A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This work extends Ledoit-Wolf shrinkage to unknown mean covariance estimation.
Estimates covariance matrices with correlations between samples.
We study the design of portfolios under a minimum risk criterion. The performance of the optimized portfolio relies on the accuracy of the estimated covariance matrix of the portfolio asset returns. For large portfolios, the number of available market returns is often of similar order to the number of assets, so that t…
New method improves PCA for high-dimensional data with n < p.
Spatial statisticians and quantitative investors use the same mathematical object: a Schur complement, damped by one parameter.
New methods incorporate alpha signals into portfolio construction, improving performance.
Hybrid classical-quantum framework optimizes portfolio rebalancing with reduced transaction costs.
Unified framework for optimizing portfolios with distributions over weights, returns, and parameters.
A new method corrects bias in machine learning for trading by filtering out non-executable prices.
This study evaluates shrinkage estimators for improving mean and covariance in portfolio optimization.