An Optimal Strategy Model for Purchasing Initial Public Offerings in China

Authors

Dong Huang, Computer Science and Technology Department, Harbin Institute of Technology Shenzhen graduate School, Shenzhen, Guangdong, China
Xiaolong Wang
Jingjing Ma
Ronggang Dou

Author Country (or Countries)

China

Abstract

An optimal recommend model for purchasing Chinese IPOs (Initial Public Offerings) based on artificial intelligence method is proposed here. In this paper, we focus on how to get the optimal income in a period of time. In order to avoid the problem of sparsity by separate method, combinations of classification, regression and maximum entropy are adopted to adjust the density of the filters in Double-layer filter model we propose.We improve the accuracy of the forecasting by structuring data in preprocessing, adjusting filters’ density level by the entropy of maximum entropy, the probability of the classification and regression algorithm. The experimental results show that the strategy model we propose achieves better forecasting accuracy than the methods separately. Therefore, the Double-layer filter model not only can forecast precisely but also has much application value.

Suggested Reviewers

N/A

Recommended Citation

Huang, Dong; Wang, Xiaolong; Ma, Jingjing; and Dou, Ronggang (2013) "An Optimal Strategy Model for Purchasing Initial Public Offerings in China," Applied Mathematics & Information Sciences: Vol. 07: Iss. 3, Article 20.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol07/iss3/20