Title page for etd-0621118-142449


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URN etd-0621118-142449
Author Jia-Hong Liu
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2017
Semester 2
Degree Master
Type of Document
Language English
Title Portfolio Investment Based on Neural Networks
Date of Defense 2018-07-30
Page Count 56
Keyword
  • neural network
  • stock investment
  • gene expression programming
  • convolutional neural network
  • portfolio
  • Abstract In this thesis, we combine the trading signals generated by the gen expression programming (GEP) method of Lee et al. and the portfolio generated by convolutional neural network (CNN) structure of Jiang et al. to form a stock investment method with portfolio management.
    The method of Jiang et al. focuses on the investment of the cryptocurrency. We change the invested target of Jiang et al. from cryptocurrency to stocks. We recompute the weights of the portfolio when the method of Lee et al. generates a trading signal (buy or sell). To test our method, we choose 213 stocks which always exist during 1995/1/5 to 2017/12/29 on stock market in Taiwan. Our training period starts from 1995/1/5. We perform the trading from 2002/1/2 until 2017/12/29.
    There are three cases in our experiments: Trading 100 stocks with the 100-stock features, trading 100 stocks with the 213-stock features, and trading 213 stocks with the 213-stock features. The annualized returns for the three cases are 25.00%, 26.52% and 27.32%, respectively. Our method is better than the buy-and-hold 12.36% for 100 stocks, and 12.21% for 213 stocks. Our method is also better than the method of Lee et al. without portfolio management 12.94% for 100 stocks, and 12.67% for 213 stocks.
    Advisory Committee
  • Shih-Chung Chen - chair
  • Chiou-Yi Hor - co-chair
  • Chien-Feng Huang - co-chair
  • Chang-Biau Yang - advisor
  • Files
  • etd-0621118-142449.pdf
  • Indicate in-campus at 0 year and off-campus access at 1 year.
    Date of Submission 2018-07-21

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