A multiscale neural network learning paradigm for financial crisis forecasting

doi:10.1016/j.neucom.2008.11.035

	A multiscale neural network learning paradigm for financial crisis forecasting
	Yu, Lean 1; Wang, Shouyang 1; Lai, Kin Keung 2; Wen, Fenghua 3
刊名	NEUROCOMPUTING
	2010
卷号	73 期号:4-6 页码:716-725
关键词	Artificial neural networks Empirical mode decomposition (EMD) Hilbert-EMD transform Multiscale learning Financial crisis forecasting
ISSN号	0925-2312
DOI	10.1016/j.neucom.2008.11.035
英文摘要	A financial crisis is typically a rare kind of an event, but it hurts sustainable economic development when it occurs. This study proposes a multiscale neural network learning paradigm to predict financial crisis events for early-warning purposes. In the proposed multiscale neural network learning paradigm, currency exchange rate, a typical financial indicator that usually reflects economic fluctuations, is first chosen. Then a Hilbert-EMD algorithm is applied to the currency exchange rate series. Using the Hilbert-EMD procedure, some intrinsic mode components (IMCs) of the currency exchange rate series, with different scales, can be obtained. Subsequently, the internal correlation structures of different IMCs are explored by a neural network model. Using the neural network weights, some important IMCs are selected as the final neural network inputs and some unimportant IMCs that are of little use in mapping from inputs to output are discarded. Using these selected IMCs, a neural network learning paradigm is used to predict future financial crisis events, based upon some historical data. For illustration purpose, the proposed multiscale neural network learning paradigm is applied to exchange rate data of two Asian countries to evaluate the state of financial crisis. Experimental results reveal that the proposed multiscale neural network learning paradigm can significantly improve the generalization performance relative to conventional neural networks. (C) 2009 Elsevier B.V. All rights reserved.
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000275643000023
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/9846]
专题	中国科学院数学与系统科学研究院
通讯作者	Yu, Lean
作者单位	1.Chinese Acad Sci, Inst Syst Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China 3.Changsha Univ Sci & Technol, Sch Econ & Management, Changsha 410076, Hunan, Peoples R China
推荐引用方式 GB/T 7714	Yu, Lean,Wang, Shouyang,Lai, Kin Keung,et al. A multiscale neural network learning paradigm for financial crisis forecasting[J]. NEUROCOMPUTING,2010,73(4-6):716-725.
APA	Yu, Lean,Wang, Shouyang,Lai, Kin Keung,&Wen, Fenghua.(2010).A multiscale neural network learning paradigm for financial crisis forecasting.NEUROCOMPUTING,73(4-6),716-725.
MLA	Yu, Lean,et al."A multiscale neural network learning paradigm for financial crisis forecasting".NEUROCOMPUTING 73.4-6(2010):716-725.