Optimizing Mixed-Asset Portfolios Involving REITs

Habbab, Fatim Z.; Kampouridis, Michael; Voudouris, Alexandros A.

doi:10.1109/cifer52523.2022.9776074

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…However, Burns and Epley (1982), Mueller et al (1994), Chandrashekaran (1999) and Jandura (2003), Ennis and Burik (1991) and Mull and Soenen (1997) have examined the US market along with the US-REITs in a global mixed-asset portfolio and found evidence for diversification. In a more recent study, Habbab et al (2022) came to the same conclusion after analyzing UK, Australian, and US-Reits. Jandura (2003) investigated European stocks and observed the same dynamics as appear in US real-estate stocks.…”

Section: 10 Introductionmentioning

confidence: 63%

Role of Real Estate in a Mixed-Asset Portfolio and the Impact of Illiquidity

Günther

Wills²,

Piazolo³

2022

INTREST

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Real estate ratios have increased in recent years. This article thus examines the diversification potential of real estate investments that German investors can achieve at a global scale. To this end, it analyzes how the illiquidity of some real estate investments or the illiquidity preference of an investor can bring about optimal investment ratios. Optimum allocation quotas for German investors with a wide range of mixed-asset allocations are examined. In addition to traditional optimizations, this article applies the three-fund theorem to include liquid and illiquid forms of real estate investment and to determine optimum allocation ratios. While real estate may be an essential component in a mixed-asset portfolio, it is not included in all optimal portfolios. An optimal portfolio also depends on the investment form, insofar as real estate vehicles are not always suitable for diversifying the portfolio risk or for improving the performance of a mixed-asset portfolio. Moreover, illiquid investment vehicles can often provide strong diversification benefits. The optimum allocations to real estate thus depend on the investor’s illiquidity acceptance, even if allocation dominance has increased in recent years. While many studies have demonstrated the advantage that investors gain from adding certain real estate assets, such as those obtained by direct investments, this study goes further by examining the comparative advantage of different real estate investment forms within a variety of asset classes. New insights can thus be gained by considering investors’ liquidity preferences within a given portfolio. One of these insights is that there is a trade-off between illiquidity and diversification potential. Another is that optimum portfolio allocations depend on illiquidity acceptance. These findings therefore also provide practical guidance not only to German investors with a global portfolio diversification but also to practitioners who add illiquid asset classes to their portfolio, to say nothing of the valuable field knowledge it offers to researchers in this field.

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Section: 10 Introductionmentioning

confidence: 63%

Role of Real Estate in a Mixed-Asset Portfolio and the Impact of Illiquidity

Günther

Wills²,

Piazolo³

2022

INTREST

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“…The benefit of including the real estate investment trust in the portfolio was studied using a genetic algorithm. The genetic algorithm outperformed the global minimum variance portfolio [26]. Diversification-aware portfolios were optimized using a genetic algorithm-Out of the three portfolios constructed, One of the portfolios underperformed.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Portfolio Management System Utilizing Machine Learning Techniques

et al. 2023

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There are 1641 companies listed on the National Stock Exchange of India. It is undoubtedly infeasible for a retail investor to invest in all the stocks. It is a well-known fact that the portfolio's return is an average return of all its constituent stocks, and risk will be less than or equal to the maximum risk of all the portfolio components. This paper is unique as it elaborates on the entire portfolio selection, optimization, and management process. Portfolio selection is accomplished through the K-Means algorithm. Optimization is achieved utilizing the genetic algorithm, and a sliding window is applied for portfolio management. Four different ways of portfolio calculation, namely, equally-weighted portfolio, global minimum variance portfolio, market cap-weighted portfolio, and maximum Sharpe ratio portfolio, are applied. The results depict that all three optimized portfolios outperform the Nifty index. The dataset for the study is obtained from globaldatafeeds.in.

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“…Much work in the asset allocation literature has focused on the problem of optimizing mixed-asset portfolios including real estate [5,6,7,8]. However, typically the focus lies on optimising asset weights, using prices from a training set.…”

Section: Introductionmentioning

confidence: 99%

Improving REITs Time Series Prediction Using ML and Technical Analysis Indicators

Habbab

Kampouridis

Papastylianou

2023

2023 International Joint Conference on Neural Networks (IJCNN)

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One of the most popular ways to reduce the risk of an investment portfolio is by holding shares of Real Estate Investment Trusts (REITs), which own and manage real estate. An important aspect of this process is to be able to forecast future REITs prices, as this allows investors to achieve higher returns at lower risk. This paper examines the performance of five different machine learning algorithms in the task of REITs price forecasting: Ordinary Least Squares Linear Regression, Support Vector Regression, k-Nearest Neighbours Regression, Extreme Gradient Boosting, and Long/Short-Term Memory Neural Networks. In addition to past REITs prices, we also use Technical Analysis indicators to assist the algorithms in the task of price prediction. While such indicators are very popular in stocks forecasting, they have never been used to forecast REITs. Our experiments show that (i) all ML algorithms produce low error and standard deviation, and are able to outperform the well-known statistical benchmark of AutoRegressive Integrated Moving Average (ARIMA), and (ii) the introduction of Technical Analysis (TA) indicators into the feature set leads to an error reduction of up to 50%.

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Optimizing Mixed-Asset Portfolios Involving REITs

Cited by 6 publications

References 16 publications

Role of Real Estate in a Mixed-Asset Portfolio and the Impact of Illiquidity

Role of Real Estate in a Mixed-Asset Portfolio and the Impact of Illiquidity

Real-Time Portfolio Management System Utilizing Machine Learning Techniques

Improving REITs Time Series Prediction Using ML and Technical Analysis Indicators

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