Analyst forecasting during COVID-19 pandemic

Purpose: This study aims to examine the characteristics of financial analysts’ earnings forecasts after COVID-19 outbroke in the USA. Specifically, the authors examine how financial analysts tradeoff between accuracy and responsiveness under investors’ heightened information demand when there is market-wide uncertainty. In addition, the authors investigate how COVID-19 may affect analysts’ cognitive bias. Design/methodology/approach: The research uses a sample of US-listed firms from March 2019 to February 2021, the period surrounding the COVID-19 outbreak in the USA. Findings: The empirical analyses reveal that analysts issue timelier, more frequent, but less accurate forecasts after the COVID-19 outbreak, indicating that analysts become more responsive to investors’ intensified demand for information during the pandemic. Yet, the high uncertainty caused by COVID-19 increases forecasting difficulty. There is no systematic difference regarding the forecast accuracy between high- and low-ability analysts. Meanwhile, high-quality audit can improve forecast accuracy. Contrary to prior findings that analysts tend to underreact to bad news, the empirical evidence suggests that analysts, shaped by the salience bias, overestimate the negative impact of the pandemic. Analysts first issue pessimistic forecasts at the start of the outbreak and then revise forecasts upward steadily as the fiscal year-end approaches. Originality/value: The study contributes to the literature by adding novel evidence on how COVID-19-induced uncertainty affects analyst forecast characteristics. It also provides additional evidence on how high-quality audit is associated with improved analyst forecast accuracy even under heightened uncertainty of COVID-19.