iason’s Post

John H. Cochrane "Portfolios for long-term investors" is highly recommended: https://lnkd.in/eUjDEHTe Cochrane's point is quite interesting, though, and is orthogonal to the discussion on optimality. He emphasizes the fact mostly ignored by theoreticians that there is no "optimal portfolio" without specifying "for whom". "For whom" means that every portfolio serves a purpose - it serves an individual or a group. Optimality then becomes subservient to the purpose. The problem of an individualized long-term portfolio is rich, complex, and the solutions (there are many) are immensely _useful_. Many of us quants sometimes forget, regretfully, that the whole point is to be _useful_.

Modern Portfolio Theory has had "almost no impact on portfolio practice" according to John H Cochrane, writing in Review of Finance a few years ago. "Let us figure out why investors are not implementing our theory, and try to make the theory more useful so they are more interested in using it", he continued. But how do we do that? I don't know the answer, by the way, as to why optimization is so unpopular as a means of arriving at a portfolio. Beyond the robustness concerns, which might be a tad overblown due to the use of naked covariance or mean estimates, I suspect there is something basic: an entirely understandable reluctance to maintain in our heads a mental model of matrix inversion ... and be sufficiently comfortable with it to place our life savings on the line! (The same discomfort applies to combining forecasts and to model ensembles. How often do we progress past precision weighting, which ignores the off-diagonal covariance entries? Sometimes we don't even do that, and use a straight average of forecasts.) Well, here's my contribution. I provide the unexpected mathematical connection between heuristic top-down portfolio construction and optimization. Here is a means of replicating the minimum variance portfolio, normally obtained by inversion of a matrix, using a procedural, top-down divide and conquer allocation scheme. This unified the two sides of the portfolio debate, and provides a continuum between them. The decision to invert, or not invert, is no longer binary. I describe a recipe for a more strongly motivated version of Hierarchical Risk Parity, as an example, that can contract onto the minimum variance portfolio - should you wish it to. #portfolio #investing #portfoliomanagement #optimization #machinelearning Paper: https://lnkd.in/emzXjrXn Code: https://lnkd.in/di48NkeM

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Cutting Edge: Authors introduce novel methodology for pricing and management of complex share buy-back contracts https://hubs.li/Q02LdhxF0 Non-subscribers can get a snapshot of Risk’s coverage. Registration is free, and allows you to read two articles a month: https://hubs.li/Q02Ldhwx0

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Cutting Edge: Authors introduce novel methodology for pricing and management of complex share buy-back contracts https://hubs.li/Q02Ldhxy0 Non-subscribers can get a snapshot of Risk’s coverage. Registration is free, and allows you to read two articles a month: https://hubs.li/Q02LdtCd0