Optimizing Mixed-Asset Portfolios With Real Estate: Why Price Predictions?

Abstract

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 port-folio 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p_{t}$</tex> 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.