  There is considerable evidence that machine learning algorithms have better
predictive abilities than humans in various financial settings. But, the
literature has not tested whether these algorithmic predictions are more
rational than human predictions. We study the predictions of corporate earnings
from several algorithms, notably linear regressions and a popular algorithm
called Gradient Boosted Regression Trees (GBRT). On average, GBRT outperformed
both linear regressions and human stock analysts, but it still overreacted to
news and did not satisfy rational expectation as normally defined. By reducing
the learning rate, the magnitude of overreaction can be minimized, but it comes
with the cost of poorer out-of-sample prediction accuracy. Human stock analysts
who have been trained in machine learning methods overreact less than
traditionally trained analysts. Additionally, stock analyst predictions reflect
information not otherwise available to machine algorithms.
