Joseph Simonian, PhD, is senior investment strategist at Scientific Beta. He is a noted contributor to leading finance journals and is also a prominent speaker at investment events worldwide. Simonian is also currently the co-editor of the Journal of Financial Data Science and on the editorial board of The Journal of Portfolio Management. He holds a PhD from the University of California, Santa Barbara; an MA from Columbia University; and a BA from the University of California, Los Angeles.
As machine learning (ML) and data science become ever more integrated into finance, which factors should we consider for our ML-driven investment models and how should we select among them?
Can we retain the benefits and economically sound basis of a factor approach to equity investing while more closely aligning a factor portfolio’s performance to a cap-weighted benchmark?
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