Direction-of-change forecasting in commodity futures markets

Jiadong Liu; Fotis Papailias; Barry Quinn

doi:10.1016/j.irfa.2021.101677

Direction-of-change forecasting in commodity futures markets

Jiadong Liu^*, Fotis Papailias, Barry Quinn

^*Corresponding author for this work

Queen's Business School (QBS)

Research output: Contribution to journal › Article › peer-review

113 Downloads (Pure)

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 language	English
Article number	101677
Journal	International Review of Financial Analysis
Volume	74
Early online date	03 Feb 2021
DOIs	https://doi.org/10.1016/j.irfa.2021.101677
Publication status	Published - 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

Access to Document

10.1016/j.irfa.2021.101677Licence: Unspecified

Direction-of-Change Forecasting in Commodity Futures Markets
© 2021 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/,which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited.
Accepted author manuscript, 550 KBLicence: CC BY-NC-ND

Cite this

@article{70620bd7f36a4a7a9297d5390274408e,

title = "Direction-of-change forecasting in commodity futures markets",

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 (B{\"u}hlmann & 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.",

keywords = "Direction-of-change, Dynamic probit model, Forecasting commodity futures, Return signal momentum, Variable length Markov chain",

author = "Jiadong Liu and Fotis Papailias and Barry Quinn",

year = "2021",

month = mar,

doi = "10.1016/j.irfa.2021.101677",

language = "English",

volume = "74",

journal = "International Review of Financial Analysis",

issn = "1057-5219",

publisher = "Elsevier Inc.",

}

TY - JOUR

T1 - Direction-of-change forecasting in commodity futures markets

AU - Liu, Jiadong

AU - Papailias, Fotis

AU - Quinn, Barry

PY - 2021/3

Y1 - 2021/3

N2 - 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.

AB - 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.

KW - Direction-of-change

KW - Dynamic probit model

KW - Forecasting commodity futures

KW - Return signal momentum

KW - Variable length Markov chain

U2 - 10.1016/j.irfa.2021.101677

DO - 10.1016/j.irfa.2021.101677

M3 - Article

AN - SCOPUS:85101172541

VL - 74

JO - International Review of Financial Analysis

JF - International Review of Financial Analysis

SN - 1057-5219

M1 - 101677

ER -

Direction-of-change forecasting in commodity futures markets

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this