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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

Citation:

Loo, W.K. (2019), “Predictability of HK-REITs returns using artificial neural network”, Journal of Property Investment & Finance, Vol. 38 No. 4, pp. 291-307. https://doi.org/10.1108/JPIF-07-2019-0090

About the Author - Dr Loo

I am a PhD holder with 8 years of teaching experience at secondary school and university. My expertizes are mathematics, statistics, econometrics, finance and machine learning.

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