Purpose – The purpose of this paper is to examine the long-run relationship and short-term linkage between the Asian REIT markets and their respective macroeconomic variables. Design/methodology/approach – The data collected comprised total return REIT Index from Japan, Hong Kong, Singapore, Malaysia, Thailand, Taiwan and South Korea and their macroeconomic variables from the date of availability of the data until December 2014. The macroeconomic variables are either available in monthly or quarterly basis, they will be separately tested with REIT Index respectively to their frequency. All the variables are tested for its stationarity prior to the investigation of their long-run relationship and short-term linkage using Johansen cointegration test and Granger causality test. Findings – The results showed that certain of the emerging REIT markets show a higher degree of integration with macroeconomic variables in the long run. This implies that the emerging REIT markets are more sensitive towards the change in macroeconomic environment in relative to the developed REIT markets. Practical implications – The paper implied that the distinction of each market structure and their unique way of policy implementation. The findings can assists policy makers to understand about the significance of policy implementation on the Asian REIT markets prior to decision making and also for the portfolio management my asset managers. Originality/value – The paper is one of the few attempts at assessing the long-term relationship and short term linkage between the Asian REIT markets and the macroeconomic variables.

PurposeThe purpose of this study is to evaluate the performance of the ensemble learning models, such as the Random Forest and Extreme Gradient Boosting models, in predicting the direction of the Japan real estate investment trusts (J-REITs) at different return horizons, based on input obtained from various technical indicators.Design/methodology/approachThis study measures the predictability of J-REITs with technical indicators by using different horizons of REITs' return and machine learning models. The ensemble learning models includes Random Forest and Extreme Gradient Boosting models while the return horizons of REITs ranging from 1 to 300 days. The results were further split into individual years to check for the consistency of the performance across time.FindingsThe Extreme Gradient Boosting appears to be the best method in improving forecast accuracy but not the trading return. A wider return horizons platform seemed to deliver a relatively better performance in both forecast accuracy and trading return, when compared to the return horizon of one.Practical implicationsIt is recommended that the Extreme Gradient Boosting and Random Forest model be considered by practitioners for back-testing trading model. In addition, selecting different return horizons so as to achieve a better performance in trading/investment should also be considered.Originality/valueThe predictability of J-REITs using technical indicators was compared among different returns horizons and the models (Extreme Gradient Boosting and Random Forest).

Purpose The purpose of this paper is to determine if artificial neural network (ANN) works better than linear regression in predicting Hong Kong real estate investment trusts’ (REITs) excess return. Design/methodology/approach Both ANN and the regression were applied in this study to forecast the Hong Kong REITs’ (HK-REITs) return using the capital asset pricing model and Fama and French’s three-factor models. Each result was further split into annual time series as a measure to investigate the consistency of the performance across time. Findings ANN had produced a better forecasting results than the regression based on their trading performance. However, the forecasting performance varied across individual REITs and time periods. Practical implications ANN should be considered for use when one were to attempt forecasting the HK-REITs excess returns. However, the trading performance should be always compared with buy and hold strategy prior to make any investment decisions. Originality/value This paper tested the predicting power of ANN on the HK-REITs and the consistency of its predicting power.

This paper investigates the long-run relationship and short-term linkage among the Asian REIT markets before, during and after global financial crisis through the combination of Johansen Cointegration Test and Granger Causality Test. The results indicate that the existence of cross-border diversification opportunities remain even though the markets were cointegrated since the global financial crisis. Short-run causality tests show that the number of causality relationships decrease over the time. Overall, the results suggest that domestic REIT investors can achieve diversification benefits by incorporating certain international REITs into the domestic portfolio, but they need to review their portfolios periodically as the linkages among markets could change from time-to-time.