Predictability of HK-REITs returns using artificial neural network

Wei Kang Loo

Journal of Property Investment & Finance, 2019, vol. 38, issue 4, 291-307

Abstract: 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.

Keywords: Investment; Artificial neural network; Real estate investment trust; Hong Kong REITs; Real estate investment; Return forecasting (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eme:jpifpp:jpif-07-2019-0090

DOI: 10.1108/JPIF-07-2019-0090

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