Robust and Adaptive Algorithms for Online Portfolio Selection

TL;DR

This paper introduces two online algorithms for portfolio selection that adapt quickly to new data, outperforming benchmarks in computational efficiency and financial returns, with applications in algorithmic trading.

Contribution

The paper presents two novel online portfolio algorithms, R-EWRLS and O-VAR, integrating signal processing and statistical techniques for improved performance.

Findings

Outperform benchmark techniques in real datasets

Reduce computational demand compared to traditional methods

Achieve better financial performance in online trading scenarios

Abstract

We present an online approach to portfolio selection. The motivation is within the context of algorithmic trading, which demands fast and recursive updates of portfolio allocations, as new data arrives. In particular, we look at two online algorithms: Robust-Exponentially Weighted Least Squares (R-EWRLS) and a regularized Online minimum Variance algorithm (O-VAR). Our methods use simple ideas from signal processing and statistics, which are sometimes overlooked in the empirical financial literature. The two approaches are evaluated against benchmark allocation techniques using 4 real datasets. Our methods outperform the benchmark allocation techniques in these datasets, in terms of both computational demand and financial performance.