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Informed Trading Intensity
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There is sufficient evidence in the popular, legal, and financial literatures that informed options trading ahead of scheduled and unexpected corporate events is pervasive. In this review, we piece together the extant evidence on this topic into a cohesive picture, which includes abnormal activity ahead of announcements of earnings, mergers and acquisitions, as well as numerous other corporate events. We also discuss the more limited evidence on informed trading in other derivatives markets, such as credit default swaps. In addition, we characterize the impact and features of illegal insider trading and insider trading networks. We also provide a brief overview of the legal framework in the United States concerning legal and illegal insider trading to emphasize the challenges associated with identifying informed options trading. We end with our suggestions regarding future research opportunities in this broad topic.
We present broad-based evidence that the monthly payment cycle induces systematic patterns in liquid markets around the globe. First, we document temporary increases in the costs of debt and equity capital that coincide with key dates associated with month-end cash needs. Second, we present direct and indirect evidence on the role of institutions in the genesis of these patterns and derive estimates of the associated costs borne by market participants. Third, and finally, we investigate the limits to arbitrage that prevent markets from functioning efficiently. Our results indicate that many investors and their agents, including mutual funds, suffer from liquidity-related trading.
Received January 16, 2018; editorial decision January 2, 2019 by Editor Robin Greenwood. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best-performing methods (trees and neural networks) and trace their predictive gains to allowing nonlinear predictor interactions missed by other methods. All methods agree on the same set of dominant predictive signals, a set that includes variations on momentum, liquidity, and volatility.
Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
We quantify the pervasiveness of informed trading activity in target companies’ equity options before the announcements of 1,859 U.S. takeovers between 1996 and 2012. About 25% of all takeovers have positive abnormal volumes, which are greater for short-dated, out-of-the-money calls, consistent with bullish directional trading before the announcement. Over half of this abnormal activity is unlikely due to speculation, news and rumors, trading by corporate insiders, leakage in the stock market, deal predictability, or beneficial ownership filings by activist investors. We also examine the characteristics of option trades litigated by the Securities and Exchange Commission (SEC) for alleged illegal insider trading. Although the characteristics of such trades closely resemble the patterns of abnormal option volume in the U.S. takeover sample, we find that the SEC litigates only about 8% of all deals in it.
This paper was accepted by Lauren Cohen, finance.
Do fast, competitive markets yield liquidity measurement problems when using the popular Monthly Trade and Quote (MTAQ) database? Yes. MTAQ yields distorted measures of spreads, trade location and price impact compared with the expensive Daily Trade and Quote (DTAQ) database. These problems are driven by: (1) withdrawn quotes, (2) second (vs. millisecond) timestamps, and (3) other causes, including cancelled quotes. The expensive solution, using DTAQ, is first best. For financially constrained researchers, the cheap solution, using MTAQ with our new Interpolated Time technique, adjusting for withdrawn quotes, and deleting economically nonsensical states, is second best. These solutions change research inferences.
This paper attempts to bridge the gap between rational expectations equilibrium analysis and the standard CAPM. It finds that although the market portfolio is not mean-variance efficient under asymmetric information, the security market capital line still exists. Moreover, this security market line is determined by the Information-adjusted Average Market Portfolio (IAMP) such that the expected rate of return to an asset equals the product of IAPM's return and the asset's beta with respect to the IAMP. The paper also investigates Bayesian decision-making based on lognormal prior and lognormal signals, which overcomes a major technical difficulty in asset pricing under asymmetric information.
If competitive equilibrium is defined as a situation in which prices are such that all arbitrage profits are eliminated, is it possible that a competitive economy always be in equilibrium? Clearly not, for then those who arbitrage make no (private) return from their (privately) costly activity. Hence the assumptions that all markets, including that for information, are always in equilibrium and always perfectly arbitraged are inconsistent when arbitrage is costly. We propose here a model in which there is an equilibrium degree of disequilibrium: prices reflect the information of informed individuals (arbitrageurs) but only partially, so that those who expend resources to obtain information do receive compensation. How informative the price system is depends on the number of individuals who are informed; but the number of individuals who are informed is itself an endogenous variable in the model. The model is the simplest one in which prices perform a well-articulated role in conveying information from the informed to the uninformed. When informed individuals observe information that the return to a security is going to be high, they bid its price up, and conversely when they observe information that the return is going to be low. Thus the price system makes publicly available the information obtained by informed individuals to the uninformed. In general, however, it does this imperfectly; this is perhaps lucky, for were it to do it perfectly , an equilibrium would not exist. In the introduction, we shall discuss the general methodology and present some conjectures concerning certain properties of the equilibrium. The remaining analytic sections of the paper are devoted to analyzing in detail an important example of our general model, in which our conjectures concerning the nature of the equilibrium can be shown to be correct. We conclude with a discussion of the implications of our approach and results, with particular emphasis on the relationship of our results to the literature on "efficient capital markets."
