Thursday, October 22, 2020

Knowledge@Wharton on Machine Learning and Markets

They go with a much more ambitious headline than we'd prefer.

From K@W, October 13:

How to Beat Analysts and the Stock Market with Machine Learning

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.

“[With the machine-learning model], we can predict how the prices of the stocks will behave based on whether or not the analyst forecast is too optimistic or too pessimistic,” said Wharton finance professor Jules H. van Binsbergen, who is one of the paper’s authors. His co-authors are Xiao Han, a doctoral student at the University of Edinburgh Business School; and Alejandro Lopez-Lira, a finance professor at the BI Norwegian Business School.

The researchers found that the biases of analysts increase “in the forecast horizon,” or in the period when the earnings announcement date is not anytime soon. However, on average, analysts revise their expectations downwards as the date of the earnings announcement approaches. “These revisions induce negative cross-sectional stock predictability,” the researchers write, explaining that “stocks with more optimistic expectations earn lower subsequent returns.” At the same time, corporate managers have more information about their own firms than investors have, and can use that informational advantage by issuing fresh stock, Binsbergen and his co-authors note.

The Opportunity to Profit

Comparing analysts’ earnings expectations with the benchmarks provided by the machine-learning algorithm reveals the degree of analysts’ biases, and the window of opportunity it opens. Binsbergen explained how investors could profit from their machine-learning model. “With our machine-learning model, we can measure the mistakes that the analysts are making by taking the difference between what they’re forecasting and what our machine-learning forecast estimates,” he said....