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.
Not all low volatility strategies are created equal. Many lack the diversification and risk control needed to guard against concentration and macro risk.
There is hope for investors seeking a robust emerging market equity strategy to complement their other equity investments.
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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