Many questions about institutional trading can only be answered if one tracks high-frequency changes in institutional ownership. In the United States, however, institutions are only required to report their ownership quarterly in 13-F filings. We infer daily institutional trading behavior from the "tape", the Transactions and Quotes database of the New York Stock Exchange, using a sophisticated method that best predicts quarterly 13-F data from trades of different sizes. We find that daily institutional trades are highly persistent and respond positively to recent daily returns but negatively to longer-term past daily returns. Institutional trades, particularly sells, appear to generate short-term losses--possibly reflecting institutional demand for liquidity--but longer-term profits. One source of these profits is that institutions anticipate both earnings surprises and post-earnings announcement drift. These results are different from those obtained using a standard size cutoff rule for institutional trades.
We examine two potential mechanisms through which disclosure quality is expected to reduce information asymmetry: (1) altering
the trading incentives of informed and uninformed investors so that there is relatively less trading by privately informed
investors, and (2) reducing the likelihood that investors discover and trade on private information. Our results indicate
that the negative relation between disclosure quality and information asymmetry is primarily caused by the latter mechanism.
While information asymmetry is negatively associated with the quality of the annual report and investor relations activities,
it is positively associated with quarterly report disclosure quality. Additionally, we hypothesize and find that that the
negative association between disclosure quality and information asymmetry is stronger in settings characterized by higher
levels of firm-investor asymmetry.
This paper shows that over time, expected market illiquidity positively affects ex ante stock excess return, suggesting that expected stock excess return partly represents an illiquidity premium. This complements the cross-sectional positive return–illiquidity relationship. Also, stock returns are negatively related over time to contemporaneous unexpected illiquidity. The illiquidity measure here is the average across stocks of the daily ratio of absolute stock return to dollar volume, which is easily obtained from daily stock data for long time series in most stock markets. Illiquidity affects more strongly small firm stocks, thus explaining time series variations in their premiums over time.
This paper studies insider trading quantities and dollar profits to measure the benefits insiders extract from their superior information. Dollar profits are economically small for a typical insider, the median insider earning $464 per year. The correlation between dollar profits and percentage returns is moderate, because returns are negatively correlated with trade size and frequency. We show that these correlations vary with proxies for insider preferences, firm-level monitoring, and regulatory scrutiny. As a consequence, variables that predict percentage returns fail to predict dollar profits, and past dollar profits are negatively related to future returns. Our work suggests that dollar profits are a better measure for corporate governance applications of insider trading.
Big data is revolutionizing the finance industry and has the potential to significantly shape future research in finance. This special issue contains papers following the 2019 NBER-RFS Conference on Big Data. In this introduction to the special issue, we define the “big data” phenomenon as a combination of three features: large size, high dimension, and complex structure. Using the papers in the special issue, we discuss how new research builds on these features to push the frontier on fundamental questions across areas in finance—including corporate finance, market microstructure, and asset pricing. Finally, we offer some thoughts for future research directions.
I investigate cross-sectional variation in stock returns over the trading day and overnight to shed light on what drives asset pricing anomalies. Margin requirements are higher overnight, and lending fees are typically charged only on positions held overnight. Such institutional constraints and overnight risk incentivize arbitrageurs who trade on mispricing to reduce their positions before the end of the day. Consistent with this intuition, a mispricing factor earns positive returns throughout the day but performs poorly at the end of the day. This pattern strengthens in the second half of the sample and is shared by several well-known anomalies.
