Real Estate Investment Trusts (REITs) is a popular investment choice as it allows investors to hold shares in real estate rather than investing large sums of money to purchase real estate by themselves. Previous work studied the effectiveness of multi-asset portfolios that include REITs via an efficient frontier analysis. However, the advantages of including (both domestic and international) REITs in multi-asset portfolios, as well as analyzing all the possible combinations of asset classes, has not been investigated before. In this paper, we fill in this gap by performing a thorough investigation across 456 different portfolios to demonstrate the added value of including REITs in mixed-asset portfolios in terms of different important financial metrics. To this end, we use a genetic algorithm approach to maximize the Sharpe ratio of the portfolios. Our results show that optimization via a genetic algorithm outperforms the results obtained from a global minimum variance portfolio. More importantly, our results also show that there can be significant improvements in average returns, risk and Sharpe ratio when including REITs.

The main purpose of portfolio optimization is to reduce the risk, and/or maximize the return of a group of investments. Most of the works that have been done on portfolio optimization are based on the Modern Portfolio Theory introduced by Markowitz in 1959. Some of them have employed price predictions to compute optimal asset weights. It has been demonstrated that using price predictions, instead of historical data, might improve portfolio performance under a risk-adjusted perspective. However, contributions in the field mainly focused on stocks, while little attention has been given on multi-asset portfolios including real estate. In this paper, we fill this gap by running a genetic algorithm on 456 portfolios to demonstrate the added value of including price predictions in our asset allocation problem. To investigate this, we compare the theoretical case of having a perfect foresight, where the predicted price pt is exactly the same as the expected price pt; under this case, the portfolio optimization task takes place in the test set (since we have assumed a perfect price prediction). We compare the results under perfect foresight with results derived from portfolio optimization that only took place in the training set, and the weights were then directly applied to the test set. Our goal is to demonstrate the theoretical advantages of using price predictions on mixed-asset portfolios that include real estate. Our results show that there can be significant improvements (up to 45%) in sharpe ratio, rate of return, and risk, when using price predictions instead of a historical prices based portfolio.

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%.

No abstract