Forecasting the REITs and stock indices: Group Method of Data Handling Neural Network approach

Author/s: Rita Yi Man Li, Simon Fong, Kyle Weng Sang Chong

Date Published: 4/05/2017

Published in: Volume 23 - 2017 Issue 2 (pages 123 - 160)

Abstract

If there is long-term memory in property stocks and REITs prices, historical data is relevant for future prices prediction. Despite previous research adopted various different methods to forecast future asset prices by using historical data; we attempted to forecast the REITs and stock indices by Group Method of Data Handling (GMDH) neural network method with Hurst which is the first of its kind. Our results showed that GMDH neural network performed better than the classical forecasting algorithms such as Single Exponential Smooth, Double Exponential Smooth, ARIMA and back-propagation neural network. The research results also provide useful information for investors when they make investment decisions.

Download Full Article

Download the Full Article PDF

14445921.2016.1225149.pdf 14445921.2016.1225149.pdf (6MB)

Keywords

Forecast - Group Method of Data Handling Neural Network - Reits - Stocks

References

  • Assaf, A. (2006) Canadian REITs and stock prices: Fractional cointegration and long memory, Review of Pacific Basin Financial Markets and Policies. 9, –.p:441-462. Review of Pacific Basin Financial Markets and Policies.
  • Australia Shareholders Associations. (2014) Retrieved from https://www.australianshareholders.com.au/resources/areit-sector-view-and-outlook-2014
  • Burger, C. J. S. C.Dohnal, M.Kathrada, M.Law, R. (2001) A practitioners guide to time-series methods for tourism demand forecasting — A case study of Durban, South Africa, Tourism Management. 22, p:403-409. Tourism Management.
  • Cadenas, E.Jaramillo, O. A.Rivera, W. (2010) Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method, Renewable Energy. 30, p:925-930. Renewable Energy.
  • Cheung, Y. W.Lai, K. S. (1995) A search for long memory in international stock market returns, Journal of International Money and Finance. 14, p:597-615. Journal of International Money and Finance.
  • Cotter, J.,Stevenson, S. (2008) Modeling long memory in REITs, Real Estate Economics. 36, p:533-554. Real Estate Economics.
  • Crawford, G. W.,Fratantoni, M. C. (2003) Assessing the forecasting performance of regime-switching, ARIMA and GARCH models of house prices, Real Estate Economics. 31, p:223-243. Real Estate Economics.
  • Dase, R. K.,Pawar, D. D. (2010) Application of Artificial Neural Network for stock market predictions: A review of literature, International Journal of Machine Intelligence. 2, p:14-17. International Journal of Machine Intelligence.
  • DataStream. (2016) Retrieved from http://financial.thomsonreuters.com/en/products/tools-applications/trading-investment-tools/datastream-macroeconomic-analysis.html
  • Dosset, P.Rassam, P.Fernandez, L.Espenel, C.Rubinstein, E.Margeat, E.Milhiet, P. E. (2016) Automatic detection of diffusion modes within biological membranes using backpropagation neural network, BMC Bioinformatics. 17, p:111-208. BMC Bioinformatics.
  • Ellis, C. A.,Parbery, S. A. (2005) Is smarter better? A comparison of adaptive, and simple moving average trading strategies, Research in International Business and Finance. 19, p:399-411. Research in International Business and Finance.
  • Faggini, M.,Parziale, A. (2012) The failure of economic theory. Lessons from chaos theory, Modern Economy. 3(1), p:1-10. Modern Economy.
  • Fama, Eugene F. (1965) Random walks in stock market prices, Financial Analysts Journa. 21, –.p:55-59. Financial Analysts Journa.
  • Fong, S.Nannan, Z.Wong, R. K.Yang, X. S. (2012) Rare events forecasting using a residual-feedback GMDH neural network. Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on Data Mining Workshops. p:464-473. Rare events forecasting using a residual-feedback GMDH neural network.
