Adaptive Configuration Oracle for Online Portfolio Selection Methods

TL;DR

This paper introduces an adaptive Bayesian optimization oracle that automatically tunes parameters of online portfolio selection algorithms to better handle non-stationary financial market data, improving their performance.

Contribution

It proposes a novel adaptive Bayesian optimization approach using Gaussian processes to dynamically configure online portfolio selection methods in non-stationary environments.

Findings

Effective parameter tuning improves portfolio selection performance.

The method adapts to different market conditions across various datasets.

Enhanced robustness of algorithms in noisy, non-stationary data environments.

Abstract

Financial markets are complex environments that produce enormous amounts of noisy and non-stationary data. One fundamental problem is online portfolio selection, the goal of which is to exploit this data to sequentially select portfolios of assets to achieve positive investment outcomes while managing risks. Various algorithms have been proposed for solving this problem in fields such as finance, statistics and machine learning, among others. Most of the methods have parameters that are estimated from backtests for good performance. Since these algorithms operate on non-stationary data that reflects the complexity of financial markets, we posit that adaptively tuning these parameters in an intelligent manner is a remedy for dealing with this complexity. In this paper, we model the mapping between the parameter space and the space of performance metrics using a Gaussian process prior. We then propose an oracle based on adaptive Bayesian optimization for automatically and adaptively configuring online portfolio selection methods. We test the efficacy of our solution on algorithms operating on equity and index data from various markets.