Recent Quant Links from Quantocracy as of 04/13/2026

This is a test of Meb Fabers Tactical Yield from T-Bills and ChillMost of the Time. Backtested results from 1930 follow compared to a benchmark of 50% int-term US Treasuries (IEF) and 50% US corporate bonds (LQD). Results are net of transaction costs see backtest assumptions. Learn about what we do and follow 100+ asset allocation strategies like this one in near real-time.

Imagine that a team downloads a price series, defines a target, applies a transformation, and moves on to signal design, model fitting, validation, and execution. That sequence looks efficient. However, the transformation of the data is is the first act of model construction. That is why data-shape transformation sits at the true front line of feature engineering. The problem is whether the

A portfolio of seasonal strategies presents a problem that modern portfolio theory was not designed for. Most of these strategies are active fewer than sixty days per year. Many pairs share zero overlapping observations. The covariance matrix the standard tool for combining return streams produces nothing but noise. You need a different approach. The Foundation The IVOL three-asset core

Someone asked me recently whether strategies based on mean reversion, trend following, and momentum are good or just data mining. Its a reasonable question, but it reveals some confusion that arises from mixing up two things that sound similar but are very different. Mean reversion, trend, momentum: these arent edges. Theyre labels for how prices move. They describe patterns, not

Investment professionals have long relied on factor investingstrategies built around characteristics like value, momentum, and qualityto generate returns beyond the broad market. But predicting which factors will perform well in the future has remained challenging. Liyao Wang and Ming Zeng, authors of the December 2025 study Factor MAX and Predictable Factor Returns, introduced an