Direction-of-change forecasting in commodity futures markets

Jiadong Liu*, Fotis Papailias, Barry Quinn

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This paper examines direction-of-change predictability in commodity futures markets using a variety of binary probabilistic techniques. As well as traditional techniques, we apply Variable Length Markov Chain (VLMC) analysis, an innovative technique popularised in computational biology when predicting DNA sequences (Bühlmann & Wyner, 1999). To the best of our knowledge, this is the first application of VLMC in finance. Our results show that both VLMC and technical analysis methods provide strong predictability of the direction-of-change of commodity returns, with annualised mean returns of approximately 8%, substantially higher than the passive long strategy. Our results suggest that a short-term learning effect is present in commodities market which can be exploited using innovative direction-of-change forecasting techniques.

Original languageEnglish
Article number101677
JournalInternational Review of Financial Analysis
Volume74
Early online date03 Feb 2021
DOIs
Publication statusPublished - Mar 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Inc.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Direction-of-change
  • Dynamic probit model
  • Forecasting commodity futures
  • Return signal momentum
  • Variable length Markov chain

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics

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