Quant Radio: Predicting Stock Returns with Local and Global Data

In this episode of Quant Radio, we explore one of the most fundamental questions in modern finance: When predicting stock returns, is it better to rely on global data or focus on local market insights?

Backed by a massive 30-year dataset covering 45 markets and 147 stock characteristics, this discussion breaks down a compelling new study that uses machine learning—specifically, the Elastic Net model—to uncover whether broader data truly gives investors an edge. The results might surprise you.

From analyzing abnormal returns and Sharpe ratios to identifying when global strategies outperform local ones (and why they often don’t), we uncover practical insights that could change how you approach investing.

Whether you’re managing portfolios, researching market signals, or just fascinated by how data shapes financial decision-making, this episode brings clarity to the trade-off between complexity and precision. Dive in and discover where the real predictive power lies.

Find the full research paper here: https://community.quantopian.com/c/community-forums/the-more-the-better-predicting-stock-returns-with-local-and-global-data

For more quant-focused content, join us at ⁠⁠⁠⁠https://community.quantopian.com⁠⁠⁠⁠. There, you can explore a wealth of resources, connect with fellow quants, engage in insightful discussions, and enhance your skills through our extensive range of online courses.

Quant Radio is an AI-generated podcast, intended to help people develop their knowledge and skills in Quant finance. This podcast is not intended to provide investment advice.