N-gram distribution and unification gain problem and its optimal solution

Abstract

N-gram distribution and unification gain is a type of problem in which objects obtain gain after a series of regular actions, such as ‘distributions’ and ‘unifications’. The uncertainty return forms of the gain lead to the complexity of the whole gain process in the problem we propose. The gain path is usually concurrent and consecutive to the timeline in practice; thus, we are unable to solve the problem and obtain the optimal path or overall gain at a certain time using the optimal path algorithm alone. Therefore, the N-gram distribution and unification gain model, which utilises a new dynamic programming algorithm in solving problems, is proposed. This procedure facilitates the solving of similar comprehensive gain problems and obtaining important information, such as the optimal gain path and the overall gain.

Keywords:

• distribution and unification
• gain
• dynamic programming
• optimal path

Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (No. 61173075 and 60973076).

Notes

In the experiment, we adopted the first-day sell-off method that is often adopted by most investors, and calculated the gain by the IPO first-day closing price.

Additional information

Notes on contributors

Dong Huang

Dong Huang is currently a PhD student in School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School. Her research interests are in the area of natural language processing, machine learning, data mining, and time series forecasting. The applications of her research are mainly in financial field – the recommendation of IPOs, the valuation of closed-end funds NAV, and the forecasting of US stock price.

Xiaolong Wang

Xiaolong Wang received the BE degree in Computer Science from the Harbin Institute of Electrical Technology, China, the ME degree in Computer Architecture from Tianjin University, China, and the PhD degree in Computer Science and Engineering from Harbin Institute of Technology in 1982, 1984, and 1989, respectively. He joined Harbin Institute of Technology as an assistant lecturer in 1984 and became an associate professor in 1990. He was a senior research fellow in the Department of Computing, Hong Kong Polytechnic University from 1998 to 2000. Currently, he is a professor of Computer Science at Harbin Institute of Technology. His research interest includes artificial intelligence, machine learning, computational linguistics, and Chinese information processing.

Ronggang Dou

Ronggang Dou is currently a master student in School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School. His research directions are text classification, heterogeneous information processing, natural language processing, and so on.

Shiwen Liu

Shiwen Liu is currently a graduate student in School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School. He searched for the financial data and collected the relevant news from the Internet, analysed the features of the data and the tendency of the debenture shares, then did prediction on the tendency of share price. His research interest includes Chinese processing and machine learning.

Jia Fang

Jia Fang is a graduate student in School of Computer Technology, Harbin Institute of Technology Shenzhen Graduate School. She received the BE degree in Software Engineer, Changchun University of Science and Technology. Her researches are mainly on machine learning, data mining, and the forecasting of financial time series.