Title :
A Nonparametric Approach to Pricing Convertible Bond via Neural Network
Author :
Zhou, Wei ; Yang, Meiying ; Han, Liyan
Author_Institution :
Beijing Univ. of Aeronaut. & Astronaut., Beijing
fDate :
July 30 2007-Aug. 1 2007
Abstract :
The paper proposes a nonparametric method for estimating the price of convertible bonds using artificial neural networks (ANNs). Market convertible bonds prices quoted on the Shanghai stock exchange are used for performance comparison between the parametric Black-Scholes (BS), binary tree model and the proposed ANN model. The input variables of model are investigated and the results are compared. The results show that the performances of the proposed model produce often better convertible bonds price than other parametric models. The model simulation results slightly lower than actual market prices generally, which are significant and differ from previous literatures.
Keywords :
neural nets; nonparametric statistics; pricing; stock markets; trees (mathematics); Shanghai stock exchange; artificial neural networks; binary tree model; market convertible bonds; nonparametric approach; parametric Black-Scholes; pricing convertible bond; Artificial neural networks; Bonding; Cost accounting; Data security; Economic indicators; Forward contracts; Input variables; Neural networks; Pricing; Stochastic processes;
Conference_Titel :
Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-0-7695-2909-7
DOI :
10.1109/SNPD.2007.399