Model-free portfolio allocation in continuous-time

TL;DR

This paper develops a path-dependent, model-free framework for continuous-time portfolio allocation, providing explicit solutions and extending universal algorithms to combine multiple strategies with theoretical guarantees.

Contribution

It introduces a novel path-dependent PDE approach for portfolio evolution and extends continuous-time universal algorithms, generalizing Cover's theorem to a model-free setting.

Findings

Explicit solutions for wealth evolution in generic markets.

Continuous-time algorithms track the best strategies with bounded log wealth error.

Extension of Cover's theorem to a path-dependent, model-free context.

Abstract

We present a non-probabilistic, path-by-path framework for studying path-dependent (i.e., where weight is a functional of time and historical time-series), long-only portfolio allocation in continuous-time based on [Chiu & Cont '23], where the fundamental concept of self-financing was introduced, independent of any integration theory. In this article, we extend this concept to a portfolio allocation strategy and characterize it by a path-dependent partial differential equation. We derive the general explicit solution that describes the evolution of wealth in generic markets, including price paths that may not evolve continuously or exhibit variation of any order. Explicit solution examples are provided. As an application of our continuous-time, path-dependent framework, we extend an aggregating algorithm of [Vovk '90] and the universal algorithm of [Cover '91] to continuous-time algorithms that combine multiple strategies into a single strategy. These continuous-time (meta) algorithms take multiple strategies as input (which may themselves be generated by other algorithms) and track the wealth generated by the best individual strategy and the best convex combination of strategies, with tracking error bounds in log wealth of order O(1) and O(ln t), respectively. This work extends Cover's theorem [Cover '91, Thm 6.1] to a continuous-time, model-free setting.