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研究生: 吳元傑
Yuan-Chien Wu
論文名稱: 基於共同基金持股偏好之投資組合建構──結合遞歸神經網路與矩陣分解之新方法
Portfolio Construction based on Mutual Fund Holdings Using Recurrent Neural Network and Matrix Factorization
指導教授: 呂永和
Yung-Ho Leu
口試委員: 楊維寧
Wei-Ning Yang
陳雲岫
Yun-Shiow Chen
學位類別: 碩士
Master
系所名稱: 管理學院 - 資訊管理系
Department of Information Management
論文出版年: 2019
畢業學年度: 107
語文別: 英文
論文頁數: 43
中文關鍵詞: 遞歸神經網絡投資組合預測矩陣分解
外文關鍵詞: Recurrent Neural Network, Portfolio Prediction, Matrix Factorization
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  •  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.


     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.

    ABSTRACT i ACKNOWLEDGEMENT ii TABLE OF CONTENTS iii LIST OF FIGURES v LIST OF TABLES vi Chapter 1. Introduction 1 1.1. Background 1 1.2. Motivation 1 1.3. Purpose 2 Chapter 2. Related Work 2 2.1. Mutual Fund 4 2.2. Recommendation System 4 2.2.1. Collaborative Filtering 4 2.3. Matrix Factorization 6 2.3.1. Alternating Least Squares 7 2.3.2. Stochastic Gradient Descent 7 2.4. Recurrent Neural Network 8 2.4.1. Long Short-Term Memory 9 Chapter 3. Proposed Approach 11 3.1. Overview 11 3.2. Date Preprocessing 11 3.2.1. Data Collection 12 3.2.2. Data Assembly 13 3.3. Fund’s Feature Extraction 14 3.3.1. Matrix Factorization 14 3.4. Model Establishment 15 3.4.1. Long Short-Term Memory Network 16 3.4.2. Combining Multiple Mutual Funds 17 3.5. Evaluation Metric 18 3.5.1. Mean-Square Error 18 3.5.2. Profit Performance 18 Chapter 4. Experimental Result 19 4.1. Experimental Environment 21 4.2. Dataset Description 22 4.3. Experimental Variables 22 4.3.1. Alternating Least Squares Parameters 22 4.3.2. Stochastic Gradient Descent Parameters 23 4.3.3. LSTM Parameters 23 4.4. Evaluation of Prediction Models 25 4.5. Performance Comparisons 26 4.5.1. Predict Portfolio by Top 1 Fund - T0906Y 26 4.5.2. Predict Portfolio by Top n Funds 27 4.6. Discussion 28 Chapter 5. Conclusions and Future Research 28 5.1. Conclusions 30 5.2. Future Research 30 REFERENCES 32

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