Online Portfolio Selection: Principles and Algorithms

✍ Scribed by Bin Li (Author); Steven Chu Hong Hoi (Author)

• Publisher: CRC Press
• Year: 2016
• Leaves: 227
• Edition: 1
• Category: Library

✦ Synopsis

With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment.

The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that:

1. Introduce OLPS and formulate OLPS as a sequential decision task
2. Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning
3. Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques
4. Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art
5. Investigate possible future directions

Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB^® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment.

Readers are encouraged to visit the authors’ website for updates: http://olps.stevenhoi.org.

✦ Table of Contents

I: INTRODUCTION

Introduction

Background

What Is Online Portfolio Selection?

Methodology

Book Overview

Problem Formulation

Problem Settings

Transaction Costs and Margin Buying Models

Evaluation

Summary

II: Principles

Benchmarks

Buy-and-Hold Strategy

Best Stock Strategy

Constant Rebalanced Portfolios

Follow the Winner

Universal Portfolios

Exponential Gradient

Follow the Leader

Follow the Regularized Leader

Summary

Follow the Loser

Mean Reversion

Anticorrelation

Summary

Pattern Matching

Sample Selection Techniques

Portfolio Optimization Techniques

Combinations

Summary

Meta-Learning

Aggregating Algorithms

Fast Universalization

Online Gradient and Newton Updates

Follow the Leading History

Summary

III: Algorithms

Correlation-Driven Nonparametric Learning

Preliminaries

Formulations

Algorithms

Analysis

Summary

Passive–Aggressive Mean Reversion

Preliminaries

Formulations

Algorithms

Analysis

Summary

Confidence-Weighted Mean Reversion

Preliminaries

Formulations

Algorithms

Analysis

Summary

Online Moving Average Reversion

Preliminaries

Formulations

Algorithms

Analysis

Summary

IV: Empirical Studies

Implementations

The OLPS Platform

Data

Setups

Performance Metrics

Summary

Empirical Results

Experiment 1: Evaluation of Cumulative Wealth

Experiment 2: Evaluation of Risk and Risk-Adjusted Return

Experiment 3: Evaluation of Parameter Sensitivity

Experiment 4: Evaluation of Practical Issues

Experiment 5: Evaluation of Computational Time

Experiment 6: Descriptive Analysis of Assets and Portfolios

Summary

Threats to Validity

On Model Assumptions

On Mean Reversion Assumptions

On Theoretical Analysis

On Back-Tests

Summary

V: Conclusion

Conclusions

Future Directions

Appendix A: OLPS: A Toolbox for Online Portfolio Selection

Introduction

Framework and Interfaces

Strategies

Summary

Appendix B: Proofs and Derivations

Proof of CORN

Derivations of PAMR

Derivations of CWMR

Derivation of OLMAR

Appendix C: Supplementary Data and Portfolio Statistics

Bibliography

Index

✦ Subjects

Economics, Finance, Business & Industry;Finance;Engineering & Technology;Systems & Control Engineering;Machine Learning;Mathematics & Statistics;Statistics & Probability;Statistics;Statistics for Business, Finance & Economics