Online Portfolio Selection: A Survey

TL;DR

This survey comprehensively reviews online portfolio selection techniques, categorizing approaches from machine learning perspectives and relating them to financial theories to guide future research and applications.

Contribution

It provides a structured overview of state-of-the-art online portfolio selection methods, linking them with Capital Growth theory and highlighting open issues and future directions.

Findings

Categorizes approaches into benchmarks, Follow-the-Winner, Follow-the-Loser, Pattern-Matching, and Meta-Learning.

Connects algorithms with Capital Growth theory to understand trading strategies.

Identifies open issues and emerging trends for future research.

Abstract

Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining, etc. This article aims to provide a comprehensive survey and a structural understanding of published online portfolio selection techniques. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and "Meta-Learning Algorithms". In addition to the problem formulation and related algorithms, we also discuss the relationship of these algorithms with the Capital Growth theory in order to better understand the similarities and differences of their underlying trading ideas. This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in the financial industry to help them understand the state-of-the-art and facilitate their research and practical applications. We also discuss some open issues and evaluate some emerging new trends for future research directions.