Language negativity and analysts' earnings forecast

Purpose– The paper examines the effect of language negativity of US financial analysts’ ancestral origins on
their earnings forecast behavior.
Design/methodology/approach– The paper first developed a dictionary of the most emotionally negative
words in 25 languages, based on the study by Dodds et al. (2015). The authors constructed firm-year analyst
level earnings forecast data and applied multivariate regression model along with a series of robustness tests to
examine the research question.
Findings–Theempiricalresults indicate that financial analysts with their ancestral countries characterized by a
high level of language negativity tend to issue less optimistic earnings forecasts than other analysts. Additional
evidence suggests that the effect of language negativity on analysts’ forecast is strengthened (1) during periods
offinancial crisis, (2) for firms with losses and a highlevelofearningsvolatilityand(3)foryoungeranalystsand
analysts working for small brokerage firms. Finally, we find evidence that higher levels of language negativity
increase analysts’ forecast accuracy.
Originality/value– Collectively, the findings of this study support the conjecture that the level of negativity
across languages can have a significant impact on capital market participants’ behavior. Thus, the study sheds
light on how culturally inherited emotion can affect analysts’ earnings forecast properties.