Forecasting Taiwan Capitalization Weighted Stock Index by Using Convolutional Neural Network

Chia Hung Liao, Te Lun Kao, Shyan Ming Yuan*

*Corresponding author for this work

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

Abstract

Deep learning has been widely used in many research areas recently. One of the common applications is financial forecasting. Convolutional neural network (CNN), a class of deep learning, which is capable of capturing complex features from the images. In this paper, we proposed a method for forecasting Taiwan capitalization weighted stock index by using Xception, a CNN presented by Chollet, F from Google. Furthermore, with the labeling technique we proposed which aims to predict the median of the closing price of future 20, 30, or 40 days. Based on the results produced by each model, we propose a method to find the optimal trading strategy. Then, after the simulation based on trading the common ETFs in Taiwan which includes 0050.TW, 0056.TW, the promising results show that we can make more profits than those traditional trading strategies such as Buy and Hold and some technical indicators.

Original languageEnglish
Title of host publication2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020
EditorsTeen-Hang Meen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages326-329
Number of pages4
ISBN (Electronic)9781728180601
DOIs
StatePublished - 23 Oct 2020
Event2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020 - Yunlin, Taiwan
Duration: 23 Oct 202025 Oct 2020

Publication series

Name2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020

Conference

Conference2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020
CountryTaiwan
CityYunlin
Period23/10/2025/10/20

Keywords

  • Convolution Neural Network
  • Gramian Angular Field
  • Stock Index Prediction
  • Time Series Analysis

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