We investigate how firm-specific certification practices through corporate governance can reduce perceived ambiguity and thus enhance liquidity of a firm in the stock market. We show that better corporate governance helps reduce ambiguity. In addition, a reduction in ambiguity is significantly related to higher liquidity of firms. Our results are robust to alternative model specifications and measures of ambiguity, and remain statistically significant after controlling for other known determinants of ambiguity and liquidity. Our results shed light on how ambiguity can be moderated through firm-level certification practices and on the channel through which a moderation of ambiguity affects shareholder wealth.
Vol. 49 No. 4 - November 2014
I investigate the role odd lot trades play in equity markets, and how this role changes over four periods: 2005, 2007, 2010, and 2012. In each of these years, I examine the determinants, price contribution, and characteristics of odd lot trading. I find that odd lot proportions are increasing, but the determinants of the proportions remain consistent. I find that odd lot transactions contribute to price formation, this contribution is in excess of the odd lot proportion of volume, and is increasing over time. An intraweek pattern of odd lot trading exists with Monday having the highest proportions.
Insider trading may alleviate financing constraints by conveying value-relevant information to the market (the information effect) or may exacerbate financing constraints by impairing market liquidity and distorting insiders’ incentives to disclose value-relevant information (the confidence effect). We examine the significance of these two contrasting effects by investigating the link between insider trading and financing constraints as measured by the investment-cash flow sensitivity. We find that, overall insider trading exacerbates financing constraints; however the information effect dominates the confidence effect for insider purchases. Only trades by executive directors are significantly related to financing constraints.
This paper investigates split credit ratings awarded by Moody’s and Standard & Poor’s (S&P) to U.S. corporations. Bivariate probit model estimates, analyzing 5,238 firm-year observations from dual-rated S&P 500/400/600 index-constituent corporations, indicate firm-specific financial and governance characteristics predict split ratings. Large, profitable companies with enhanced interest coverage, a greater percentage of independent directors, and more institutional investment are less likely to receive splits. Moody’s appears more conservative in its evaluations, assigning lower ratings to smaller, less profitable companies with low interest coverage. Moody’s also associates external, independent constraints on managerial autonomy with a higher corporate credit standing relative to S&P.
We examine the effects of daily return compounding, financing costs, and management factors on the performance of leveraged exchange-traded funds (LETFs) over various holding periods. We propose a new method to measure LETFs’ tracking errors that allows us to disentangle these effects. Our results show that the compounding effect generally has more influence on tracking errors than other factors, especially for long holding periods and in a “sideways” market. The explicit costs (i.e., the expense ratios) and other factors (e.g., financing costs) can materially affect the performance of LETFs, especially for those with high leverage ratios and bear funds.
We examine how a firm’s research and development (R&D) increases affect its intra-industry competitors in the long run. Consistent with the R&D spillover hypothesis, when a firm unexpectedly increases its R&D spending, its intra-industry competitors experience improvements in operating performance and analyst forecast revisions and earn positive abnormal stock returns in the long run. The industry concentration, which is related to the firm’s strategic reaction, is crucial in determining the magnitude of the R&D spillover effect.
We compare 20 years of data from Thompson Financial SDC Platinum (SDC)’s Mergers and Acquisitions database with a hand-collected database, providing evidence on the completeness and accuracy of SDC data across time. We find that our hand-collected data is generally more accurate than SDC, but SDC’s accuracy and coverage improves over time. Our investigation of discrepancies between the databases finds that SDC is more prone to errors on smaller, high book-to-market acquirers with weak announcement period market responses. Preliminary analyses suggest that this potential bias is not significant, but could affect inferences when examining smaller, high book-to-market firms.