This paper investigates the relation between analyst characteristics (number of analysts following a firm and their forecast dispersion) and market liquidity characteristics (bid‐ask spreads and depths and the adverse‐selection component of the spread). Prior research has found contradictory results on the relation between analyst following and market liquidity and has offered differing theories on how analysts affect liquidity. While prior research has posited analysts as proxies for privately informed trade or as signals of information asymmetry, I hypothesize that analysts provide public information, implying that analyst following (forecast dispersion) should have a positive (negative) association with liquidity. Cross‐sectional simultaneous estimations provide support for this hypothesis. The results are both statistically significant and economically important. Granger causality tests indicate that analyst characteristics lead market liquidity characteristics. These results clarify the role of analysts in providing information to financial markets and highlight benefits of increased analyst following.

In this paper, we examine the relative efficiency of audit production by one of the then Big 6 public accounting firms for a sample of 247 geographically dispersed audits of U.S. companies performed in 1989. To test the relative efficiency of audit production, we use both stochastic frontier estimation (SFE) and data envelopment analysis (DEA). A feature of our research is that we also test whether any apparent inefficiencies in production, identified using SFE and DEA, are correlated with audit pricing. That is, do apparent inefficiencies cause the public accounting firm to reduce its unit price (billing rate) per hour of labor utilized on an engagement? With respect to results, we do not find any evidence of relative (within‐sample) inefficiencies in the use of partner, manager, senior, or staff labor hours using SFE. This suggests that the SFE model may not be sufficiently powerful to detect inefficiencies, even with our reasonably large sample size. However, we do find apparent inefficiencies using the DEA model. Audits range from about 74 percent to 100 percent relative efficiency in production, while the average audit is produced at about an 88 percent efficiency level, relative to the most efficient audits in the sample. Moreover, the inefficiencies identified using DEA are correlated with the firm's realization rate. That is, average billing rates per hour fall as the amount of inefficiency increases. Our results suggest that there are moderate inefficiencies in the production of many of the subject public accounting firm's audits, and that such inefficiencies are economically costly to the firm.