Anglia Ruskin Research Online (ARRO)
Browse

Using Neural Network Architectures for Intraday Trading in the Gold Market

Download (1.07 MB)
conference contribution
posted on 2023-08-30, 20:33 authored by Srinivas Devarajula, Vitaliy Milke, Cristina Luca
Financial market forecasting is used to assess the future value of financial instruments in various exchange and over-the-counter markets. Investors have a high interest in the most accurate prediction of the financial instruments’ prices. Inaccurate forecasting might result in a significant financial loss in certain circumstances. This research aims to determine the most probabilistic deep learning model that can improve price forecasting in the financial markets. In this research, Convolutional Neural Networks and Long Short-Term Memory are used for the experiments to forecast the Gold price movements on the Forex market. The Gold(XAU/USD) dataset is used in this research to predict the prices for the next minute. The models proposed have been evaluated using Mean squared error, Mean absolute error, and Mean absolute percentage error metrics. The results show that the Convolutional Neural Network has performed better than the Long Short-Term Memory network and has the potential to (More)

History

Page range

885-892

ISSN

2184-433X

Publisher

SCITEPRESS - Science and Technology Publications

ISBN

978-989-758-623-1

Conference proceeding

Proceedings of the 15th International Conference on Agents and Artificial Intelligence

Name of event

15th International Conference on Agents and Artificial Intelligence

Event start date

2023-02-22

Event finish date

2023-02-24

File version

  • Accepted version

Language

  • eng

Legacy posted date

2023-05-19

Legacy creation date

2023-05-19

Legacy Faculty/School/Department

Faculty of Science & Engineering

Usage metrics

    ARU Outputs

    Categories

    No categories selected

    Keywords

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC