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.

中文翻译：

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预测房地产投资信托和股票指数：数据处理神经网络方法的分组方法

摘要如果房地产存量和房地产投资信托基金的价格具有长期记忆，则历史数据与未来的价格预测相关。尽管先前的研究采用了各种不同的方法来利用历史数据来预测未来资产价格。我们尝试使用Hurst的数据处理组方法（GMDH）神经网络方法来预测REIT和股票指数，这是此类方法中的第一个。我们的结果表明，GMDH神经网络的性能优于经典的预测算法，例如单指数平滑，双指数平滑，ARIMA和反向传播神经网络。研究结果还为投资者做出投资决策提供有用的信息。