Predicting multivariate financial time series using neural networks: the Swiss bond case

Abstract

Presents an integrated approach for modelling the behaviour of financial markets with artificial neural networks (ANNs). The method allows the forecasting of financial time series. Its originality lies in the fact that it is based on statistics and macroeconomics principles, integrating fundamental economic knowledge in a multivariate, nonlinear time-series ANN model. The core of the work is a feasibility analysis, which is seldom attempted in ANN work, consisting of a series of different univariate and multivariate, linear and nonlinear statistical tests. The enhancement of prior work is a sensitivity analysis with bootstrap as part of the feasibility analysis. The feasibility analysis evaluates the "a priori" chance of forecasting the defined system and helps in defining the topology of the ANN. The method is applied to a real-life case study with a few data samples.