Machine Learning and Algorithmic Pair Trading in Futures Markets

Machine Learning and Algorithmic Pair Trading in Futures Markets

Seungho Baek, Seok Hee Oh, Mina Glambosky, Jeong Lee

This study applies machine learning methods to develop a sustainable pairs trading market-neutral investment strategy across multiple futures markets. Cointegrated pairs with similar price trends are identified, and a hedge ratio is determined using an Error Correction Model (ECM) framework and support vector machine algorithm based upon the two-step Engle–Granger method. The study shows that normal backwardation and contango do not consistently characterize futures markets, and an algorithmic pairs trading strategy is effective, given the unique predominant price trends of each futures market. Across multiple futures markets, the pairs trading strategy results in larger risk-adjusted returns and lower exposure to market risk, relative to an appropriate benchmark. Backtesting is employed and results show that the pairs trading strategy may hedge against unexpected negative systemic events, specifically the COVID-19 pandemic, remaining profitable over the period examined.

2020

machine learning, futures prices, futures markets, backwardation, contango, cointegration pairs trading

10.3390/su12176791

Biotemas

Universidade Federal de Santa Catarina

Environmental effects of industries and plants, Renewable energy sources, Environmental sciences