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We argue that uninformed subscribers to an initial public offering (IPO) of common stocks are exposed to greater ex ante risk of trading against informed traders in the secondary market because the advent of public trading conveys hitherto private information and thereby mitigates adverse selection. The going‐public firm underprices the new issue to compensate uninformed subscribers for this added secondary market adverse selection risk. We test this market liquidity‐based explanation by investigating the ex‐post consequences of ownership structure choice on the initial pricing and the secondary market liquidity of a sample of initial public offerings on the New York Stock Exchange (NYSE). Consistent with our argument, we find that initial underpricing varies directly with the ex post trading costs in the secondary market. Further, initial underpricing is related positively to the concentration of institutional shareholdings and negatively to the proportional equity ownership retained by the founding shareholders. Finally, the secondary market illiquidity of new issues is positively related to institutional ownership concentration and negatively to ownership retention and underwriter reputation. Thus, the evidence based on our NYSE sample supports the view that the entrepreneurs' choice of ownership structure affects both the initial pricing and the subsequent market liquidity of new issues.

This study investigates whether broker anonymity impairs the ability of the market to detect informed trading in the lead up to takeover announcements. Our research represents the first study in this area to analyse the effects of broker anonymity in the context of significant information asymmetry. Results indicate that informed traders are less detected, and therefore better off when broker identifiers are concealed. This finding has important policy implications for exchange officials deciding whether or not to reveal broker identifiers surrounding trades, especially considering that almost all prior research suggests that broker anonymity is correlated with improved liquidity.

The purpose of this paper is to examine the effect of firm-level investor sentiment on a firm's share liquidity.

The authors use Bloomberg's firm-level, daily investor sentiment scores derived from firm-level news and Twitter content in a regression model to explain the variability in a firm's share liquidity.

The results indicate that improvements (deterioration) in investor sentiment derived solely from Twitter content lead to a decrease (increase) in the average firm's share liquidity. Results, although not as strong, are opposite for investor sentiment derived solely from news articles: improvements (deterioration) in news sentiment leads to an increase (decrease) in the average firm's share liquidity.

The proxy for share liquidity is the bid-ask spread, which may be an imperfect measure of liquidity. The Amihud illiquidity measure was used as an alternative proxy and yield similar results. The results have important implications for investors in assessing the determinants of share liquidity.

The sample period covers four years (2015–2018), which is determined by the availability of the Bloomberg sentiment data.

Investors increasing use of social media to express views on particular stocks and seek information that might be used in the investment decision-making process. The study links investor sentiment derived from social media (Twitter) to share liquidity.

By examining the relationship between a firm's sentiment and the firm's share liquidity, this paper advances the authors' understanding of the factors that drive a firm's share liquidity. To the authors' knowledge, this is the first study to link investor sentiment derived from firm-level news and Twitter content to a firm's share liquidity.

Using S&P 500 additions, the purpose of this paper is to test the permanence of abnormal returns around the index inclusion announcement and effective implementation dates to differentiate among competing explanations for the index inclusion premia puzzle.

The event study methodology is used to examine abnormal returns and volume effects around the announcement dates (ADs) and implementation dates of index additions.

This study documents a twofold increase in trading volume and significant permanent abnormal returns at the ADs that are correlated with subsequent decreases in bid‐ask spreads. There is a fivefold increase in trading volume, but only temporary abnormal returns, around the effective dates (EDs). Taken collectively, the evidence indicates that the permanent return at announcement is best explained by liquidity/information cost explanation, but the temporary return and large trading increases at the ED can best be attributed to the price pressure hypothesis.

These results do not support the well documented long‐run downward‐sloping demand curve as the primary explanation for the abnormal returns observed on these dates.

This study contributes to the body of literature on the index inclusion effect by providing supporting evidence for the liquidity/information cost explanation, and by extending the previously analyzed index additions with an additional five‐year period from 2000‐2004.

The purpose of this study is to examine the impact of a recent innovation in the uranium market, the Global X Uranium Exchange-Traded Fund (URA), on the trading characteristics of constituent and non-constituent stocks.

The authors analyse bid-ask spread measures, relative effective spreads and adverse selection costs to assess changes in information asymmetry among uranium stocks. The authors also study abnormal returns to assess the impact of URA on the market.

