Version 1
: Received: 17 December 2019 / Approved: 19 December 2019 / Online: 19 December 2019 (07:39:54 CET)
How to cite:
Hailesilassie, T. Financial Market Prediction Using Recurrence Plot and Convolutional Neural Network. Preprints2019, 2019120252. https://doi.org/10.20944/preprints201912.0252.v1
Hailesilassie, T. Financial Market Prediction Using Recurrence Plot and Convolutional Neural Network. Preprints 2019, 2019120252. https://doi.org/10.20944/preprints201912.0252.v1
Hailesilassie, T. Financial Market Prediction Using Recurrence Plot and Convolutional Neural Network. Preprints2019, 2019120252. https://doi.org/10.20944/preprints201912.0252.v1
APA Style
Hailesilassie, T. (2019). Financial Market Prediction Using Recurrence Plot and Convolutional Neural Network. Preprints. https://doi.org/10.20944/preprints201912.0252.v1
Chicago/Turabian Style
Hailesilassie, T. 2019 "Financial Market Prediction Using Recurrence Plot and Convolutional Neural Network" Preprints. https://doi.org/10.20944/preprints201912.0252.v1
Abstract
An application of deep convolutional neural network and recurrence plot for financial market movement prediction is presented. Though it is challenging and subjective to interpret its information, the pattern formed by a recurrence plot provide a useful insight into the dy- namical system. We used a recurrence plot of seven financial time series to train a deep neural network for financial market movement predic- tion. Our approach is tested on our dataset and achieved an average of 53.25% classification accuracy. The result suggests that a well trained deep convolutional neural network can learn a recurrence plot and pre- dict a financial market direction.
Keywords
time series; deep learning; convolutional neural network; recurrence plot; financial market prediction
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.