Analyst expectations of firms' earnings are on average biased upwards, and that bias varies over time and stocks, according to new research by experts at Wharton and elsewhere. They have developed a machine-learning model to generate "a statistically optimal and unbiased benchmark" for earnings expectations, which is detailed in a new paper titled, "Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases." According to the paper, the model has the potential to deliver profitable trading strategies: to buy low and sell high. When analyst expectations are too pessimistic, investors should buy the stock. When analyst expectations are excessively optimistic, investors can sell their holdings or short stocks as price declines are forecasted.