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研究生: 何其達
Chi-Ta Ho
論文名稱: 優化美國跨產業不動產投資信託投資組合績效表現和蒙地卡羅模擬法的應用
Optimizing Portfolio Performance of U.S. Cross-industry REITs and Application of Monte Carlo Simulation
指導教授: 謝劍平
Joseph C.P. Shieh
口試委員: 劉代洋
Day-Yang Liu
張琬喻
Woan-Yuh Jang
陳俊男
Chun-Nan Chen
學位類別: 碩士
Master
系所名稱: 管理學院 - 財務金融研究所
Graduate Institute of Finance
論文出版年: 2023
畢業學年度: 111
語文別: 英文
論文頁數: 38
中文關鍵詞: 多元化不動產投資信託基金優化投資組合馬可維茲蒙地卡羅模擬法
外文關鍵詞: Diversified, REITs, Portfolio optimization, Markowitz, Monte Carlo Simulation
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  • 本研究致力於優化跨產業不動產投資信託的投資組合績效,並嘗試在風險固定的情 況下設計回報最高的投資策略。本研究的數據觀察區間為 2012 年 1 月 1 日至 2021 年 12 月 31 日。我們使用效率前緣上的投資組合搭配蒙地卡羅模擬法來測試本次研究 中所選之不動產投資信託的績效表現。結果顯示,美國跨產業不動產投資信託投資組合 搭配效率前緣的應用,在蒙地卡羅模擬法的測試中,其表現高於平均買入策略的績效。 儘管整個市場受到投資情緒不樂觀的影響,不動產投資信託仍然證明其本身就是一個優 秀的被動投資標的。如果投資者選擇最大夏普比率的策略,其投資組合累積回報率為平 均買入策略的 1.262 倍,並在相同風險下實現收益最大化。


    This study aims to explore the portfolio performance of cross-industry real estate investment trusts (REITs) and attempts to design an investment strategy with the highest return for a fixed risk. The data observation period for this study spans from January 1, 2012 to December 31, 2021. We use an efficient frontier portfolio with Monte Carlo simulation to test the performance of the REITs selected for this study. The results show that the U.S. cross- industry REIT portfolio with the efficient frontier outperformed the average buy strategy in the Monte Carlo simulation. REITs have proven to be an excellent passive investment target despite the unoptimistic investment sentiment across the market. If an investor chooses a strategy with the maximum Sharpe ratio, the cumulative return on the portfolio is 1.262 times the market average cumulative return on the portfolio, and the return is maximized under the same risk.

    摘要 ................................................................................ I ABSTRACT............................................................................ II ACKNOWLEDGEMENT.................................................................... III LIST OF FIGURES..................................................................... VI LIST OF TABLES .................................................................... VII CHAPTER 1 INTRODUCTION............................................................... 1 CHAPTER 2 LITERATURE REVIEW.......................................................... 3 2.1 Characteristics and Performance of REITs......................................... 3 2.1.1 The Initial Development of U.S. REITs ......................................... 3 2.1.2 The Relationship between the REITs Index and the Direct Real Estate ........... 3 2.1.3 Diversified Portfolio of REITs ................................................ 4 2.2 Application of Efficient Frontier................................................ 4 2.2.1 Optimal Portfolios............................................................. 4 2.3 Effect of REITs on Forecasting .................................................. 5 2.3.1 The Main Factors Affecting the Profitability of the REIT Industry.............. 5 2.3.2 Application of Machine Learning in the REIT Industry .......................... 6 2.3.3 Application of Monte Carlo Simulation in Optimizing Investment Portfolio ...... 6 CHAPTER 3 DATA AND METHODOLOGY....................................................... 7 3.1 Data............................................................................. 7 3.2 Methodology..................................................................... 11 3.2.1 Markowitz’s Mean-Variance Portfolio Theory.................................... 11 3.2.2 Monte Carlo Simulation ....................................................... 11 3.2.3 Johansen's Cointegration Test ................................................ 12 3.2.4 Granger Causality Test........................................................ 14 3.2.5 Augmented Dickey-Fuller Test ................................................. 15 CHAPTER 4 EMPIRICAL RESULTS ........................................................ 16 4.1 Result of Augmented Dickey-Fuller Test ......................................... 16 4.2 Result of Granger Causality Test................................................ 17 4.3 Result of Efficient Frontier.................................................... 19 4.4 Monte Carlo Simulation.......................................................... 20 4.4.1 Correlation Analysis of Investment Portfolio.................................. 22 CHAPTER 5 CONCLUSIONS............................................................... 24

