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PurposeThe purpose of this paper is to examine the effect of firm-level investor sentiment on a firm's share liquidity.Design/methodology/approachThe 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.FindingsThe 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.Research limitations/implicationsThe 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.Practical implicationsThe sample period covers four years (2015–2018), which is determined by the availability of the Bloomberg sentiment data.Social implicationsInvestors 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.Originality/valueBy 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.

The value relevance research in accounting offers robust analyses about how the market views accounting information. Although significant work has been done to date in this mature research area, the literature does not fully explain or agree upon the changes in value relevance over time, and the impact of economic conditions on the value relevance of accounting information is a relatively understudied research stream. We review three streams of the value relevance literature: (i) the value relevance of earnings and book values, (ii) the value relevance of other accounting information, and (iii) the role of economic conditions on the value relevance of accounting information. Further, we review the various explanations put forth in the literature to try to explain the variation in value relevance over time. Finally, we provide a limited baseline for the current state of scholarly work and offer insight for future value relevance research.

Actuaries manage risk, and asset price volatility is the most fundamental parameter in models of risk management. This study utilizes recent advances in econometric theory to decompose total asset price volatility into a smooth, continuous component and a discrete (jump) component. We analyze a data set that consists of high-frequency tick-by-tick data for all stocks in the S&P 100 Index, as well as similar futures contract data on three U.S. equity indexes and three U.S. Treasury securities during the period 1999-2005. We find that discrete jumps contribute between 15% and 25% of total asset risk for all equity index futures, and between 45% and 75% of total risk for Treasury bond futures. Jumps occur roughly once every five trading days for equity index futures, and slightly more frequently for Treasury bond futures. For the S&P 100 component stocks, on days when a jump occurs, the absolute jump is between 80% and 90% of the total absolute return for that day. We also demonstrate that, in the cross section of individual stocks, the average jump beta is significantly lower than the average continuous beta. Cross-correlations within the bond and stock markets are significantly higher on days when jumps occur, but stockbond correlations are relatively constant regardless of whether or not a jump occurs. We conclude with a discussion of the implications of our findings for risk management.

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