Shanghai University of Finance and Economics Zhejiang College
*Corresponding Author Email: firstname.lastname@example.org
In order to accurately analyze the convertible bond market, reasonably determine its interest rate and volatility, reduce the risk of convertible bond buyers, and improve their purchase rate, the current convertible bond algorithm is analyzed. Combined with Black-Scholcs pricing model, Radial Basis Function (RBF) neural network algorithm is improved, and RBF neural network is used for orthogonal least squares calculation. The central position of RBF neural network is guaranteed to remain unchanged while the algorithm remains unchanged. The least square method is used to calculate the weight vector of the network again, and the width of the RBF network is set according to the obtained data, in order to find the optimal width. Relevant data samples of convertible bonds are collected by Tonghuashun Ifind Financial Data System and other systems, and the data are simulated and calculated by Matlab software, and the pricing simulation results of convertible bonds under three neural networks are compared. The results show that the modified RBF neural network algorithm can evaluate the price of convertible bonds. And its calculation error is less than the other two neural network algorithms. This indicates that the RBF neural network algorithm designed by us can accurately predict the pricing of convertible bonds in combination with Black-Scholcs pricing, which provides guidance and data support for the pricing of convertible bonds and market investment in the future.
Neural network; Convertible bond; Simulation model; Pricing; Black-Scholcs model
Bowen Li. The Role of Improved Neural Network Algorithm in Convertible Bond Market Analysis. Academic Journal of Business & Management (2019) Vol. 1, Issue 3: 26-35. https://doi.org/10.25236/AJBM.2019.010304.
 Xiao L, Guo B, and Shen Y. "(2012)Application of Improved BP Neural Network in Software Energy Consumption Analysis." Computer Engineering, , vol 38.no 10, pp. 185-187.
 Simões S. S, Silva I, Ajenjo A C, & Dias M J (2014). Optimization of cultural conditions using response surface methodology versus artificial neural network and modeling of l-glutaminase production by bacillus cereus mtcc 1305. Glutaminase isoenzymes as key regulators in metabolic and oxidative stress aga. Forensic Science International, vol 243. no 6, pp. 117-125.
 Chattopadhyay M, Dan P K, Mazumdar S, et al (2012). Application of Neural Network in Market Segmentation: A Review on Recent Trends[J]. Management Science Letters, vol 2. no 2, pp. 425-438.
 FajäÃ¬Kovã, K., Stehlã¬Kovã, B., Cveä Kovã, V(2017)., & Rapant, S. Application of artificial neural network in medical geochemistry. Environmental Geochemistry & Health, , vol 39. no 6, pp. 1-17.
 Cheng K, Guo L M, Wang Y K, & Zafar M T (2017). Application of clustering analysis in the prediction of photovoltaic power generation based on neural network. IOP Conference Series: Earth and Environmental Science, vol 93, pp. 012024.
 Moayedi H, Mosallanezhad M, Rashid A S A, Jusoh W A W, & Muazu, M A.(2019) A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications. Neural Computing and Applications, vol 6, pp. 1-24.
 Botsis T, Foster M, Arya N, Kreimeyer K, Pandey A, & Arya D (2017). Application of natural language processing and network analysis techniques to post-market reports for the evaluation of dose-related anti-thymocyte globulin safety patterns. Applied Clinical Informatics, vol 08. no 02, pp. 396-411.
 Seifollahi, Saeed, and Shajari M (2018). "Word sense disambiguation application in sentiment analysis of news headlines: an applied approach to FOREX market prediction." Journal of Intelligent Information Systems, pp. 1-27.
 Bektić, Demir, and Regele T. (2017)"Exploiting uncertainty with market timing in corporate bond markets." Journal of Asset Management, pp. 1-14.
Berna Simsek, & Tüysüz F. (2018) An application of network data envelopment analysis with fuzzy data for the performance evaluation in cargo sector. Journal of Enterprise Information Management, vol 31.no 4, pp. 00-00.
Pan X Q, Chen Y, and Kuo C J J.( 2017) "Design, analysis and application of a volumetric convolutional neural network. "Journal of Visual Communication & Image Representation .vol 46, pp. 128-138.