Can Machine Learning Predict a Hit or Miss on Estimated Earnings?
February 4, 2016
At Bloomberg, we encourage our technologists and engineers to explore new technologies and think outside the box to solve complex problems — especially when solving those problems brings more value to our customers. It is why Bloomberg Software Engineer Roberto Martin recently embarked on a project to implement prediction models regarding whether a publicly traded company will beat earnings estimates.
The code for this project and detailed paper resides on GitHub.
“My time spent working on this idea gave me an invaluable learning experience. Given the available programming tools and frameworks, relatively cheap cloud-based systems and readily available data, one can attain significant accomplishment with a little effort,” said Martin.
Here is the summary of his work in his own words:
Public companies listed and traded on the U.S stock exchanges are required by law to report earnings at the end of each fiscal quarter. This quarterly report is scrutinized carefully by current and prospective shareholders to gain insights into how well the company is doing, and make decisions on whether or not to invest.
Traders are also keenly interested in these reports. Share prices can move significantly if there are any surprises – such as when actual reported earnings differ greatly from analysts’ estimates.
But what if – by using machine learning – you could figure out the likelihood of these variables in advance?...MORE