Forecasting Interaction of Exchange Rates between Fiat Currencies and Cryptocurrencies Based on Deep Relation Networks

Chiao Ting Chen, Lin Kuan Chiang, Yi Cheng Huang, Szu Hao Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Forecasting exchange rates is difficult because financial time-series data is too complicated to analyze. In traditional financial studies, economic models and statistic approaches were widely used for predicting exchange rates. Recently, machine learning and deep learning techniques have played increasingly important roles in financial technology studies. This study adopts a deep learning technique called relation networks (RNs) to predict the exchange rates of fiat currencies and cryptocurrencies. To discover the relationship among different currencies, the concept of visual question answering (VQA) is applied in RNs. We also propose a specially designed architecture for the feature extraction stage to consider both spatial and temporal relationships simultaneously. The experimental results show that the proposed approach can achieve higher prediction performance for cryptocurrencies with approximately 65% accuracy rate. We aim to improve traditional approaches and construct a model using the concept of VQA based on RNs to optimize the prediction performance between fiat currencies and cryptocurrencies.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Agents, ICA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages69-72
Number of pages4
ISBN (Electronic)9781728140261
DOIs
StatePublished - Oct 2019
Event2019 IEEE International Conference on Agents, ICA 2019 - Jinan, China
Duration: 18 Oct 201921 Oct 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Agents, ICA 2019

Conference

Conference2019 IEEE International Conference on Agents, ICA 2019
CountryChina
CityJinan
Period18/10/1921/10/19

Keywords

  • deep learning
  • exchange rates
  • relation networks
  • visual question answering

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  • Cite this

    Chen, C. T., Chiang, L. K., Huang, Y. C., & Huang, S. H. (2019). Forecasting Interaction of Exchange Rates between Fiat Currencies and Cryptocurrencies Based on Deep Relation Networks. In Proceedings - 2019 IEEE International Conference on Agents, ICA 2019 (pp. 69-72). [8929155] (Proceedings - 2019 IEEE International Conference on Agents, ICA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AGENTS.2019.8929155