Capsule network regression using information measures: an application in bitcoin market

Mahsa Tavakoli*, Hassan Doosti, Christophe Chesneau

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Predicting financial markets has always been one of the most challenging issues, attracting the attention of many investors and researchers. In this regard, deep learning methods have been used a lot recently. Due to the desired results, such networks are always in development and progress. One of the networks that is being implemented in various fields is capsule network. This relatively new network uses structures called capsules, which makes better results than competing networks such as Convolutional networks in the problems such as image processing. The first time the classification capsule network was introduced, it was able to attract a lot of attention with its success on MNIST 1data. In such networks, as in the other ones, the parameters are obtained by minimizing a loss function. In this paper, we first change the classification capsule network to a regression capsule network by modifying the last layer of the network. Then we use different information measures such as Kullnack-Leibler, Lin-Wang and Triangular information measures as a loss function, and compare their results with well known models including Artificial Neural Network (ANN), Convolutional Network (CNN) and Long Short-Term Memory (LSTM) as well as common used loss functions such as Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Using appropriate accuracy metrics, it is shown that the capsule network using triangular information measure is well able to predict the price of bitcoin for the medium and long term period including 10, 90 and 180 days. So its forecast accuracy is 94% in the long term and 64% in the medium term.
Original languageEnglish
Pages (from-to)37-48
Number of pages12
JournalAdvances in Mathematical Finance and Applications
Volume7
Issue number1
Publication statusPublished - Jan 2022

Keywords

  • Deep Learning
  • Financial Market
  • Prediction

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