  • Gardner, E. S.Anderson-Fletcher, E. A.Wicks, A. M. (2001) Further results on focus forecasting vs. exponential smoothing, International Journal of Forecasting. 17, p:287-293. International Journal of Forecasting.
  • Ghasemi, A.Shayeghi, H.Moradzadeh, M.Nooshyar, M. (2016) A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management, Applied Energy. 177, p:40-59. Applied Energy.
  • Granero, M. A. S.Segovia, J. E. T.Pérez, J. G. (2008) Some comments on Hurst exponent and the long memory processes on capital markets, Physica A. 387, p:5543-5551. Physica A.
  • Holt, C. C. (2004) Forecasting trends and seasonal by exponentially weighted averages, International Journal of Forecasting. 20, –.p:5-10. International Journal of Forecasting.
  • Hong, T.,Fan, S. (2016) Probabilistic electric load forecasting: A tutorial review, International Journal of Forecasting. 32, p:914-938. International Journal of Forecasting.
  • Huang, J. H. (2011) A study of commodity trading advisor performance prediction-an analysis of chaos and artificial neural networkDepartment of Bussiness Administrator, Chung Yuan Christian University, Taiwan. A study of commodity trading advisor performance prediction-an analysis of chaos and artificial neural network.
  • Huang, H.-S.,Chiu, I.-H. (2005) The study of neural network to predict Taiwan ETF-50 stock index price. Department of Information Management, National Changhua University of Education. The study of neural network to predict Taiwan ETF-50 stock index price.
  • Hurst, H. E. (1951) Long term storage capacities of reservoirs, Transactions of the American Society of Civil Engineers. 116, –.p:776-808. Transactions of the American Society of Civil Engineers.
  • Ivakhnenko, A. G. (1970) Heuristic self-organization on problems of engineering cybernetics, Automatica. 6, –.p:207-219. Automatica.
  • Kendall, Maurice. (1953) The analytics of economic time series, part 1: PricesLondon School of Economics, Division of Research Techniques. The analytics of economic time series, part 1: Prices.
  • Kimoto, T.,Kazuo, A. (1990) Stock market prediction system with modular neural networks, IEEE International Joint Conference on Neural Networks. p:11-16. IEEE International Joint Conference on Neural Networks.
  • Kuhle, J. L. (1987) Portfolio diversification and return benefits – Common stocks vs. real estate investment trusts, Journal of Real Estate Research. 2(2), –.p:1-9. Journal of Real Estate Research.
  • Kwiatkowski, D.Phillips, P. C. B.Schmidt, P.Shim, Y. (1992) Testing the null hypothesis of stationarity against the alternative of a unit root, Journal of Econometrics. 54, p:159-178. Journal of Econometrics.
  • Lapedes and Farber. (1987) Advances in neural information processing systems. (pp. –)p:442-456. How Neural Nets Work. American Institute of Physics. Advances in neural information processing systems.
  • LeRoy, S. F.,Porter, R. D. (1981) The present-value relation: Tests based on implied variance bounds, Econometrica. 49, p:555-574. Econometrica.
  • Li, R. Y. M.Chau, K. W. (2016) Econometric analyses of international housing markets. Routledge. Econometric analyses of international housing markets.
  • Ling, D. C.Naranjo, A.Ryngaert, M. D. (2000) The predictability of equity REIT returns: Time variation and economic significance, The Journal of Real Estate Finance and Economics. 20, p:117-136. The Journal of Real Estate Finance and Economics.
  • Makridakis, S.,Hibon, M. (2000) The M3-competition: Results, conclusions and implications, International Journal of Forecasting. 16, p:451-476. International Journal of Forecasting.
  • Méndez-Acosta, H. O.Hernandez-Martinez, E.Jáuregui-Jáuregui, J. A.Alvarez-Ramirez, J.Puebla, H. (2013) Monitoring anaerobic sequential batch reactors via fractal analysis of pH time series, Biotechnology and Bioengineering. 110, p:2131-2139. Biotechnology and Bioengineering.