The Probability of Informed Trading (PIN) is a widely used indicator of information asymmetry risk in the trading of securities. Its estimation using maximum likelihood algorithms has been shown to be problematic, resulting in biased or unavailable estimates, especially in the case of liquid and frequently traded assets. We provide an alternative approach to estimating PIN by means of a Bayesian method that addresses some of the shortcomings in the existing estimation strategies. The method leads to a natural quantification of the uncertainty of PIN estimates, which may prove helpful in their use and interpretation. We also provide an easy to use toolbox for estimating PIN.
What moves stock prices? Prior literature concludes that the revelation of private information through trading, and not public news, is the primary driver. We revisit the question by using textual analysis to identify fundamental information in news. We find that this information accounts for 49.6% of overnight idiosyncratic volatility (vs. 12.4% during trading hours), with a considerable fraction due to days with multiple news types. We use our measure of public information arrival to reinvestigate two important contributions in the literature related to individual |$R^{2}$|s of stock returns on aggregate factors.
Received February 24, 2016; editorial decision May 19, 2018 by Editor Andrew Karolyi.
Using over 5,000 trades unequivocally based on nonpublic information about firm fundamentals, we find that asymmetric information proxies display abnormal values on days with informed trading. Volatility and volume are abnormally high, whereas illiquidity is low, in equity and option markets. Daily returns reflect the sign of private signals, but bid-ask spreads are lower when informed investors trade. Market makers’ learning under event uncertainty and limit orders help explain these findings. The cross-section of information duration indicates that traders select days with high uninformed volume. Evidence from the U.S. SEC Whistleblower Reward Program and the FINRA involvement addresses selection concerns.
Received January 11, 2017; editorial decision December 17, 2018 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
The cross-section and time series of stock returns contains a wealth of information about the stochastic discount factor (SDF), the object that links cash flows to prices. A large empirical literature has uncovered many candidate factors—many more than seem plausible—to summarize the SDF. This special volume of the Review of Financial Studies presents recent advances in extracting information from both the cross-section and the time series, in dealing with issues of replication and false discoveries, and in applying innovative machine-learning techniques to identify the most relevant asset pricing factors. Our editorial summarizes what we learn and offers a few suggestions to guide future work in this exciting new era of big data and empirical asset pricing.
Understanding modern market microstructure phenomena requires large amounts of data and advanced mathematical tools. We demonstrate how machine learning can be applied to microstructural research. We find that microstructure measures continue to provide insights into the price process in current complex markets. Some microstructure features with high explanatory power exhibit low predictive power, while others with less explanatory power have more predictive power. We find that some microstructure-based measures are useful for out-of-sample prediction of various market statistics, leading to questions about market efficiency. We also show how microstructure measures can have important cross-asset effects. Our results are derived using 87 liquid futures contracts across all asset classes.
This paper exploits hand-collected data on illegal insider trades to provide new evidence on the ability of a host of standard measures of illiquidity to detect informed trading. Controlling for unobserved cross-sectional and time-series variation, sampling bias, and strategic timing of insider trades, I find that when information is short-lived, only absolute order imbalance and effective spread are statistically and economically robust predictors of illegal insider trading. However, when information is long-lasting, insiders strategically time their trades to avoid illiquidity, and none of the standard measures considered are reliable predictors, including bid-ask spreads, order imbalance, Kyle’s λ, and Amihud illiquidity. (JEL D53D82G12G14K42)
Received: March 14, 2019; Editorial decision: February 18, 2020 by Editor Thierry Foucault. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
When activist shareholders file Schedule 13D filings, the average stock-price volatility drops by approximately 10%. Prior to filing days, volatility information is reflected in option prices. Using a comprehensive sample of trades by Schedule 13D filers that reveals on what days and in what markets they trade, we show that on days when activists accumulate shares , option-implied volatility decreases, implied volatility skew increases, and implied volatility time slope increases. The evidence is consistent with a theoretical model where it is common knowledge that informed trading occurs only in the stock market and market makers update option prices based on stock-price and order-flow dynamics.
