Using Machine Learning to Improve Economic Forecasts

Wharton Webinar Series

In this webinar, Professor Jules H. van Binsbergen will discuss how he and other researchers used state-of-the-art machine learning methods to create an algorithm that predicts economic fundamentals such as GDP (both nationally and locally), consumption, and employment growth, even after controlling for commonly-used predictors; it also materially predicts monetary policy decisions, particularly during recessions. The measure relies on text of 200 million pages from 13,000 US local newspapers to construct a novel 170-year-long time series measure of economic sentiment at the country and state levels. This innovative application of machine learning techniques on a large historical data set has allowed for a better understanding of the role that sentiment has on economic activity as well as an improved predictive power for economic forecasts.

This Wharton Webinar Series is powered by The Wharton Fund and is exclusively available to Wharton alumni with PennKey login credentials.

Date and Time:
Wednesday, December 6, 2023
12:00 PM ET — 1:00 PM ET

About Jules H. van Binsbergen
Jules van Binsbergen conducts theoretical and empirical research in finance. His current work focuses on asset pricing, in particular the relationship between financial markets and the macro economy, and the organization, skill and performance of financial intermediaries. Some of his recent research focuses on the influence of financial market anomalies on real economic activity, measuring the skill of mutual fund managers and the term structure of cash flow growth and stock return predictability. Professor van Binsbergen’s research has appeared in leading academic journals, such as the American Economic Review, the Journal of Finance, the Journal of Financial Economics and the Journal of Monetary Economics. He received his PhD from the Fuqua School of Business at Duke University. After obtaining his PhD in 2008, he joined the faculty at Stanford’s Graduate School of Business, where he got tenure in 2014. He joined the Wharton School in 2014.

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