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

  title={Forecasting the REITs and stock indices: Group Method of Data Handling Neural Network approach},
  author={Rita Yi Man Li and Simon James Fong and Kyle Weng Sang Chong},
  journal={Pacific Rim Property Research Journal},
  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… 
Rent index forecasting through neural networks
PurposeChinese housing market has been growing fast during the past decade, and price-related forecasting has turned to be an important issue to various market participants, including the people,
Predicting primary commodity prices in the international market: an application of group method of data handling neural network
The results showed that the proposed model based on GMDH technique outperforms than other methods in prediction of commodity prices, and provides a promising alternative for price prediction.
Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network
A novel hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural network for extracting temporal features of stock time series data is proposed.
Forecasting CDS Term Structure Based on Nelson-Siegel Model and Machine Learning
It is confirmed that the Nelson–Siegel model can be used to predict not only the interest rate term structure but also the CDS term structure and it is demonstrated that machine-learning models, namely, SVR, RNN, LSTM, and GMDH, outperform the model-driven methods.
House price forecasting with neural networks
Fund Price Analysis Using Convolutional Neural Networks for Multiple Variables
This study focuses on learning the patterns of prices rather than time points to predict Korean fund prices, which means that multi-variable models have a higher cumulative return than the single-variable model and KOSPI and thus a higher average of all funds for active investors.
Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions
The outbreak of COVID-19 has brought the world to an unprecedented position where financial and mental resources are drying up. Livelihoods are being lost, and it is becoming tough to save lives.
REITs Portfolio Optimization: A Nonlinear Generalized Reduced Gradient Approach
Many investors would like to know which countries’ REITs they should invest, and the respective industrial REITs in those countries to maximize their profits. To construct REITs the optimal
The Stock Price Performance and Return Style of the Pan-Infrastructure Reits Corporation: Evidence from U.S. and Japan Market
  • Wei-Shen Li
  • Economics
    International Journal of Economics and Finance
  • 2022
The growth of big data analytics, cloud computing and 5G communication promotes the expansion of Pan-infrastructure REITs market. Despite previous studies confirmed the value-added role of


This paper presents a study of artificial neural nets for use in stock index forecasting. The data from a major emerging market, Kuala Lumpur Stock Exchange, are applied as a case study. Based on the
Application of Artificial Neural Network for stock market predictions: A review of literature
This paper presents a review of literature application of Artificial Neural Network for stock market predictions and from this literature found that Artificial Neural network is very useful for predicting world stock markets.
Economic prediction using neural networks: the case of IBM daily stock returns
  • H. White
  • Economics
    IEEE 1988 International Conference on Neural Networks
  • 1988
Having to deal with the salient features of economic data highlights the role to be played by statistical inference and requires modifications to standard learning techniques which may prove useful in other contexts.
Long memory in REIT volatility and changes in the unconditional mean: a modified FIGARCH approach
We examine the long memory of real estate investment trust (REIT) volatility in the mature REIT markets of Australia, Japan, the UK and the US, and propose a modified fractionally integrated
Trend Prediction for Stock Price Using Dynamic Hurst Index
Hurst Index is defined in fractal chaos theory and time series to determine groups of statistical parameters,which has been applied in marketing process.In this paper,we calculate the Hurst index
Is Hurst Exponent Value Useful in Forecasting Financial Time Series
We estimated Hurst exponent of twelve stock index series from across the glove using daily values of for past ten years and found that the Hurst exponent value of the full series is around 0.50
A Comparison of Alternative Forecast Models of REIT Volatility
This study compares the relative performance of several well-known models in the forecasting of REIT volatility. Overall our results suggest that long-memory models (ARFIMA & FIGARCH) provide the
Forecasting Eurozone real-estate returns
We use a real-time forecasting approach to study the predictability of excess returns on a benchmark Euro Area real-estate index. The real-time forecasting approach accounts for the fact that, in
Stock market prediction system with modular neural networks
The authors developed a number of learning algorithms and prediction methods for the TOPIX (Tokyo Stock Exchange Prices Indexes) prediction system, which achieved accurate predictions, and the simulation on stocks trading showed an excellent profit.