The properties of variance ratio tests across trading and non-trading periods are examined using the generalized method of moments. For the case of opening and closing return variances, the joint tests indicate that the null hypothesis that the variance of opening returns equals the variance of closing returns cannot be rejected for a sample of New York Stock Exchange stocks. This example demonstrates the importance of accounting for overlapping observations and cross correlation in such frameworks. The conventional average (across assets) variance ratio test is shown to be biased against the null in small samples. Specifically, when non-zero correlations are ignored, previous tests have the wrong asymptotic size. This bias persists in other frameworks as well: although this study confirms earlier findings that the return variance during non-trading periods is significantly lower than during trading periods, test statistics that ignore correlations are shown to be inflated.

A unique data set from the Tel-Aviv Stock Exchange (TASE) is used to study the effect of trading mechanisms on stock return volatility. The TASE represents a natural experiment which allows separation of the overnight information effect from the trading mechanism effect. The data span a time period in which the order of the trading mechanisms (a sequential continuous mechanism and a call auction) was switched. Since overnight information should impact opening prices equally across periods, this affords an unparalleled opportunity to examine the trading mechanism effect without the confounding effect of the non-trading period. This paper finds that the null, that opening variances equal closing variances, cannot be rejected for either period. Further, the tests cannot reject null that the ratio of opening to closing return variances is equal across periods. This suggests that the trading mechanisms on the TASE do not differ in their effect upon return volatility.

Using a unique dataset based on daily and hourly high-yield bond transaction prices, we find the informational efficiency of corporate bond prices is similar to that of the underlying stocks. We find that stocks do not lead bonds in reflecting firm-specific information. We further examine price behavior around earnings news and find that information is quickly incorporated into both bond and stock prices, even at short return horizons. Finally, we find that measures of market quality are no poorer for the bonds in our sample than for the underlying stocks.

Using a unique dataset based on daily and hourly high-yield bond transaction prices, we find the informational efficiency of corporate bond prices is similar to that of the underlying stocks. We find that stocks do not lead bonds in reflecting firm-specific information. We further examine price behavior around earnings news and find that information is quickly incorporated into both bond and stock prices, even at short return horizons. Finally, we find that measures of market quality are no poorer for the bonds in our sample than for the underlying stocks.

Information about an executive's decisions at his/her former employer can have a significant impact on the stock returns of his/her current employer, even after the job change occurs. In this article, we analyze transfers of information, which, we argue, reflect executive decision‐making ability as executives change jobs. We examine information about restructurings and write‐downs at the new employer emerging after the executive has left an employer that indicates the executive is of lower ability than investors had expected at the time of the hiring. Our results show that such signals are associated with significant negative returns to the current employer's shares, particularly for within‐industry job changes. We distinguish between restructurings at firms with and without departing executives and find that following an executive's departure, firms that had executives depart experience negative market reactions.

We consider how images (i.e., photos but not graphs, charts, or infographics) in annual reports provide users with information and use machine-learning algorithms to assess their informativeness. We develop a metric of content reinforcement, defined as the degree to which information investors extract from images complements and reinforces details in textual narratives. We find that firms are more likely to use images when they experience greater asset growth, have greater business complexity, and provide less readable textual disclosures—suggesting images are used more often when information processing costs are high. Our main results indicate that increases in visual prevalence and the extent to which images reinforce text are associated with greater analyst forecast accuracy and lower dispersion, suggesting that images improve users’ information processing. Firms also increase image use after an exogenous decline in analyst coverage. Overall, firms use images when their information environment is poorer, and visual informativeness facilitates information assimilation.