Over a three-month period, following the introduction of URA, the authors find uranium stocks display decreased bid-ask spread measures, driven by reductions in information asymmetry. Relative effective spreads decrease by 36% after the introduction of URA, and adverse selection costs decline by 24% over the same period. Uranium stocks experience a significant positive abnormal return of 5.0% the day after the introduction of URA with subsequent price reversals. These suggest that the introduction of URA prompted uninformed traders to rebalance portfolios and migrate to the less information-sensitive Exchange-Traded Fund (ETF), causing temporary deviations in trading characteristics.

The authors demonstrate that the introduction of new financial securities to the market can have a significant impact on the trading characteristics of related equities. As URA is the only ETF in the uranium sector, the authors thereby avoid the influence of multiple ETFs that may have impacted previous studies.

The purpose of this paper is to examine trading costs of both acquiring firms and target firms differentiated by method of payment, mode of acquisition, and deal attitude around merger and acquisition (M&A) announcements. The author calculates four spread measures of trading costs: quoted spread, percentage quoted spread, effective spread, and percentage effective spread.

Differences in spreads differentiated by M&A characteristics are calculated and two‐sample t‐tests applied. A linear regression model is developed to test whether changes in trading costs are related to acquiring firm's post‐merger price performance. The regression is estimated by OLS method.

It is found that various methods of payment affect the spreads of target firm differently on certain days around M&A announcement. For acquiring firms, significant differences are found in spreads between cash offers and stock offers, and between stock offers and mix offers. Significant difference was not found in spreads between cash offers and mix offers. The mode of acquisition affects the bid‐ask spreads of target firms only, but not those of acquiring firms. Deal attitudes affect the spreads of target firms on and after M&A announcements. It was also found that all four spread measures are significantly linked to acquiring firms’ post‐merger daily returns.

Further study can be done on mechanisms through which M&A characteristics impact trading costs.

This study suggests that M&A characteristics affect firms’ spreads and that changes in spreads need to be accounted for in explaining acquiring firms’ post‐merger daily returns.

The paper fills in an important gap in existing literature by examining trading costs of acquiring firms around M&A announcements. It provides additional evidence on the anomaly of acquiring firm's negative post‐merger returns. The paper is intended to help improve the understanding of trading costs and the behavior of the market participants in response to a major corporate event.

This study investigates the impact of the enhanced continuous disclosure regime introduced in December 2002 on several measures of information risk in NZX‐listed stocks. We employ two microstructure models and an intraday data set to measure information risk in a sample of 71 stocks. Our empirical results show that the reforms enacted in December 2002 had no significant effect on either the level of information‐based trading or the adverse selection component of market spreads in our sample of NZX‐listed stocks.

This paper aims to investigate the performance of estimators of the bid-ask spread in a wide range of circumstances and sampling frequencies. The bid-ask spread is important for many reasons. Because spread data are not always available, many methods have been suggested for estimating the spread. Existing papers focus on the performance of the estimators either under ideal conditions or in real data. The gap between ideal conditions and the properties of real data are usually ignored. The consistency of the estimates across various sampling frequencies is also ignored.

The estimators and the possible errors are analysed theoretically. Then we perform simulation experiments, reporting the bias, standard deviation and root mean square estimation error of each estimator. More specifically, we assess the effects of the following factors on the performance of the estimators: the magnitude of the spread relative to returns volatility, randomly varying of spreads, the autocorrelation of mid-price returns and mid-price changes caused by trade directions and feedback trading.

The best estimates come from using the highest frequency of data available. The relative performance of estimators can vary quite markedly with the sampling frequency. In small samples, the standard deviation can be more important to the estimation error than bias; in large samples, the opposite tends to be true.

There is a conspicuous lack of simulation evidence on the comparative performance of different estimators of the spread under the less than ideal conditions that are typical of real-world data. This paper aims to fill this gap.

This study aims to investigate whether the cash flow forecasts (CFF) of analysts can disseminate valuable information to the information environments of companies.

The author uses empirical archival methodology to conduct differences-in-difference analyses.

It is found that information asymmetry decreases in the treatment group following the initiation of CFF during the postperiod, which is consistent with the hypothesis of this paper.

To the best of the author’s knowledge, this study is the first among the cash flow forecast studies to demonstrate the usefulness of CFF in the mitigation of information asymmetry, a friction that is widespread in capital markets.