Boston QWAFAFEW – Jan 19 2021, 7:00 PM ET
Bayesian Portfolio Allocation
A QWAFAFEW discussion led by,
Markowitz portfolio optimization is optimal in theory, however, when applied in practice it often fails catastrophically. Usually, this is addressed by various simplifications to increase robustness. In this talk I will make the case that the reason this theory fails in practice is because uncertainty in the parameter estimation is not taken into account. By using Bayesian statistics we can fix Markowitz and retain all its desirable properties while still having a robustness technique that can be easily extended. This talk is geared at intermediate and will give a general introduction to Bayesian modeling using PyMC3 and focus on application and code examples rather than theory.
Thomas Wiecki is the Chief Executive Officer at PyMC Labs, a Bayesian consultancy (www.pymc-labs.io). Prior to that Thomas was the VP of Data Science at Quantopian, where he used probabilistic programming and machine learning to help build the world’s first crowdsourced hedge fund. Among other open source projects, he is involved in the development of PyMC3—a probabilistic programming framework for Python. He holds a PhD from Brown University.
Time: 7:00 PM sharpe
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