market. When the government raises the perceived threat level, investors’ perceptions of risk increase (as measured by the conditional volatility of returns and implied volatility on S&P 500 Index options), but these responses have declined monotonically over time. We also find evidence that informed traders are more active when there is an increased threat of an attack. Thus, the perceived threat of terrorism can affect both equity volatility and the information content of trades but not necessarily the level of aggregate returns.

Herding and the information content of trades in the Australian dollar market

This study shows that the information content of FX transactions depends on the identity of market participants. Using spot FX transactions of a major Australian bank, we find that central banks have the greatest price impact, followed by non-bank financial institutions (NBFIs) such as hedge funds and mutual funds. Trades by non-financial corporations have the least impact on dealer pricing. In the interbank market, dealers with greater private information tend to choose direct trading which has lower post-trade transparency. Indirect trading via brokers is partially revealed to the market and has little price impact. The price impact largely comes from institutions in the top quartile of the trading volume. Furthermore, NBFIs have the greatest propensity for herding, followed by interbank dealers. Non-financial corporations do not herd in their trades. Except for central banks, the differential impact of market participants can largely be explained by their propensity for herding.

The Information Content in Trades of Inactive Nasdaq Stocks

In this paper we analyze the frequency and information content of small Nasdaq stock trades and their impacts on return volatility at the intraday interval. We employ an autoregressive conditional duration (ACD) model to estimate the intensity of the arrival and information content of trades by accounting for the deterministic nature of intraday periodicity and irregular trading intervals in transaction data. We estimate and compare the price duration of thinly and heavily traded stocks to assess the differential information content of stock trades. We find that the number of transactions is negatively correlated with price duration or positively correlated with return volatility. The impact of the number of transactions on price duration or volatility is higher for thinly traded stocks. On the other hand, the persistence of the impact on price duration adjusted for intradaily periodicity is about the same for thinly and heavily traded stocks on average.

Margin regulation and market quality: a microstructure analysis

We find that trading volume increases and market liquidity remains unchanged, while the adverse selection and order-processing cost components of the spread increase and decrease, respectively, after margin levels decline when stocks become margin-eligible. This evidence indicates that the information content of trades has increased, thereby improving market quality. However, no changes were detected after the 1997 regulatory reforms. These results have implications across a broad swath of corporate finance dimensions, including the (1) cost of capital, (2) public vs. private financing decision, (3) form of managerial compensation, (4) type of ownership structure, and (5) degree of shareholder monitoring.

How Stock Splits Affect Trading: A Microstructure Approach

Extending an empirical technique developed in Easley, Kiefer, and O'Hara (1996), (1997a), we examine different hypotheses about stock splits. In line with the trading range hypothesis, we find that stock splits attract uninformed traders. However, we also find that informed trading increases, resulting in no appreciable change in the information content of trades. Therefore, we do not find evidence consistent with the hypothesis that stock splits reduce information asymmetries. The optimal tick size hypothesis predicts that stock splits attract limit order trading and this enhances the execution quality of trades. While we find an increase in the number of executed limit orders, their effect is overshadowed by the increase in the costs of executing market orders due to the larger percentage spreads. On balance, the uninformed investors' overall trading costs rise after stock splits.

Prices, Liquidity, and the Information Content of Trades

We investigate the effect of asymmetric information on prices and liquidity by analyzing trades, quotes, spreads, and depths. Information content should increase with trade size and the information asymmetry of the trading period. Results show that price and liquidity effects are significantly associated with information content as measured by both trade size and timing relative to information events. Results are stronger for purchases than sales. Quoted prices are better measures of information effects than transaction prices, because they control for bid-ask bounce. Finally, trades that a priori contain no information have no impact on prices and liquidity, despite their large size.

Return Volatility, Trading Imbalance and the Information Content of Volume

In this paper, we examine the relationship between volume and return volatility using the transaction data. We introduce transaction and volume imbalance measures to capture the information content of trades. These two information measures are shown to have a strong explanatory power for return volatility and contain incremental information about the asset values over and above that conveyed by the size and frequency of trades. Also, return volatility is significantly correlated with the percentage of trading volume taking place at NYSE. This result suggests that NYSE trades are more informative and contribute more to price discovery. There is evidence that price discovery concentrates in more heavily traded stocks, particularly the Dow Jones Stocks. Finally, return volatility is found to be persistent at the intraday level. The persistence level is higher for less frequently traded stocks. Return volatility also exhibits temporal variations. In particular, return volatility is significantly higher in the opening half-hour for less frequently traded stocks. Thus, stocks with different frequencies of trades may follow different volatility processes.

Inferring the Private Information Content of Trades: A Regime-Switching

This paper presents an empirical model for inferring the private information content of trades at the transaction level. The trade-indicator model of Glosten and Harris (1988) is extended to a two-state regime-switching setting, and the model is estimated using tick-by-tick data from stocks listed on the New York Stock Exchange (NYSE). The specialist is found to react in accordance with the proposed model. Bid-ask quotes, set after the execution of a trade, reflect the conjectured information content of that particular trade. Based on the estimated model, the reverse J-shaped pattern of intra daily quoted spreads is shown to agree with the clustering of costs incurred by the specialist through trading with better informed agents. On average, 9% of all trades are found to reveal private information to the specialist.