The Empirical Research of Trading Strategies for Convolutional Neural Network and Portfolio Theory on Taiwan Stock Market

• Main Authors: Chuang, Cheng-Hsun, 莊承勳
• Other Authors: Liao, Szu-Lang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/5774t5

碩士 === 國立政治大學 === 金融學系 === 107 === This Research selects 49 companies from the top 60 companies in Taiwan as a sample, collects stock data from 2006 to 2018. Choose technical indicators as variables, and use convolutional neural network prediction as a stock selection strategy to form a portfolio. In the selected stocks, the “Mean-Variance Analysis” is used to allocate the asset weights, and different investment groups are constructed according to different risk aversion levels. The result of this study shows that: the investment strategy of the convolutional neural network is quite good during the training period (2010~2016) of data. However, the strategy make negative return during the out-of-sample period (2008-2009, 2017~2018). With this performance, compare to a simple momentum strategy, the momentum portfolio can perform better during the out-of-sample period.