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Author: 黃多睿
Do-Re Huang
Thesis Title: 美國倉儲業投資信託績效表現與三柵交易法應用導入
The Performance of U.S. Warehouse REITs and Implementation of Triple Barrier Trading Strategy.
Advisor: 張光第
Guang-Di Chang
Committee: 謝劍平
Joseph C.P. Shieh
Chun-Nan Chen
Day-Yang Liu
Degree: 碩士
Department: 管理學院 - 財務金融研究所
Graduate Institute of Finance
Thesis Publication Year: 2022
Graduation Academic Year: 110
Language: 英文
Pages: 29
Keywords (in Chinese): 倉儲業房地產投資信託基金投資組合機器學習
Keywords (in other languages): Warehouse, REITs, portfolio optimization, machine learning
Reference times: Clicks: 245Downloads: 6
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  • 本研究在於探討美國倉儲產業 REITs 之異常報酬現象是否存在,以及針對前述現象 之分析結果導入投資策略並分析其模擬交易績效,並提出相關投資建議。研究中針 對 2012 年 1 月至 2021 年 12 月之資料,使用 Granger Causality test 、 Augmented Dickey-Fuller test 、以及 Long Short-Term Memory model 檢驗其時間序列特徵並進 行建模。本研究之結論為:(1)美國倉儲產業 REITs 為相當穩健之投資標的,其 研究區間內不存在顯著之異常報酬,(2)由美國倉儲產業 REITs 所組成之投資組 合在適當交易策略的配合下,可以提供高於市場報酬之投資選項。

    This study is aimed to investigate if the U.S. warehouse REITs and self-storage REITs contain abnormal returns or high predictability in returns from January 2012 to December 2021. We apply Granger’s Causality Test, Augmented Dickey-Fuller test, Long Short- Term Memory model, to examine the general statistical characteristics and time series properties of the six selected REITs. The empirical results show that U.S. warehouse REITs contain a high degree of stability and predictability in the observing period. Based on the results, we conclude that the U.S. warehouse REITs, bundled into a portfolio, provide a new instance for proactive hedging with lower risk.

    摘要 Abstract List of Figures List of Tables 1. Introduction 2. Literature Review 3. Data and Methodology 3.1 Data 3.2 Methodology 3.2.1 Granger Causality test 3.2.2 Augmented Dickey-Fuller test 3.2.3 Long Short-Term Memory model 3.2.4 Triple Barrier strategy 4. Empirical Result 4.1 Result from Augmented Dickey-Fuller test 4.2 Result of Granger Causality test 4.3 Result from Long Short-Term Memory model 4.4 Result from Triple Barrier strategy 5.Conclusion 6.Reference

    1. Benya, M. B. (2021). Supervised Learning - Predictive AI Research for REIT tasks.
    2. Beracha, E. (2019). REIT Operational Efficiency: Performance, Risk, and Return. The
    Journal of Real Estate Finance and Economics, 58, 408-437.
    3. Cheok, S. M. (2011). Diversification as a Value-Adding Strategy for Asian REITs: A
    Myth or Reality?. International Real Estate Review, 14(2), 184-207.
    4. Deng,Y.L.(2016).TheRoleofDebtCovenantsintheInvestmentGradeBondMarket – The REIT Experiment. The Journal of Real Estate Finance and Economics, 52,
    5. Lee, Y. H. (2010). REIT volatility prediction for skew-GED distribution of the
    GARCH model. Expert Systems with Applications, 37(7), 4737-4741.
    6. Li, Y. (2008). Market Timing and REIT Capital Structure Changes.
    7. Mita, D., Nishimura, K. G. (2022). A Dynamic Analysis of the Bank of Japan's ETF/
    REIT Purchase Program. SSRN, 26.
    8. Mueller, G. (2020). Warehouse Demand and the Path of Goods Movement. Journal of
    Real Estate Portfolio Management, 13(1), 45-46.
    9. Mule, R. K., Mukras, M. S. (2015). Capital Structure, Ownership Structure and Firm
    Value: An Econometric Panel Analysis of Firms Listed in Kenya.
    10.Newell, G. (2016). The significance and performance of UK-REITs in a mixed-asset
    portfolio. Journal of European Real Estate Research, 9(2), 171-182.
    11.Packer, F. (2013). Securitization and the Supply Cycle: Evidence from the REIT
    Market. The Journal of Portfolio Management, 39(5), 134-143.
    12.Poerwati, T. (2020). Does the Company's Scale, Fixed Asset Intensity and Operating
    Cash Flow Affect Asset Revaluation? Jurnal Analisis Bisnis Ekonomi, 18(1), 149-178.