Logan Bell | Searcharxiv

arXiv2026

A Distributed Method for Cooperative Transaction Cost Mitigation

Funds at large portfolio management firms may consist of many portfolio managers (PMs), each managing a portion of the fund and optimizing a distinct objective. Although the PMs determine their trades independently, the trade lists may be netted and executed by the firm. These net trades may be sufficiently large to impact the market prices, so the PMs may realize prices on their trades that are different from the observed midpoint price of the assets before execution. These transaction costs generally reduce the returns of a portfolio over time. We propose a simple protocol, based on methods from distributed convex optimization, by which a firm can communicate estimated transaction costs to its PMs, and the PMs can potentially revise their trades to realize reduced transaction costs. This protocol does not require the PMs to disclose their method of determining trades to the firm or to each other, nor does it require the PMs to communicate their trade lists with each other. As the number of adjustment rounds grows, the trades converge to the ones that are optimal for the firm. As a practical matter we observe that even just a few rounds of adjustment lead to substantial savings for the firm and the PMs.

math.OC↗

arXiv2023

Efficient Shapley Performance Attribution for Least-Squares Regression

We consider the performance of a least-squares regression model, as judged by out-of-sample $R^2$. Shapley values give a fair attribution of the performance of a model to its input features, taking into account interdependencies between features. Evaluating the Shapley values exactly requires solving a number of regression problems that is exponential in the number of features, so a Monte Carlo-type approximation is typically used. We focus on the special case of least-squares regression models, where several tricks can be used to compute and evaluate regression models efficiently. These tricks give a substantial speed up, allowing many more Monte Carlo samples to be evaluated, achieving better accuracy. We refer to our method as least-squares Shapley performance attribution (LS-SPA), and describe our open-source implementation.

stat.CO↗