Recent Quant Links from Quantocracy as of 10/28/2025

Features are inputs to machine learning algorithms. Sometimes also called independent variables, covariates, or just X, they can be used for supervised or unsupervised learning, or for optimization. For example, at QTS, we use more than 100 of them as inputs to dynamically calibrate the allocation between our Tail Reaper strategy and E-mini S&P 500 futures. In general, modelers have no idea

SUMMARY Sensitivity analysis can highlight portfolio risks However, it is sensitive to data availability and assumptions Focusing on tail betas is sensible INTRODUCTION The U.S. stock market is currently being driven by a small group of stocks, creating a concentration risk not seen in decades. While comparisons to the 2000 tech bubble are common, a key difference is that todays leading

The Term Structure of Stock-Bond Covariances (Gandhi) After 2009, the term structure of stockbond covariances turned downward-sloping, with long-maturity bonds providing a stronger equity hedge than short-maturity bonds. A new stockbond factor based on these covariances predicts bond excess returns (R > 20%) after 2010. Key takeaway: Unconventional monetary policy post-GFC affected the

A growing body of research suggests that inflation doesnt just erode purchasing power it reshapes how financial markets function. This paper shows that when inflation rises, trading behavior changes in systematic ways: liquidity deteriorates, bid-ask spreads widen, and investors trade less on fundamentals and more on short-term noise. These effects amplify volatility and reduce

Gold prices have risen sharply in recent months, prompting renewed debate over whether the market has reached its peak. In this post, we examine quantitative models used to forecast gold prices and evaluate their effectiveness in capturing volatility and market dynamics. However, gold is not only a speculative vehicle, it also functions as an effective hedging instrument. We explore both aspects