Portfolio Construction based on Mutual Fund Holdings Using Recurrent Neural Network and Matrix Factorization

• Main Authors: Yuan-Chien Wu, 吳元傑
• Other Authors: Yung-Ho Leu
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/8n9ww8

碩士 === 國立臺灣科技大學 === 資訊管理系 === 107 === 　For many people, there is often petty cash on hand that they can do some investment, hoping to gain another way of income. However, the general retail investors usually do not have enough time and resources to conduct a detailed study on the stocks. As a result, most of the retail investors are always unable to make profits when investing in stocks. 　In Taiwan, there are more than 100 funds covering index funds and mutual funds. Each mutual fund relies on a professional investment manager to make good trading decisions for clients. It is possible to purchase an entire mutual fund as an investment, and frequent investment in mutual fund is costly because transaction costs are high. Therefore, direct investment in stocks is a better practice to most retail investors. For Taiwan stocks, many of them can buy one thousand shares or below after the market close, which also makes investment in stocks more flexible and convenient. 　Through researching funds, it can be found that the total monthly return rates of certain funds are always stable. Therefore, if we can predict these profitable funds’ holdings next month, and then invest in similar targets, we will have more stable investment results for retail investors. This study uses the funds’ holdings information provided by the Taiwan Economic Journal(TEJ) to study the changes in the funds’ holdings preferences from 2016 to 2017 for a total of 24 months to predict and verify the ideal portfolio for each month of 2018. In conjunction with the content of the time series, a Long Short-Term Memory neural network (LSTM) is used to build the model. In addition, the use of Matrix Factorization can make the selected targets more diverse while achieving the effect of dispersing risks. According to the experimental results of this study, the forecasted portfolio had a total of 34% higher than the annualized return rate of the Taiwan Index(TAIEX) for the year of 2018.