Portfolio Optimization

What if you could simulate thousands of realistic futures for the market, all conditioned on what’s happening today—and then train an investment strategy on those futures? That’s the central idea behind a bold new approach to portfolio optimization that blends score-based diffusion models with reinforcement learning, and it’s showing results that beat classic benchmarks like the S&P 500 and traditional Markowitz portfolios. ...

The search for the elusive optimal portfolio has always been a balancing act between signal and noise. Covariance matrices, central to risk estimation, are notoriously fragile in high dimensions. Classical fixes like shrinkage, spectral filtering, or factor models have all offered partial answers. But a new paper by Bongiorno, Manolakis, and Mantegna proposes something different: a rotation-invariant, end-to-end neural network that learns the inverse covariance matrix directly from historical returns — and does so better than the best analytical techniques, even under realistic trading constraints. ...