基於共同基金持股偏好之投資組合建構 ─使用結合注意力機制與張量分解之LSTM模型

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研究生：	程彥瑋 Yan-Wei Cheng
論文名稱：	基於共同基金持股偏好之投資組合建構 ─使用結合注意力機制與張量分解之LSTM模型 Portfolio Construction based on Mutual Fund Holdings Using LSTM Model with Attention Mechanism and Tensor 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：	2020
畢業學年度：	108
語文別：	英文
論文頁數：	49
中文關鍵詞：	推薦系統、投資組合預測、遞迴神經網絡、張量分解、注意力機制
外文關鍵詞：	Recommendation System, Portfolio Prediction, Recurrent Neural Network, Tensor Factorization, Attention mechanism
相關次數：	點閱：384 下載：0
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上一筆

在當今社會，許多人將部分薪水用於投資股票或基金，以獲取額外收入。但是，許多不專業或剛剛開始投資的散戶投資者沒有足夠的經驗來決定正確的投資。這將導致散戶投資者在投資時無利可圖。
台灣有數百隻基金，其中包括指數基金和共同基金。共同基金是一種將許多投資者的資金匯集在一起的融資方式，由專業機構管理，通過專業經理掌握投資時機以追求穩定的利潤。但是由於共同基金的高交易成本，頻繁的投資導致過多的投資成本。對於散戶投資者而言，購買股票將是一種低風險的方法。在台灣股票市場，投資者可以在收盤後購買1,000股或更少的股票，這使得投資更加靈活便捷。
通過對共同基金的研究，我們發現某些基金的每月總回報率始終穩定。因此，如果有一個模型可以預測下個月的獲利基金持有量，散戶投資者將獲得更穩定的投資結果。
在這項研究中，我們使用《台灣經濟日報》（TEJ）提供的資金持有信息來觀察2016年至2017年每個月的資金持有偏好的變化，預測並驗證2018年每個月的投資組合建設。使用張量分解可以提高訓練數據的相關性，並達到較好的模型訓練效果。另外，使用具有註意機制的長期短期記憶神經網絡（LSTM）來構建模型。根據本研究的實驗結果，使用本研究提出的模型生成的預測投資組合比2018年台灣指數（TAIEX）的年化收益高37％。

In today’s society, many people will use part of their salary to invest in stocks or funds, in order to get extra revenue. However, many retail investors who are not professional or have just started investing do not have enough experience to decide right investment. This will cause retail investors to be unprofitable when investing.
There are hundreds of funds in Taiwan which contain index funds and mutual funds. Mutual funds are a kind of financing method that brings together the funds of many investors and is managed by professional institutions, through professional manager to grasp the timing of investing in order to pursue stable profits. But due to the high transaction costs of mutual funds, frequent investment leads to excessive investment costs. For retail investors, buying stocks will be a low-risk approach. In Taiwan stock market, investors can buy one thousand shares or less after closing, which makes investment more flexible and convenient.
Through research on mutual funds, we found that the total monthly return rates are always stable on certain funds. Consequently, if there is a model that can predict the holdings of profitable funds next month, retail investors will have more stable investment results.
In this study, we use the holding information of funds provided by Taiwan Economic Journal (TEJ) to observe the changes in the holding preferences of funds of each month from 2016 to 2017, predicting and verifying the portfolio construction of each month of 2018. We used tensor factorization improves the relevance of training data and achieves a better model training effect. In addition, used a long-term short-term memory neural network (LSTM) with attention mechanism to build the model. According to the experimental results of this study, the predicted portfolio that generated using the model proposed by this research is 37% higher than Taiwan Index (TAIEX) annualized return in 2018.

ABSTRACT    i
ACKNOWLEDGEMENT    ii
TABLE OF CONTENTS    iii
LIST OF FIGURES    v
LIST OF TABLES    vi
Chapter 1. Introduction    1
1. Background    1
2. Motivation    1
3. Purpose    2
Chapter 2. Related Work    4
1. Mutual Fund    4
2. Recommendation System    5
2.1.    Collaborative Filtering    5
3. Tensor Factorization    6
3.1.    CANDECOMP/PARAFAC Decomposition    7
3.2.    Tucker Decomposition    9
4. Recurrent Neural Network    10
4.1.    Long Short-Term Memory    11
5. Attention Mechanism    13
Chapter 3. Proposed Approach    14
1. Overview    14
2. Date Preprocessing    15
2.1.    Data Collection    15
2.2.    Data Assembly    16
3. Fund’s Feature Extraction    17
3.1.    Tensor Factorization    17
4. Model Establishment    19
4.1.    Long Short-Term Memory Network    20
4.2.    Attention Mechanism With LSTM    21
5. Evaluation Metric    22
5.1.    Mean-Square Error    23
5.2.    Profit Performance    23
Chapter 4. Experimental Result    25
1. Experimental Environment    25
2. Dataset Description    26
3. Experimental Variables    26
3.1.    Long Short-Term Memory Parameters    27
4. Evaluation of Prediction Models    27
5. Performance Comparisons    28
5.1.    Predict Portfolio by Top 1 Fund - T0906Y    29
5.2.    Predict Portfolio by Top n Funds    30
5.3.    Predict Portfolio by Short-Term Transaction    33
6. Discussion    34
Chapter 5. Conclusions and Future Research    35
1. Conclusions    35
2. Future Research    36
REFERENCES    37


                                

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全文公開日期 2025/08/21 (校內網路)
全文公開日期本全文未授權公開 (校外網路)
全文公開日期本全文未授權公開 (國家圖書館：臺灣博碩士論文系統)

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