Language negativity and analysts’ earnings forecast
研究发现，分析师祖籍语言中的负面情绪水平越高，其盈利预测越不乐观，且在金融危机、公司亏损或盈利波动大时影响更强，但预测准确性反而更高。
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 The empirical results 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 of financial crisis, (2) for firms with losses and a high level of earnings volatility and (3) for younger analysts and 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.