Coal Price Prediction Using Financial Indices

Hu Hsiang Yeh, Min Te Sun

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The price movement prediction in the futures market is difficult due to fluctuating demands and supplies. This thesis addresses the problem of coal price movement prediction. The study compares two prediction models using two different datasets. The first dataset includes daily trading data, while the second dataset contains both daily trading data and computed financial indices. The data from Indonesia and Australia between 2010 and 2019 is used for the experiment. The experimental results show that the second model achieves higher accuracy. The market simulation also indicates that the second model enjoys a larger trade gain higher than 30% of the budget within a year.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728146669
DOIs
StatePublished - Nov 2019
Event24th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019 - Kaohsiung, Taiwan
Duration: 21 Nov 201923 Nov 2019

Publication series

NameProceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019

Conference

Conference24th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019
Country/TerritoryTaiwan
CityKaohsiung
Period21/11/1923/11/19

Keywords

  • Deep learning
  • financial indices
  • price prediction

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