PREDICTION OF DEMAND FOR PRIMARY BOND OFFERINGS USING ARTIFICIAL NEURAL NETWORKS
Michal Tkac and
Quality Innovation Prosperity, 2014, vol. 18, issue 2
Purpose: Primary bond markets represent an interesting investment opportunity not only for banks, insurance companies, and other institutional investors, but also for individuals looking for capital gains. Since offered securities vary in terms of their rating, industrial classification, coupon, or maturity, demand of buyers for particular offerings often overcomes issued volume and price of given bond on secondary market consequently rises. Investors might be regarded as consumers purchasing required service according to their specific preferences at desired price. This paper aims at analysis of demand for bonds on primary market using artificial neural networks. Design/methodology: We design a multilayered feedforward neural network trained by Levenberg-Marquardt algorithm in order to estimate demand for individual bonds based on parameters of particular offerings. Outcomes obtained by artificial neural network are compared with conventional econometric methods. Findings: Our results indicate that artificial neural network significantly outperformed standard econometric techniques and on examined sample of primary bond offerings achieved considerably better performance in terms of prediction accuracy and mean squared error. Originality: We show that proposed neural network is able to successfully predict demand for primary obligation offerings based on their specifications. Moreover, we identify relevant parameters of issues which are able to considerably affect total demand for given security. Our findings might not only help investors to detect marketable securities, but also enable issuing entities to increase demand for their bonds in order to decrease their offering price.
Keywords: bonds; public offering; neural networks; prediction; quality (search for similar items in EconPapers)
JEL-codes: Z (search for similar items in EconPapers)
References: Add references at CitEc
Citations Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:tuk:qipqip:v:18:y:2014:i:2:9
Access Statistics for this article
Quality Innovation Prosperity is currently edited by Kristina Zgodavova
More articles in Quality Innovation Prosperity from Technical University of Košice, Department of integrated management
Series data maintained by Matus Horvath ().