A Radial Basis Function Approach to Financial Time Series Analysis

dc.creator	Hutchinson, James M.
dc.date	2004-10-20T14:45:36Z
dc.date	2004-10-20T14:45:36Z
dc.date	1993-12-01
dc.date.accessioned	2013-10-09T02:46:51Z
dc.date.available	2013-10-09T02:46:51Z
dc.date.issued	2013-10-09
dc.identifier	AITR-1457
dc.identifier	http://hdl.handle.net/1721.1/6783
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	Nonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of practical techniques to address important estimation and confidence issues for Radial Basis Function networks arising from such a data driven approach, including efficient methods for parameter estimation and pruning, a pointwise prediction error estimator, and a methodology for controlling the "data mining'' problem. Novel applications in the finance area are described, including customized, adaptive option pricing and stock price prediction.
dc.format	160 p.
dc.format	681549 bytes
dc.format	2849290 bytes
dc.format	application/octet-stream
dc.format	application/pdf
dc.language	en_US
dc.relation	AITR-1457
dc.subject	radial basis functions
dc.subject	option pricing
dc.subject	parametersestimation
dc.subject	time series prediction
dc.subject	confidence
dc.subject	stock market
dc.title	A Radial Basis Function Approach to Financial Time Series Analysis

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