A radial basis function approach to financial time series analysis
James M. Hutchinson · DSpace@MIT (Massachusetts Institute of Technology) · 1993
Billions of dollars flow through the world's financial markets every day, and market participants are understandably eager to accurately price financial instruments and understand relationships involving them. Nonlinear multivariate statistical modeling on fast computers offers the potential to capture more of the underlying dynamics of these high dimensional, noisy systems than traditional models while at the same time making fewer restrictive assumptions about them. For this style of exploratory, nonparametric modeling to be useful, however, care must be taken in fundamental estimation and confidence issues, especially concerns deriving from limited sample sizes. This thesis presents a collection of practical techniques to address these issues for a modeling methodology, Radial Basis Function networks. These techniques include efficient methods for parameter estimation and pruning, including a heuristic for setting good initial parameter values, a pointwise prediction error estimator for kernel type RBF networks, and a methodology for controlling the "data mining" problem. Novel applications in the finance area are described, including the derivation of customized, adaptive option pricing formulas that can distill information about the associated time varying systems that may not be readily captured by theoretical models. A second application area is stock price prediction, where models are found with lower out-of-sample error and better "paper trading" profitability than that of simpler linear and/or univariate models, although their true economic significance for real life trading is questionable. Finally, a case is made for fast computer implementations of these ideas to facilitate the necessary model searching and confidence testing, and related implementation iss...