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Neural network methods for forecasting turning points in economic time series: an asymmetric verification to business cycles

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Abstract

This paper examines the relevance of various financial and economic indicators in forecasting business cycle turning points using neural network (NN) models. A three-layer feed-forward neural network model is used to forecast turning points in the business cycle of China. The NN model uses 13 indicators of economic activity as inputs and produces the probability of a recession as its output. Different indicators are ranked in terms of their effectiveness of predicting recessions in China. Out-of-sample results show that some financial and economic indicators, such as steel output, M2, Pig iron yield, and the freight volume of the entire society are useful for predicting recession in China using neural networks. The asymmetry of business cycle can be verified using our NN method.

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Authors and Affiliations

  1. Department of Information Management, Huazhong Normal University, Wuhan, 430079, China

    Dabin Zhang

  2. Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Dabin Zhang, Lean Yu, Shouyang Wang & Haibin Xie

Authors
  1. Dabin Zhang
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  2. Lean Yu
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  3. Shouyang Wang
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  4. Haibin Xie
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Correspondence to Lean Yu.

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Zhang, D., Yu, L., Wang, S. et al. Neural network methods for forecasting turning points in economic time series: an asymmetric verification to business cycles. Front. Comput. Sci. China 4, 254–262 (2010). https://doi.org/10.1007/s11704-010-0506-4

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  • DOI: https://doi.org/10.1007/s11704-010-0506-4

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