從2020年疫情開始，美聯儲的量化寬鬆政策以及投資者的低成本資金引爆了股市與加密貨幣投資熱潮。然而，隨著2022年通膨上升，美聯儲開始進行連續升息，導致各種資產面臨價格下跌的壓力。特別是加密貨幣市場，經歷一系列黑天鵝事件後，市值蒸發了約1.5兆美元。至2023年，儘管通膨得到控制，市場活動逐步恢復，投資者對加密貨幣市場的疑慮依然存在。
　　為了幫助投資者理解加密貨幣市場的風險和特性，本研究旨在探討加密貨幣報酬率的共性，以協助投資者識別具有特定特徵的加密貨幣所帶來的潛在投資機會。我們利用Barra's EUE3模型建立了多因子風險模型，考慮了規模、動能、波動性、流動性和網絡中心性等因素，並根據CoinDesk Indices的數位資產分類標準，分析了2017年至2022年的所有合格幣種的報酬率，以評估投資組合風險和驗證模型的風險預測能力。結果顯示，市值加權模型在預測報酬率方面的準確度接近90%，確認了多因子模型能夠有效把握加密貨幣市場風險。　
　　在實際運用加密貨幣因子投組上，我們採用的是核心-衛星投資策略，以期在控制風險的同時，創造更好的績效。我們發現規模較小、流動性較差和中位數網絡中心性的加密貨幣組成的多因子組合表現最佳，Sharpe Ratio 達到 2.5。總的來說，我們透過這樣的全面方法，期望能深入瞭解加密貨幣市場的風險，並探討如何利用風險因子來建立成功的投資策略。

Since the 2020 pandemic, the Federal Reserve's quantitative easing and investors' low-cost funds spurred a surge in stocks and cryptocurrency. However, 2022's inflation rise and subsequent interest rate hikes led to decreased asset values, notably causing a loss of about $1.5 trillion in cryptocurrency market value.
This research aims to help investors understand the risks and features of the cryptocurrency market. We used Barra's EUE3 model to construct a multi-factor risk model considering factors such as size, momentum, volatility, liquidity, and network centrality. We analyzed the returns of eligible cryptocurrencies from 2017 to 2022, finding that the market value-weighted model accurately predicted returns by nearly 90%.
In practice, we implemented a core-satellite investment strategy and found that cryptocurrencies with smaller size, poorer liquidity, and median network centrality performed best. Through this approach, we aim to comprehend cryptocurrency market risks and establish effective investment strategies.

論文審定書 i
摘要 ii
Abstract iii
Content iv
List of Figures v
List of Tables viii
I. Introduction 1
1.1 Background and Motivation for Research 1
1.2 Research Purpose 6
II. Literature Review 8
2.1 Modern Portfolio Theory and Multi-Factor Model 8
2.2 Factors of Cryptocurrency 10
2.3 Introduction to Risk Parity Portfolio 15
III. Research Methodology 18
3.1 Empirical Process 18
3.2 Estimation Universe (ESTU) and Research Period 19
3.3 Definition and Calculation of Factor Exposures 21
3.4 Multi-risk factor Model Construction 25
3.5 The Risk Parity Portfolio 36
3.6 Core-Satellite Strategy 40
IV. Empirical Results 44
4.1 Descriptive statistics of Data and Factors 44
4.2 Forecasting Accuracy Test Results 62
4.3 Performance of Core-Satellite Portfolios 68
V. Conclusions and Suggestions 98
References 103
Appendix 107

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