A Radial Basis Function Approach to Financial Time Series Analysis
MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB
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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. Radial basis functions, Option pricing, Parameter estimation, Time series prediction, Confidence, Stock market.
- Economics and Cost Analysis
- Statistics and Probability