Paper Title :A Case Study of Stock Investment Based on Neural Network Techniques
Author :Muh-Cherng Wu, Chun-Tzu Chen, Hui-Chih Hung
Article Citation :Muh-Cherng Wu ,Chun-Tzu Chen ,Hui-Chih Hung ,
(2018 ) " A Case Study of Stock Investment Based on Neural Network Techniques " ,
International Journal of Management and Applied Science (IJMAS) ,
pp. 42-45,
Volume-4,Issue-5
Abstract : Stock price predictions based on data mining techniques have been widely examined. Most studies focused on
predicting the stock price or up/down in the next trading day. Considering a future time horizon (say, 100 days), this research
attempts to predict whether a day is a “buy-day”. A buy-day denotes a “good” day to purchase a particular stock if the stock
closing price is expected to rise over 10% in the coming 100 days. The “buy-day” decision is a binary classification problem,
which herein shall be solved by various artificial neural network (ANN) models. In an ANN model, the output involves two
classification states: “buy-day” or “not-buy-day”; and the input can involve up to 15 variables (i.e., features). By selecting
different portfolios of input features, three ANNs are established. The stock price ranging from Jan. 2007 to Dec. 2016 (10
years) of a Taiwanese company (Foxconn) is used as a test case. Numerical experiments reveal that the 3rd ANN outperforms
the other ANNs; and its average annual return of investment is about 10.92%.
Keywords - Binary classification, Data mining, Neural network, Stock price prediction
Type : Research paper
Published : Volume-4,Issue-5
DOIONLINE NO - IJMAS-IRAJ-DOIONLINE-12239
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Copyright: © Institute of Research and Journals
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Published on 2018-07-26 |
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