We document how professional analysts' predictions of firm exits disagree with machine-based predictions. First, on average, human predictions underperform machine predictions. Second, however, the relative performance of human to machine predictions improves for firms with specific characteristics, such as less observable information, possibly due to the unstructured information used only in human predictions. Third, for firms with less information, reallocating prediction tasks from machine to analysts reduces type I error while simultaneously increasing type II error. Under certain conditions, human predictions can outperform machine predictions.
Keywords: Machine Learning; Human Prediction; Disagreement
Views expressed in the paper are those of the authors and do not necessarily reflect those of the Bank of Japan or Institute for Monetary and Economic Studies.