Long-form notes on portfolio intelligence, quantitative allocation, and the structural gaps between how markets actually behave and how standard risk models assume they do.
The 95% Value-at-Risk number on most risk dashboards is computed under one quiet assumption — that returns are Gaussian. They aren't. A correction Cornish and Fisher published in 1937 fixes the bias, and almost no production risk system uses it. Here is the math.
Read more →Most quantitative models pick a fixed forecast window — 21 days, 60 days, one quarter — and never revisit the choice. We show why that's wrong, derive a horizon-from-data rule, and explain when stable regimes earn longer forecasts while fast-rotation regimes earn shorter ones.
Read more →Classical theory predicts that strong directional consensus stabilizes markets. Twenty years of multi-asset data shows the opposite — and four independent statistical specifications agree.
Read more →The math behind the "projected portfolio value" panel on our model-portfolio page. Why we don't compound observed alpha geometrically — and the four-step formula you can replicate in a spreadsheet.
Read more →Why the time between generating an investment insight and acting on it is the single most important variable in portfolio management — and why most allocators have no idea how long their own decision cycle is.
Read more →Structural advantages of models derived from mathematical physics over classical factor approaches, when applied to portfolio construction at institutional scale.
Read more →Institutional-grade allocation technology has been concentrated in a handful of zip codes for decades. Software is permanently changing that equation — and most distribution channels haven't noticed yet.
Read more →The gap between what wealth management costs to deliver and what clients are charged has never been wider. Automation is the only path that closes it without compromising quality.
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