TY - GEN
T1 - Forecasting Taiwan Capitalization Weighted Stock Index by Using Convolutional Neural Network
AU - Liao, Chia Hung
AU - Kao, Te Lun
AU - Yuan, Shyan Ming
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/10/23
Y1 - 2020/10/23
N2 - 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.
AB - 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.
KW - Convolution Neural Network
KW - Gramian Angular Field
KW - Stock Index Prediction
KW - Time Series Analysis
UR - http://www.scopus.com/inward/record.url?scp=85099558310&partnerID=8YFLogxK
U2 - 10.1109/ECICE50847.2020.9301956
DO - 10.1109/ECICE50847.2020.9301956
M3 - Conference contribution
AN - SCOPUS:85099558310
T3 - 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020
SP - 326
EP - 329
BT - 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020
A2 - Meen, Teen-Hang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020
Y2 - 23 October 2020 through 25 October 2020
ER -