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Grey Relational Analysis and Neural Network Forecasting of REIT returns

Author

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  • Jo-Hui Chen
  • Ting-Tzu Chang
  • Chao-Rung Ho
  • John Francis Diaz

Abstract

This study employs the Grey Relational Analysis (GRA) and Artificial Neural Network (ANN) to measure the impact of key elements on the forecasting performance of real estate investment trust (REIT) returns. To manage risks from a real estate price bubble, the findings of GRA suggest that the REIT is best influenced by industrial production index, lending rate, dividend yield, stock index and its own lagged performance. Consequently, this paper adjusts the parameters from GRA and inserts the key elements into the fitted ANN model by comparing the learning effect of the Back-propagation Neural Network (BPN). This study found that the ranking provided by the GRA is significant in correcting prediction errors using the learning outcome of the BPN. The neural network model proved to minimize error function and was able to adjust weighted values in order to enhance prediction accuracy.

Suggested Citation

  • Jo-Hui Chen & Ting-Tzu Chang & Chao-Rung Ho & John Francis Diaz, 2014. "Grey Relational Analysis and Neural Network Forecasting of REIT returns," Quantitative Finance, Taylor & Francis Journals, vol. 14(11), pages 2033-2044, November.
    • Handle: RePEc:taf:quantf:v:14:y:2014:i:11:p:2033-2044
    DOI: 10.1080/14697688.2013.816765
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    Cited by:

    1. Weipeng Tan & Tsung-Nan Chou, 2016. "Combined Grey Relational Analysis and Weighted Synthesis for Housing Price Prediction," International Journal of Business and Administrative Studies, Professor Dr. Bahaudin G. Mujtaba, vol. 2(3), pages 81-88.
    2. Mehmet Bayramoglu & Coskun Hamzacebi, 2016. "Stock Selection Based on Fundamental Analysis Approach by Grey Relational Analysis: A Case of Turkey," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(7), pages 178-178, July.

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