Author(s)

Minqiang Zhang ¹

Affiliation(s)

¹ Business School, Dongguan City University, Dongguan, Guangdong, 523000, China

Corresponding Author

Minqiang Zhang

ABSTRACT

As an emerging sector of China's capital market, the Science and Technology Innovation Board specialize in serving high-tech and innovative enterprises. However, the existing stock issuance pricing system on the Science and Technology Innovation Board has some adaptability, and new pricing models and algorithms need to be studied to better meet the needs of listed companies on the Science and Technology Innovation Board. Future research should focus on building pricing models and algorithms suitable for the Science and Technology Innovation Board, taking into account the characteristics of enterprises on the board, such as technological innovation capabilities, business models, technological barriers, etc. This helps to more accurately evaluate the value of enterprises and estimate reasonable issuance prices. At the same time, the issuance pricing mechanism has been optimized, and market-oriented pricing methods have been introduced, such as auctions and bidding, to improve market efficiency and liquidity, and meet the financing needs of different types of companies. The experimental results showed that the three-year net profit of stock companies using the traditional model algorithm has increased from 250 million yuan to 310 million yuan, while stock companies using the Sci Tech Innovation pricing model algorithm have increased from 250 million yuan to 520 million yuan. Financial company C using the traditional model algorithm has increased its three-year net profit from 350 million yuan to 470 million yuan, while financial companies using the Sci Tech Innovation pricing model algorithm have increased their net profit from 350 million yuan to 730 million yuan. From this, it can be seen that the research on pricing models and algorithms for listed companies on the Science and Technology Innovation Board can play an important role in the future, promoting the prosperity and sustainable development of the Science and Technology Innovation Board market.

KEYWORDS

Science and Technology Innovation Board, Listed Companies, Pricing Models, Algorithm Optimization, Stock Pricing

CITE THIS PAPER

Minqiang Zhang, Construction and Optimization of Pricing Models and Algorithms for Listed Enterprises on the Science and Technology Innovation Board. Financial Engineering and Risk Management (2023) Vol. 6: 51-59. DOI: http://dx.doi.org/10.23977/ferm.2023.060708.

REFERENCES

[1] Peng Li. Research on information Disclosure of listed companies on the Science and Technology Innovation Board under the registration system. Chief Financial Officer, 2022, 18(16):103-105. 
[2] Xing Jinjia, Cao Xia. Research on the relationship between executive characteristics and corporate performance of listed companies. Statistics and Applications, 2022, 11(5):1302-1311. 
[3] Wang Ke. Research on the impact of R&D input on the growth of Science and Technology board enterprises: Based on the perspective of enterprise life cycle. Advances in Applied Mathematics, 2022, 11(8):6055-6061. 
[4] Zhang Zhuhong. The validity and application of the gambling agreement in the Science and Technology Innovation Board. Northern Economy and Trade, 2021, 000(003):87-90. 
[5] Zhang Linna. Optimization Design and Practice of Computer Programming Based on Mathematical Algorithm. Automation Technology and Application, 2019, 38(8):38-40. 
[6] Li Jingxin. An empirical study on income constraint, exogenous consumption habits and stock pricing. New marketing, 2019, 000(006):136-138. 
[7] Wang LAN, He Yuanxia, Jiao Dengdan. Prediction of stock fluctuation trend based on logistic regression Model: A case study of Bank of Guiyang. Chinese Management Information Technology, 2023, 26(4):156-158. 
[8] Cui Huiying. Profit analysis of stock market gambling preference based on multi-factor model. Exploration of Financial Theory, 2019, 000(006):30-41. 
[9] Yang Shumin. Efficient market hypothesis discusses the efficiency of Chinese stock market. New Business Week, 2020, 000(016):17-18.
[10] Liu Yun. Analysis on the international market of small reactor products. Port Economy, 2021, 000(015):62-63. 
[11] LI Zhuoyang, Li Long. Analysis of market transmission mechanism of consumption upgrading in the new era. Business Times, 2021, 000(016):36-39. 
[12] Liu Hui, Wang Wenbo. Research on equity incentive Innovation and Application Practice of Science and Technology Board. Journal of Nanyang Institute of Technology, 2021, 13(3):24-29. 
[13] Tian Xuejiao, Bao Xinzhong, Yang Dafei. Impact of technological innovation on Growth of GEM listed companies: GMM estimation based on dynamic panel data. Enterprise Economics, 2020, 39(11):32-37. 
[14] Zhang Jiangjiang, Cui Zhihua. Research review of high-dimensional multi-object integration algorithms. Journal of Nanchang Institute of Technology, 2021, 040(004):67-76. 
[15] Zhang Qianzhong, Jingwei Ming. Optimization analysis of computer programming by Mathematical algorithms. Science and Technology Information, 2021, 19(32):29-31. 
[16] Jin Zhong. Teaching Thinking of minimax problem in optimization algorithm. Innovative Education Research, 2019, 7(5):628-634. 

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