We revisit the information content of stock trading by corporate insiders with an expectation that opportunistic insiders will spread their trades over longer periods of time when they have a longer-lived informational advantage, and trade in a short window of time when their advantage is fleeting. Controlling for the duration of insiders' trading strategies, we find robust new evidence that both insiders' sales and purchases predict abnormal stock returns. In addition, we provide evidence that insiders attempt to preserve their informational advantages and increase their trading profits by disclosing their trades after the market has closed. When insiders report their trades after business hours, they are more likely to engage in longer series of trades, they trade more shares overall, and their trades are associated with larger abnormal returns. Finally, we show how accounting for these trading patterns sharpens screens for corporate insiders who trade on infor- mation.
We examine the relation between insiders’ investment horizon and the information content of their trades with respect to future stock returns. We conjecture that an insider's investment horizon establishes a benchmark for expected patterns of continued trading behavior and thus helps identify unexpected insider trades, which should be more informative in efficient markets. Consistent with this conjecture, the trades of short‐horizon insiders are both more unexpected and more informed, on average, than those of long‐horizon insiders. Short‐horizon insiders and their firms also tend to display characteristics that are associated with a greater focus on short‐termism. This article is protected by copyright. All rights reserved
For years, the SEC accidentally distributed securities disclosures to some investors before the public. We expolit this setting, which is unique because the delay until public disclosure was exogenous and the private information window was well defined, to study informed trading with a random stopping time. Trading intensity and the pace at which prices incorporate information decrease with the expected delay until public release, but the relation between trading intensity and time elapsed varies with traders' learning process. Noise trading and relative information advantage play similar roles as in standard microstructure theories assuming a fixed time window. This article is protected by copyright. All rights reserved
We show that the PIN and the Duarte and Young (2009) (APIN) models do not match the variability of noise trade in the data and that this limitation has severe implications for how these models identify private information. We examine two alternatives to these models, the Generalized PIN model (GPIN) and the Odders-White and Ready (2008) model (OWR). Our tests indicate that measures of private information based on the OWR and GPIN models are promising alternatives to the APIN’s Adj.PIN and PIN.
We propose a new, price-based measure of information risk called abnormal idiosyncratic volatility (AIV) that captures information asymmetry faced by uninformed investors. AIV is the idiosyncratic volatility prior to information events in excess of normal levels. Using earnings announcements as information events, we show that AIV is positively associated with informed return run-ups, abnormal insider trading, short selling, and institutional trading during pre-earnings-announcement periods. We find that stocks with high AIV earn economically and statistically larger future returns than stocks with low AIV. Taken together, our findings support the notion that information risk is priced.
We propose and estimate a model of endogenous informed trading that is a hybrid of the PIN and Kyle models. When an informed trader trades optimally, both returns and order flows are needed to identify information asymmetry parameters. Empirical relationships between parameter estimates and price impacts and between parameter estimates and stochastic volatility are consistent with theory. We illustrate how the estimates can be used to detect information events in the time series and to characterize the information content of prices in the cross-section. We also compare the estimates to those from other models on various criteria.
Received April 5, 2017; editorial decision September 21, 2017 by Editor Itay Goldstein. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
We explore the dynamics of informed trading around corporate announcements of merger bids, dividend initiations, SEOs, and quarterly earnings by calculating daily posterior probabilities of informed buying and selling. We find evidence of informed trading before the announcements and a significant part of the news in announcements is impounded in stock prices before the announcements by pre-event informed trading. We also find evidence of informed trading after the announcements. Most strikingly, the probability of informed trading after merger bids predicts the probability of the bid being withdrawn or met with a competing bid. For other announcements, post-announcement informed-trading probabilities predict subsequent returns.
Received September 26, 2016; editorial decision December 17, 2017 by Editor Andrew Karolyi.
We analyze dynamic trading by an activist investor who can expend costly effort to affect firm value. We obtain the equilibrium in closed form for a general activism technology, including both binary and continuous outcomes. Variation in parameters can produce either positive or negative relations between market liquidity and economic efficiency, depending on the activism technology and model parameters. Two results that contrast with the previous literature are that (a) the relationship between market liquidity and economic efficiency is independent of the activist's initial stake for a broad set of activism technologies, and (b) an increase in noise trading can reduce market liquidity because it increases uncertainty about the activist's trades (the activist trades in the opposite direction of noise traders) and thereby increases information asymmetry about the activist's intentions.
