DocumentCode :
3457091
Title :
Function approximation with learning networks in the financial field and its application to the interest rate sector
Author :
Hoffmann, Günther A.
Author_Institution :
Dept. of Syst. Anal., Tech. Univ. Berlin, Germany
fYear :
1995
fDate :
9-11 Apr 1995
Firstpage :
178
Lastpage :
182
Abstract :
Quantitative analysis in the financial markets has traditionally been dominated by linear, parametric modeling approaches. Recent theoretical and empirical results suggest that nonlinear, nonparametric, multivariable regression techniques are more powerful tools to discover and capture nontrivial relationships between variables. In this work ways of improving models and thus forecasts are explored by adapting two different ways of specifying connectionist networks: radial basis function networks (RBF) and multilayer perceptrons (MLP). By employing these techniques we gain the potential to model complex data more effectively while at the same time we largely avoid imposing any particular and possibly incorrect model assumptions. Evolution strategy and a speeded up error backpropagation are utilized to estimate model parameters. To illustrate the application potential nonlinear models for Bund yields are estimated. For comparison benchmark models using a linear multivariable and a random walk approach are also estimated
Keywords :
backpropagation; feedforward neural nets; financial data processing; function approximation; multilayer perceptrons; statistical analysis; stock markets; Bund yields; benchmark models; connectionist networks; error backpropagation; evolution strategy; finance; financial markets; forecasts; function approximation; interest rate sector; learning networks; linear multivariable approach; linear parametric modeling; model parameter estimation; multilayer perceptrons; multivariable regression; nonlinear nonparametric techniques; quantitative analysis; radial basis function networks; random walk; Artificial neural networks; Backpropagation; Economic forecasting; Economic indicators; Function approximation; Intelligent networks; Parametric statistics; Predictive models; Radial basis function networks; Stock markets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Financial Engineering, 1995.,Proceedings of the IEEE/IAFE 1995
Conference_Location :
New York, NY
Print_ISBN :
0-7803-2145-6
Type :
conf
DOI :
10.1109/CIFER.1995.495272
Filename :
495272
Link To Document :
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