TY - GEN
T1 - Black-scholes versus artificial neural networks in pricing call warrants
T2 - 3rd International Conference on Natural Computation, ICNC 2007
AU - Wei, Zhou
AU - Meiying, Yang
AU - Liyan, Han
PY - 2007
Y1 - 2007
N2 - The back-propagation neural network is used to pricing call warrants, and the input variables of network model are investigated. The market call warrants prices quoted on Shanghai Stock Exchange and Shenzhen Stock Exchange are used to train and simulate the network model. The results show that the performances of the proposed network model produce better call warrant prices than Black-Scholes, and better depict the price characteristics of China's call warrants. The pricing error of Black-Scholes is detailed analyzed, and the market particularities of China's call warrants with different contract terms and price characteristics are also discussed.
AB - The back-propagation neural network is used to pricing call warrants, and the input variables of network model are investigated. The market call warrants prices quoted on Shanghai Stock Exchange and Shenzhen Stock Exchange are used to train and simulate the network model. The results show that the performances of the proposed network model produce better call warrant prices than Black-Scholes, and better depict the price characteristics of China's call warrants. The pricing error of Black-Scholes is detailed analyzed, and the market particularities of China's call warrants with different contract terms and price characteristics are also discussed.
UR - https://www.scopus.com/pages/publications/38049053672
U2 - 10.1109/ICNC.2007.285
DO - 10.1109/ICNC.2007.285
M3 - 会议稿件
AN - SCOPUS:38049053672
SN - 0769528759
SN - 9780769528755
T3 - Proceedings - Third International Conference on Natural Computation, ICNC 2007
SP - 528
EP - 532
BT - Proceedings - Third International Conference on Natural Computation, ICNC 2007
Y2 - 24 August 2007 through 27 August 2007
ER -