Knowledge that Transforms

Abstract The mere marketing of firms as environmentally friendly does not mean that the firms are genuinely green. In this paper, we propose a new measure, Green Score , to capture firms’ investment in green human capital based on the concentration of green skills required in firms’ job postings. First, we find that firms that increase their Green Score have higher future profitability. Second, firms that increase their Green Score generate more green patents, and those green patents are of higher quality and receive more citations. Third, traditional ratings widely used to evaluate firms’ environmental efforts do not consider firms’ Green Score . Overall, our new action-based measure is simpler and less subjective and it offers a larger time-series variation than traditional disclosure-based environmental ratings.

Abstract This study provides plausible causal evidence on the effect of executive equity incentives on opportunistic manager behavior. I exploit a unique setting created by the introduction of Financial Accounting Standard (FAS) 123R in 2005, which led to an exogenous increase in the cost of option pay, causing a substantial decline in option pay for some firms while leaving others largely unaffected. Using difference-in-differences analyses with a treatment group of firms that show a decline in option pay and two control groups, I find that the likelihood of a treatment firm meeting or beating analyst forecasts decreases by 14–20%. The results show that the relatively high levels of meet-or-beat before FAS 123R were largely driven by real activities manipulation such as abnormal asset sales and sales manipulation to beat analysts’ benchmarks, while accrual manipulation and analyst management were less relevant. Together, the results suggest that equity incentives encourage opportunistic actions to meet or beat earnings expectations, and a decline in option pay results in a decline in earnings management to meet earnings expectations.

Abstract Using unique new data, we examine whether brokerage trading volume creates a conflict of interest for analysts. We find that earnings forecast optimism is associated with higher brokerage volume, even controlling for forecast and analyst quality, recommendations, and target prices. However, forecast accuracy is also significantly associated with higher volume. When analysts change brokerage houses, they bring trading volume with them, influencing trading volume at the new brokerage. This indicates that analysts drive the volume effects we observe. Consistent with a reward for generating volume, brokerage houses are less likely to demote analysts who generate more volume. Finally, analysts strategically adjust forecast optimism based on expected volume impact. Analysts become more (less) optimistic if their optimistic forecasts in the prior year were more (less) successful at generating volume. However, consistent with higher costs to increasing accuracy, analysts do not update accuracy based on expected volume impact. Overall, our results are consistent with a brokerage trading volume conflict of interest moving analysts towards more optimistic earnings forecasts, despite the volume reward for accuracy.

Abstract We show analysts’ own earnings forecasts predict error in their own forecasts of earnings at other horizons, which we argue provides a measure of the extent to which analysts inefficiently use information. We construct our measure by exploiting two sources of variation in analysts’ incentives: (i) more recent forecasts have greater salience at the time of the earnings release so accuracy incentives are higher (lower) at shorter (longer) forecast horizons and (ii) analysts have greater incentives for optimism (pessimism) at longer (shorter) horizons. Consistent with these incentives affecting the incorporation of information into forecasts, we document (i) current year forecasts underweight (overweight) information in shorter (longer) horizon forecasts and (ii) the mis-weighting is more pronounced when recent news is negative—when analysts have greater (weaker) incentives to incorporate the news into shorter (longer) horizon forecasts. Finally, returns tests suggest that forecasts adjusted for the inefficiency we document better represent market expectations of earnings.

Abstract Increasing access to alternative or “big data” sources has given rise to an explosion in the use of these data in economics-based research. However, in our enthusiasm to use the newest and greatest data, we as researchers may jump to use big data sources before thoroughly considering the costs and benefits of a particular dataset. This article highlights four practical issues that researchers should consider before working with a given source of big data. First, big data may not be conceptually different from traditional data. Second, big data may only be available for a limited sample of individuals, especially when aggregated to the unit of interest. Third, the sheer volume of data coupled with high levels of noise can make big data costly to process while still producing measures with low construct validity. Last, papers using big data may focus on the novelty of the data at the expense of the research question. I urge researchers, in particular PhD students, to carefully consider these issues before investing time and resources into acquiring and using big data.