Improving Quality of Long-Term Bond Price Prediction Using Artificial Neural Networks

Robert Verner, Michal Tkáč, Michal Tkáč

Abstract

Purpose: The aim of this paper is to propose nonlinear autoregressive neural network which can improve quality of bond price forecasting.        


Methodology/Approach: Due to the complex nature of market information that influence bonds, artificial intelligence could be accurate, robust and fast choice of bond price prediction method.


Findings: Our results have reached a coefficient of determination higher than 95% in the training, validation and testing sets. Moreover, we proposed the nonlinear autoregressive network with external inputs using 50 year interest-rate swaps denominated in EUR and volatility index VIX as two external variables.


Research Limitation/Implication: Our sample of daily prices between 4th January 2016 and 13th January 2021 (totally 1,270 trading days) suggest that both Levenberg-Marquardt and Scaled conjugate gradient learning algorithms achieved excellent results.


Originality/Value of paper: Despite the fact that both learning algorithms achieved satisfying outcomes, implementation of an independent variable into the autoregressive neural network environment had no significant impact on prediction ability of the model.

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Authors

Robert Verner
Michal Tkáč
Michal Tkáč
michal.tkac1@euba.sk (Primary Contact)
Author Biographies

Robert Verner, University of Economics in Bratislava, Slovakia

Robert Verner –  is Assistant Professor at the University of Economics in Bratislava, Faculty of Business Economics with seat in Košice (PHF EU), Košice, Slovakia. He participated as a Speaker in several national and international conferences and is the author of cited publications with high impact factor. E-mail: robert.verner@euba.sk

Michal Tkáč, University of Economics in Bratislava, Slovakia

Michal Tkáč – is Professor at the University of Economics in Bratislava, Faculty of Business Economics with seat in Košice (PHF EU), Košice, Slovakia. He holds a CSc. title in Mathematics and Physics, Geometry and Topology from the University P. J. Šafárika, Faculty of Science in Košice, Slovakia. Experienced lecturer, teacher, organizer of various scientific and social events. He was the head of several successful domestic and foreign projects and conferences. He is the author of cited publications in many scientific journals. E-mail: michal.tkac@ euba.sk 

Michal Tkáč, University of Economics in Bratislava, Slovakia

Michal Tkáč – is Associate Professor at the University of Economics in Bratislava, Faculty of Business Economics with seat in Košice (PHF EU), Košice, Slovakia. He holds a PhD. title in Finance at the Technical University of Košice, Faculty of Economics, Košice, Slovakia. Is a specialist in Business Risk (Business Risk Management). He is the author of many publications. E-mail: michal.tkac1@ euba.sk. Author’s 

Verner, R., Tkáč, M., & Tkáč, M. (2021). Improving Quality of Long-Term Bond Price Prediction Using Artificial Neural Networks. Quality Innovation Prosperity, 25(1), 103–123. https://doi.org/10.12776/qip.v25i1.1532

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