Naslov
Forecasting Federal Funds Target Rate By Neural Networks

Autori
Zekić-Sušac, Marijana ; Danko, Ivan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 11th International Conference on Operational Research, KOI 2006 / Hunjak, Tihomir ; Neralić, Luka ; Scitovski, Rudolf - Pula, 2007

Skup
International Conference on Operational Research

Mjesto i datum
Pula, Hrvatska, 27.09.2006. - 29.09.2006

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
target interest rate; neural networks; prediction; multi layer perceptron; cross-validation

Sažetak
The aim of the paper was to create a model for predicting US federal funds target rate using neural networks. The model is based on macroeconomic data of USA covering the period from 1959 to 2005. Ten input variables were used, while the output was the federal funds target rate which is used by american federal bank (FED) to ensure the monetary stability in the country. Different neural network architectures were tested using the backpropagation algorithm, and the best neural network model is selected on the basis of test error. The sensitivity analysis is also conducted revealing that the most influencal input variables are the gold price change, and the change of market indices (Dow Jones i S&P500). The modeling results show that the neural network is able to incorporate the relationship among input variables and output. The created model revealed that artificial intelligence methods have great potential in the area of interest rate prediction and could be used for future research in that area.

Izvorni jezik
Engleski

Znanstvena područja
Ekonomija, Informacijske i komunikacijske znanosti

POVEZANOST RADA