Deleveraging risk is the risk attributable to investing in a security held by levered investors. When there is an aggregate negative shock to the availability of funding capital, securities with a greater presence of levered investors experience extreme return realizations as these investors unwind their positions. Using data on equity loans as a proxy for the degree of levered positions in a given stock, we find robust evidence of deleveraging risk. Stocks with a high degree of short selling experience large positive returns and a decrease in short selling around periods of funding capital scarcity.
This paper investigates the role of institutional trading in the emergence of hedge fund activism—an important corporate governance mechanism. We demonstrate that institutional sales raise a firm’s probability of becoming an activist target. Furthermore, by exploiting the funding circumstances of individual institutions, we establish that such effects occur through a liquidity channel, i.e., the activist camouflages his purchases among other institutions’ liquidity sales. Additional evidence supports our conclusion. First, activist purchases closely track institutional sales at the daily frequency. Second, such synchronicity is stronger among targets with lower expected monitoring benefits, suggesting that gains from trading with other institutions supplement these benefits in the activist’s targeting decision. Finally, we find that institutional sales accelerate the timing of a campaign at firms already followed by activists rather than attract attention to unlikely targets. Taken together, our findings offer a novel empirical perspective on the liquidity theories of activism; while activists screen firms on the basis of fundamentals, they pick specific targets at a particular time by exploiting institutional liquidity shocks.
The Internet appendix is available at https://doi.org/10.1287/mnsc.2016.2654.
This paper was accepted by Amit Seru, finance.
Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.
We extend Kyle's (1985) model of insider trading to the case where noise trading volatility follows a general stochastic process. We determine conditions under which, in equilibrium, price impact and price volatility are both stochastic, driven by shocks to uninformed volume even though the fundamental value is constant. The volatility of price volatility appears ‘excessive’ because insiders choose to trade more aggressively (and thus more information is revealed) when uninformed volume is higher and price impact is lower. This generates a positive relation between price volatility and trading volume, giving rise to an endogenous subordinate stochastic process for prices.
We develop a dynamic model of a limit order market populated by strategic liquidity traders of varying impatience. In equilibrium, patient traders tend to submit limit orders, whereas impatient traders submit market orders. Two variables are the key determinants of the limit order book dynamics in equilibrium: the proportion of patient traders and the order arrival rate. We offer several testable implications for various market quality measures such as spread, trading frequency, market resiliency, and time to execution for limit orders. Finally, we show the effect of imposing a minimal price variation on these measures. Copyright 2005, Oxford University Press.
Short selling plays a unique role in financial markets. Short selling's institutional structure is distinct from other types of trades, and short sellers have been shown to be more ormed than other types of traders. This review discusses short sellers' motivation, the institutional mechanics of short selling, the empirical findings on short selling, the regulation of short selling, the connection between corporate events and short selling, and the equity lending market. The review assesses the current direction of research as well as summarizes the current state of knowledge about the subject.
We provide evidence that some profitable insider stock selling is motivated by public information. At firms that disclose having concentrated sales relationships, insiders appear to sell their own stock profitably based on public information about their principal customers. Supplier insiders also sell more stock when public information about their customers' recent returns and earnings surprises suggests they will earn larger profits. These results are stronger when outside investor attention could be lower. Outside of this setting, insiders engage in a higher proportion of routine sales and their sales are less profitable. We do not find similar patterns for insider purchases.
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
This paper investigates the effect of trade size on security prices. We show that trade size introduces an adverse selection problem into security trading because, given that they wish to trade, informed traders perfer to trade larger amounts at any given price. As a result, market makers' pricing strategies must also depend on trade size, with large trades being made at less favorable prices. Our model provides one explanation for the price effect of block trades and demonstrates that both the size and the sequence of trades matter in determining the price-trade size relationship.
