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

doi:10.1007/s11704-010-0506-4

Front Comput Sci Chin

2010, Vol. 4

Issue (2) : 254-262 https://doi.org/10.1007/s11704-010-0506-4

RESEARCH ARTICLE

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

Dabin ZHANG^1,2, Lean YU²(

), Shouyang WANG², Haibin XIE²

1. Department of Information Management, Huazhong Normal University, Wuhan 430079, China; 2. Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

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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.

Keywords turning points business cycle leading indicators neural networks (NNs)

Corresponding Author(s): YU Lean,Email:yulean@amss.ac.cn

Issue Date: 05 June 2010

Cite this article:

Dabin ZHANG,Shouyang WANG,Haibin XIE, et al. Neural network methods for forecasting turning points in economic time series: an asymmetric verification to business cycles[J]. Front Comput Sci Chin, 2010, 4(2): 254-262.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-010-0506-4
https://academic.hep.com.cn/fcs/EN/Y2010/V4/I2/254

Fig.1 A three-layer feed-forward neural network, (assuming >2)

Tab.1 Economic indicators

Fig.2 Peaks and troughs of industrial added value (P: peak; T: tough)

Tab.2 Out-of-sample forecasts, and rank of economic indicators ( = 1-8)

Fig.3 Composite index of different indicators. (a) Composite index of NIC; (b) composite index of NSB; (c) composite index of all indicators; (d) composite index of optimization indicators

Tab.3 Out-of-sample and rankings of individual indicators, 1-8 months ahead, in first sub-period (January 1998 to December 2002)

Tab.4 Out-of-sample and rankings of individual indicators, 1-8 months ahead, in second sub-period (January 2003 to December 2008)

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[1]	Dabin ZHANG, Lean YU, Shouyang WANG, Yingwen SONG. A novel PPGA-based clustering analysis method for business cycle indicator selection[J]. Front Comput Sci Chin, 2009, 3(2): 217-225.

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