Machine Learning Insights
Richard discusses the challenges of developing a machine learning model that operates on a larger scale, emphasizing the importance of fundamental data over short-term trading signals. He contrasts traditional hypothesis-driven approaches with a theory-free model that seeks to uncover patterns within vast datasets. This perspective opens up new avenues for understanding market behaviors without preconceived notions.In this clip
From this podcast
Capital Allocators – Inside the Institutional Investment Industry
Richard Craib – Crowdsourcing Data Science for Returns at Numerai (Capital Allocators, EP.314)
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