Optimal Investment Under Transaction Costs

TL;DR

This paper develops a signal processing approach to optimal portfolio selection under transaction costs, using threshold rebalancing and Markov chain analysis to maximize expected growth in discrete-time markets with multiple assets.

Contribution

It introduces a recursive threshold rebalancing framework for optimal growth portfolios, extending analysis from two-asset to multi-asset markets with unknown distributions.

Findings

Threshold portfolios form an irreducible Markov chain.

Stationary distribution enables efficient wealth calculation.

Method effectively handles unknown distribution cases.

Abstract

We investigate how and when to diversify capital over assets, i.e., the portfolio selection problem, from a signal processing perspective. To this end, we first construct portfolios that achieve the optimal expected growth in i.i.d. discrete-time two-asset markets under proportional transaction costs. We then extend our analysis to cover markets having more than two stocks. The market is modeled by a sequence of price relative vectors with arbitrary discrete distributions, which can also be used to approximate a wide class of continuous distributions. To achieve the optimal growth, we use threshold portfolios, where we introduce a recursive update to calculate the expected wealth. We then demonstrate that under the threshold rebalancing framework, the achievable set of portfolios elegantly form an irreducible Markov chain under mild technical conditions. We evaluate the corresponding stationary distribution of this Markov chain, which provides a natural and efficient method to calculate the cumulative expected wealth. Subsequently, the corresponding parameters are optimized yielding the growth optimal portfolio under proportional transaction costs in i.i.d. discrete-time two-asset markets. As a widely known financial problem, we next solve optimal portfolio selection in discrete-time markets constructed by sampling continuous-time Brownian markets. For the case that the underlying discrete distributions of the price relative vectors are unknown, we provide a maximum likelihood estimator that is also incorporated in the optimization framework in our simulations.