Abstract
The aim of this paper is to asses the effectiveness and easiness of use of Artificial Neural Network (ANN) models in predicting interest rates by comparing the performance of ANN models with that of simple multivariate linear regression (SMLR) models. The task undertaken is to predict the yield of the Canadian 90-day Treasury Bills (TBs) one month ahead using information from eighteen indices of the current economic data, as for example the level of economic activity (GDP), inflation, liquidity etc. Following various approaches, models of both types, SMLR and ANN, are constructed that make use of the same input information. Their performance is compared from the mean absolute percentage error of twelve monthly forecasts. In all cases the ANN models outperform the SMLR models by a wide margin. In addition the absence of need to check the validity of data with respect to assumptions as linearity and normality makes handling the data for ANN models easier and their applicability wider.
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© 1998 Springer Science+Business Media New York
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Politof, T., Ulmer, D. (1998). Predicting Interest Rates Using Artificial Neural Networks. In: Zopounidis, C. (eds) Operational Tools in the Management of Financial Risks. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5495-0_17
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DOI: https://doi.org/10.1007/978-1-4615-5495-0_17
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