Switching Portfolios

TL;DR

This paper introduces a Bayesian portfolio selection algorithm that adapts to changing markets and outperforms fixed strategies, demonstrating effectiveness on 22 years of NYSE data.

Contribution

The paper presents a novel Bayesian algorithm for dynamic portfolio selection that accounts for market non-stationarity and transaction costs.

Findings

Algorithm effectively tracks market changes.

Outperforms fixed rebalanced portfolios.

Proven on 22-year NYSE data.

Abstract

A constant rebalanced portfolio is an asset allocation algorithm which keeps the same distribution of wealth among a set of assets along a period of time. Recently, there has been work on on-line portfolio selection algorithms which are competitive with the best constant rebalanced portfolio determined in hindsight. By their nature, these algorithms employ the assumption that high returns can be achieved using a fixed asset allocation strategy. However, stock markets are far from being stationary and in many cases the wealth achieved by a constant rebalanced portfolio is much smaller than the wealth achieved by an ad-hoc investment strategy that adapts to changes in the market. In this paper we present an efficient Bayesian portfolio selection algorithm that is able to track a changing market. We also describe a simple extension of the algorithm for the case of a general transaction cost, including the transactions cost models recently investigated by Blum and kalai. We provide a simple analysis of the competitiveness of the algorithm and check its performance on real stock data from the New York Stock Exchange accumulated during a 22-year period.