  • Mitra, S. K. (2012) Is Hurst exponent value useful in forecasting financial time series. Nagpur:Institute of Management Technology. Is Hurst exponent value useful in forecasting financial time series.
  • Newell, G.,Osmadi, A. (2009) The development and preliminary performance analysis of Islamic REITs in Malaysia, Journal of Property Research. 26, p:329-347. Journal of Property Research.
  • Newell, G.,Peng, H. W. (2012) The significance and performance of Japan REITs in a mixed-asset portfolio, Pacific Rim Property Research Journal. 18, p:21-34. Pacific Rim Property Research Journal.
  • Pan, G. R.,Gu, C. (2007) New method for deformaion prediction, Journal of Guilin University of Technology. 27, p:529-532. Journal of Guilin University of Technology.
  • Pavlova, I.Cho, J. H.Parhizgari, A. M.Hardin, W. G. (2014) Long memory in REIT volatility and changes in the unconditional mean: A modified FIGARCH approach, Journal of Property Research. 31, p:315-332. Journal of Property Research.
  • Peters, Edgar E. (1994) Fractal market analysis: Applying chaos theory to investment and economics, [M]Wiley. Fractal market analysis: Applying chaos theory to investment and economics, [M].
  • Pierdzioch, C.,Hartmann, D. (2013) Forecasting Eurozone real-estate returns, Applied Financial Economics. 23, p:1185-1196. Applied Financial Economics.
  • Qian, B.,Rasheed, K. (2004) Hurst exponent and financial market predictability. Athens: Department of Computer Science University of Georgia. Hurst exponent and financial market predictability.
  • Real Estate Easy Property Info. (2016) Retrieved from http://reitinfo.com/
  • Sah, V.Zhou, X.Das, P. K. (2015) Does index addition add any new information? Evidence from REIT dividend forecasts, Journal of Property Research. 32, p:33-49. Journal of Property Research.
  • Shyu, S. D.Ke, S. C.Tai, M. L. (2009) A study of long memory in international stock price indices, Bank of Taiwan Quarterly. 60, p:277-299. Bank of Taiwan Quarterly.
  • Wang, K.Erickson, J.Chen, S. H. (1995) Does the REIT stock market resemble the general stock market?, Journal of Real Estate Research. 10, p:445-460. Journal of Real Estate Research.
  • Wang, X. Y.Song, X. F.Wu, R. M. (2004) Fractal analysis of China stock markets, Journal of Management sciences in China. 7, p:1-8. Journal of Management sciences in China.
  • White, Halbert. (1988) Economic prediction using neural networks: The case of IBM daily stock returnsDepartment of Economics University of California. Economic prediction using neural networks: The case of IBM daily stock returns.
  • Wu, L.Liu, S.Yang, Y. (2016) Grey double exponential smoothing model and its application on pig price forecasting in China, Applied Soft Computing. 39, p:117-123. Applied Soft Computing.
  • Xiao, T.,Huang, G. J. (2000) GMDH neural network application to prediction, Journal of East China Shipbuilding Institute. 14, p:72-76. Journal of East China Shipbuilding Institute.
  • Yao, J.Tan, C. L.Poh, H. L. (1999) Neural networks for technical analysis: A study on klci, International Journal of Theoretical and Applied Finance. 2, p:221-241. International Journal of Theoretical and Applied Finance.
  • Zhao, S. J.,Xu, B. Z. (2011) Trend prediction for stock price using dynamic hurst index, Journal of Ningbo University (Natural Science and Engineering Edition). 24(4), p:79-82. Journal of Ningbo University (Natural Science and Engineering Edition).
  • Zhou, J.,Kang, Z. (2011) A comparison of alternative forecast models of REIT volatility, Journal of Real Estate Finance and Economics. 42, p:275-294. Journal of Real Estate Finance and Economics.
  • Zuang, X. T.Zhuang, X. lTian, Y. (2003) Hurst index and problem of fractal structure in stock market, Journal of Northeastern University (Natural Science). 24(9), p:79-82. Journal of Northeastern University (Natural Science).