    Akinsomi, O., Aye, G. C., Babalos, V., Economou, F., & Gupta, R. (2016). Real estate returns predictability revisited: novel evidence from the US REITs market. Empirical Economics, 51(3), 1165-1190.
    Baum, A., & Colley, N. (2017). Can real estate investors avoid specific risk?. Abacus, 53(3), 395-430.
    Clark, B., Feinstein, Z., & Simaan, M. (2020). A machine learning efficient frontier. Operations Research Letters, 48(5), 630-634.
    Domian, D. L., & Reichenstein, W. R. (2008). Returns-based style analysis of convertible bond funds. The Journal of Fixed Income, 18(3), 52-64.
    Duchi, J., Hazan, E., & Singer, Y. (2011). Adaptive sub gradient methods for online learning and stochastic optimization. Journal of machine learning research, 12(7).
    Fei, P., Ding, L., & Deng, Y. (2010). Correlation and volatility dynamics in REIT returns: performance and portfolio considerations. The Journal of Portfolio
    Management, 36(2), 113-125.
    Gore, R., & Stott, D. M. (1998). Toward a more informative measure of operating
    performance in the REIT industry: net income vs. funds from operations.
    Accounting Horizons, 12(4), 323.
    Granger, C. W. (1988). Causality, cointegration, and control. Journal of Economic
    Dynamics and Control, 12(2-3), 551-559.
    Ilvonen, M. (2021). REITs' main tenant business sectors' effect on REITs'
    performance.
    Liang, Y., & McIntosh, W. (1998). REIT style and performance. Journal of Real
    Estate Portfolio Management, 4(1), 69-78.
    Lee, S., & Stevenson, S. (2007). The substitutability of REITs and value stocks. Applied Financial Economics, 17(7), 541-557.
    Lee, S. (2010). The changing benefit of REITs to the mixed-asset portfolio. Journal of Real Estate Portfolio Management, 16(3), 201-215.
    Li, R. Y. M., Fong, S., & Chong, K. W. S. (2017). Forecasting the REITs and stock indices: group method of data handling neural network approach. Pacific Rim Property Research Journal, 23(2), 123-160.
    Markowitz, H. (1952). The utility of wealth. Journal of political Economy, 60(2), 151-158.
    [Markowitz, H. M. (1991). Foundations of Portfolio Theory. The Journal of Finance, 46(2), 469–477.
    Ouliaris, S., Park, J. Y., & Phillips, P. C. (1989). Testing for a unit root in the presence of a maintained trend. In Advances in econometrics and modelling (pp. 7-28). Springer, Dordrecht.
    Oyenubi, A. (2020). Optimal portfolios on mean-diversification efficient frontiers. In Advanced Studies of Financial Technologies and Cryptocurrency Markets (pp. 35-63). Springer, Singapore.
    Parungrojrat, N., & Kidsom, A. (2019). Stock price forecasting: Geometric Brownian motion and Monte Carlo simulation techniques. MUT Journal of Business Administration, 16(1), 90-103.
    Razali,M. N., &Sing,T. F.(2015).Systematic risk of Islamic REITs and conventional REITs in Malaysia.Journal of Real Estate Portfolio Management, 21, 77–92.
    Singh, T. R. (2016). A STUDY ON MONTE CARLO SIMULATION FOR STOCK PRICE FORECASTING.Global journal of Business and Integral Security.
    Zhu, Y., & Escobar-Anel, M. (2021). A neural network monte carlo approximation for expected utility theory. Journal of Risk and Financial Management, 14(7), 322.

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