Abstract Of late, there has been considerable interest among accounting researchers in the relation between asymmetric information and cost of capital. Empiricists have taken the lead with a number,of studies that document associations between proxies for asymmetric information such as earnings quality and risk premiums. However, the theoretical foundation for these studies has yet to be fully established. In this study, we consider the efiects of private signals that are informative of both systematic factors and idiosyncratic shocks afiecting asset payofis in a competitive noisy rational expectations setting. Taking a large economy limit, we show that i) private information about systematic factors afiects risk premiums only through its efiects on factor risk premiums; ii) idiosyncratic risk is fully diversiflable and afiects risk premiums,only as a source of noise in drawing inferences about sys- tematic factors from private signals iii) holding total information constant, greater information asymmetry,leads to higher factor risk premiums,and
This paper examines the implications of market microstructure for asset pricing. I argue that asset pricing ignores the central fact that asset prices evolve in markets. Markets provide liquidity and price discovery, and I argue that asset pricing models need to be recast in broader terms to incorporate the transactions costs of liquidity and the risks of price discovery. I argue that symmetric information-based asset pricing models do not work because they assume that the underlying problems of liquidity and price discovery have been solved. I develop an asymmetric information asset pricing model that incorporates these effects.
According to the Diamond-Verrecchia hypothesis, if increases in short interest are correlated with information that is not yet public, they should precipitate a price adjustment. Stocks with unexpected increases in short interest are found to generate statistically significant, but small, negative abnormal returns for a short period around the announcement date. When the sample is divided into stocks with and without tradable options, nonoptioned stocks closely mimic these results but the optioned stocks do not. In a cross-sectional analysis of individual firms, the short-term negative abnormal returns are found to be 1) more negative, the higher the degree of unexpected short interest and, 2) less negative if the firm has tradable options.
The presence of traders with superior information leads to a positive bid-ask spread even when the specialist is risk-neutral and makes zero expected profits. The resulting transaction prices convey information, and the expectation of the average spread squared times volume is bounded by a number that is independent of insider activity. The serial correlation of transaction price differences is a function of the proportion of the spread due to adverse selection. A bid-ask spread implies a divergence between observed returns and realizable returns. Observed returns are approximately realizable returns plus what the uninformed anticipate losing to the insiders.
Carl Icahn, Irwin Jacobs, Carl Lindner, David Murdock, Victor Posner, and the late Charles Bluhdorn are usually portrayed as corporate ‘raiders’. The evidence here, however, shows that between 1977 and 1982 when it was first announced that they had purchased stock in a given firm, stock prices on average increased significantly. The investors' activities in target firms for the two years following the initial stock purchase are likewise inconsistent with ‘raiding’. We discuss two hypotheses that are consistent with the evidence: first, these investors improve the management of target firms; second, they are systematically able to identify under-priced stocks.
Models of adverse selection risk generally assume that market makers offset expected losses to informed traders with expected gains from the uninformed. We recognize that the expected loss captures a combination of two effects: (1) the probability that some traders have private information, and (2) the likely magnitude of that information. We use a maximum-likelihood approach to separately estimate the probability and magnitude of private information events for NYSE-listed stocks from 1993 through 2003. The results shed light on the price discovery process and have implications for many areas of finance.
This paper suggests that earnings announcements provide information that allows certain traders to make judgements about a firm's performance that are superior to the judgements of other traders. As a result, there may be more information asymmetry at the time of an announcement than in nonannouncement periods. More information asymmetry implies that bid–ask spreads increase, suggesting that market liquidity decreases at the time of an earnings announcement. Furthermore, informed opinions resulting from public disclosure may lead to an increase in trading volume, despite the reduction in liquidity that accompanies announcements.
This paper examines the price effect of institutional stock trading, using a unique data set that reports the transactions (large and small) of 37 large institutional money management firms. The direction of each trade and the identity of the management firm behind each trade are known. Although institutional trades are associated with some price pressure, we find that the average effect is small. There is also a marked asymmetry between the price impact of buys versus sells. We relate our findings to various hypotheses on the elasticity of demand for stocks, the cost of executing transactions, and the determinants of market impact. Although market capitalization and relative trade size influence the market impact of a trade, the dominant influence is the identity of the money manager behind the trade.
This empirical examination of the relation between trades and quote revisions for New York Stock Exchange-listed stocks is designed to ascertain asymmetric-information and inventory-control effects. This study finds that negative autocorrelation in trades consistent with inventory-control behavior characterizes low-volume stocks, but not high-volume stocks. The evidence of inventory control in the impact of trades on quote revisions is inconclusive. The information content of trades, on the other hand, is found to be substantial. There is also strong evidence that large trades convey more information than small trades.