Asset Management

Portfolio committees have a talent for making slow processes look dignified. The ritual is familiar: an Investment Policy Statement sets the mandate, analysts prepare capital market assumptions, consultants run an optimizer or two, the investment committee meets, the board receives a memo, and everyone hopes the assumptions survive until the next review cycle. It is not irrational. It is simply bounded by human attention, calendar slots, model-maintenance capacity, and the fact that even very clever people cannot run twenty competing allocation philosophies before lunch. ...

TL;DR for operators A portfolio does not care whether your signal has a beautiful causal origin story. It cares whether the signal points in roughly the right direction, ranks assets usefully, and is scaled well enough not to produce absurd weights. That is the useful, slightly impolite message of Alejandro Rodriguez Dominguez’s paper, Is Causality Necessary for Efficient Portfolios?1 The paper challenges a strong claim in recent causal factor-investing work: that causal factor models are necessary for investment efficiency. Its answer is narrower and more operational. Within static mean-variance and related quadratic optimisation frameworks, causal identification is not the necessary condition. The necessary operating conditions are geometric. ...

TL;DR for operators A recent paper, Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints, proposes a portfolio engine that first turns the S&P 500 into a dependency network, then strips that network down to a minimum spanning tree, then selects the five most central stocks, then allocates capital using risk-aware weights, and finally uses ARIMA or neural autoregressive forecasts to decide whether those positions deserve exposure on a given day.1 ...