The University of Southampton
University of Southampton Institutional Repository

Bridging the divide in financial market forecasting: machine learners vs. financial economists

Bridging the divide in financial market forecasting: machine learners vs. financial economists
Bridging the divide in financial market forecasting: machine learners vs. financial economists
Financial time series forecasting is a popular application of machine learning methods. Previous studies report that advanced forecasting methods predict price changes in financial markets with high accuracy and that profit can be made trading on these predictions. However, financial economists point to the informational efficiency of financial markets, which questions price predictability and opportunities for profitable trading. The objective of the paper is to resolve this contradiction. To this end, we undertake an extensive forecasting simulation, based on data from thirty-four financial indices over six years. These simulations confirm that the best machine learning methods produce more accurate forecasts than the best econometric methods. We also examine the methodological factors that impact the predictive accuracy of machine learning forecasting experiments. The results suggest that the predictability of a financial market and the feasibility of profitable model-based trading are significantly influenced by the maturity of the market, the forecasting method employed, the horizon for which it generates predictions and the methodology used to assess the model and simulate model-based trading. We also find evidence against the informational value of indicators from the field of technical analysis. Overall, we confirm that advanced forecasting methods can be used to predict price changes in some financial markets and we discuss whether these results question the prevailing view in the financial economics literature that financial markets are efficient.
0957-4174
215-234
Hsu, Ming-Wei
c4e0d0b5-7768-4f90-9569-f05e43ac8ef9
Lessmann, Stefan
3b9f8133-67bb-4bcc-9183-e1a5db294b01
Sung, Ming-Chien
2114f823-bc7f-4306-a775-67aee413aa03
Ma, Tiejun
1f591849-f17c-4209-9f42-e6587b499bae
Johnson, Johnnie
6d9f1a51-38a8-4011-a792-bfc82040fac4
Hsu, Ming-Wei
c4e0d0b5-7768-4f90-9569-f05e43ac8ef9
Lessmann, Stefan
3b9f8133-67bb-4bcc-9183-e1a5db294b01
Sung, Ming-Chien
2114f823-bc7f-4306-a775-67aee413aa03
Ma, Tiejun
1f591849-f17c-4209-9f42-e6587b499bae
Johnson, Johnnie
6d9f1a51-38a8-4011-a792-bfc82040fac4

Hsu, Ming-Wei, Lessmann, Stefan, Sung, Ming-Chien, Ma, Tiejun and Johnson, Johnnie (2016) Bridging the divide in financial market forecasting: machine learners vs. financial economists. Expert Systems with Applications, 61, 215-234. (doi:10.1016/j.eswa.2016.05.033).

Record type: Article

Abstract

Financial time series forecasting is a popular application of machine learning methods. Previous studies report that advanced forecasting methods predict price changes in financial markets with high accuracy and that profit can be made trading on these predictions. However, financial economists point to the informational efficiency of financial markets, which questions price predictability and opportunities for profitable trading. The objective of the paper is to resolve this contradiction. To this end, we undertake an extensive forecasting simulation, based on data from thirty-four financial indices over six years. These simulations confirm that the best machine learning methods produce more accurate forecasts than the best econometric methods. We also examine the methodological factors that impact the predictive accuracy of machine learning forecasting experiments. The results suggest that the predictability of a financial market and the feasibility of profitable model-based trading are significantly influenced by the maturity of the market, the forecasting method employed, the horizon for which it generates predictions and the methodology used to assess the model and simulate model-based trading. We also find evidence against the informational value of indicators from the field of technical analysis. Overall, we confirm that advanced forecasting methods can be used to predict price changes in some financial markets and we discuss whether these results question the prevailing view in the financial economics literature that financial markets are efficient.

Text
Full paper for eprints.docx - Accepted Manuscript
Restricted to Repository staff only
Request a copy
Text
Johnnie Johnson Paper - Accepted Manuscript
Download (506kB)
Text
Johnnie Johnson Appendix - Accepted Manuscript
Download (149kB)
Text
__soton.ac.uk_UDE_PersonalFiles_Users_sb1u11_mydocuments_Academic Papers EPrints & REF_Johnnie Johnson_Figures.pdf - Accepted Manuscript
Download (23kB)
Text
__soton.ac.uk_UDE_PersonalFiles_Users_sb1u11_mydocuments_Academic Papers EPrints & REF_Johnnie Johnson_Research Highlights a.docx - Accepted Manuscript
Download (14kB)
Text
__soton.ac.uk_UDE_PersonalFiles_Users_sb1u11_mydocuments_Academic Papers EPrints & REF_Johnnie Johnson_Tables.pdf - Accepted Manuscript
Download (189kB)

Show all 6 downloads.

More information

Accepted/In Press date: 19 May 2016
e-pub ahead of print date: 25 May 2016
Published date: 1 November 2016
Organisations: Centre of Excellence in Decision, Analytics & Risk Research

Identifiers

Local EPrints ID: 395176
URI: https://eprints.soton.ac.uk/id/eprint/395176
ISSN: 0957-4174
PURE UUID: 3d42fce6-d349-47a9-9f31-42ecf060ec5f
ORCID for Ming-Chien Sung: ORCID iD orcid.org/0000-0002-2278-6185

Catalogue record

Date deposited: 25 May 2016 15:28
Last modified: 03 Dec 2019 06:42

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×