Development Analysis of Chinese and American Technology Innovation Board Based on Time Series Model
European Business & Management
Volume 5, Issue 5, September 2019, Pages: 64-69
Received: Oct. 11, 2019;
Accepted: Oct. 30, 2019;
Published: Nov. 6, 2019
Views 335 Downloads 59
Wei Wei, School of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai, China
Ma Yuyi, School of Economics and Management, Shanghai Ocean University, Shanghai, China
Hu Junlong, School of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai, China
Follow on us
The technology innovation board mainly serves scientific and technological innovation enterprises that conform to the national strategy, breakthrough key core technologies and have high market recognition. It is a crucial step for China to implement the strategy of strengthening China through science and technology and can promote the mutual growth of the capital market and scientific and technological innovation. For its research on the development of our country has a far-reaching significance. First, we conducted cluster analysis, divided the company into different industries, took the price-sales ratio as the valuation level, established a time series model, and analyzed the valuation premium or discount of various industries in China and the United States. The relation between market index, underlying index, and liquidity index is obtained by polynomial regression fitting equation. When forecasting the fundamental indicators and liquidity indicators of China and our market in 2019, the author USES the existing data to predict the fundamental indicators and liquidity indicators in 2019 by using the time series model and then obtains the valuation indicators of the two markets in 2019. Finally, in order to study the development of gem, we consulted the fundamental data of 93 companies, referred to the quantitative stock index model of our market and liquidity index of the Chinese market, and thus predicted the valuation level of the first batch of tech create board companies after listing.
Time Series Model, Cluster Analysis, Multiple Factor Regression Fitting, Grayscale Prediction, Factor Weight Analysis
To cite this article
Development Analysis of Chinese and American Technology Innovation Board Based on Time Series Model, European Business & Management.
Vol. 5, No. 5,
2019, pp. 64-69.
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Zai-Ming H U. The Time Gap between Fiscal Investment and Regional Innovation Output--The Analysis of VAR Model Based on Time Series Data [J]. Economic Survey, 2017.
Chu Z, Li B, Xiao H, et al. An Empirical Research on Indigenous Technological Innovation Process in Shenzhen City of China Based on Time Series Model [J]. Journal of Applied Sciences, 2013, 13 (8): 1245-1250.
Geng Q F. Analysis of the Dynamic Correlation between China’s Second Board and SME Board Based on Different Methods [J]. Applied Mechanics & Materials, 2014, 687-691: 4938-4941.
Yan-Yan H U. An Analysis of the Relationship between Cultural Trade and Economic Growth——Based on Time Series Model [J]. Economic Survey, 2015.
Cao H, Tian L, Li S, et al. An integrated emergency response model for toxic gas release accidents based on cellular automata [J]. Annals of Operations Research, 2017, 255 (1-2): 617-638.
Li M. Diversity of Board Interlocks and the Impact on Technological Exploration: A Longitudinal Study [J]. Journal of Product Innovation Management, 2019, 36 (4): 490-512.
Lu W, Skjetne R, Løset S. A method for real-time estimation of full-scale global ice loads on floating structures [J]. Cold Regions Science and Technology, 2018: S0165232X17306018.
Zhang Yanjun, Yang Xiaodong, Liu Yi, Zheng Dayuan, Bi Shujun. Research on the Frame of Intelligent Inspection Platform Based on Spatio-temporal Data. Computer & Digital Engineering [J], 2019, 47 (03): 616-619+637.
Bohl M A, Xu D S, Daniels L, et al. The Barrow Innovation Center case series: Early clinical experience with novel, low-cost techniques for bone graft containment in the posterolateral fusion bed. [J]. World Neurosurgery, 2018, 116: 285-295.
Li H, Mollier A, Ziadi N, et al. The long-term effects of tillage practice and phosphorus fertilization on the distribution and morphology of corn root [J]. Plant and Soil, 2017, 412 (1-2): 97-114.
Z. Zhao, J. Wang and Y. Liu, "User Electricity Behavior Analysis Based on K-Means Plus Clustering Algorithm," 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC), Dalian, China, 2017, pp. 484-487. doi: 10.1109/ICCTEC.2017.00111.
Yi Liu, Jiawen Peng, and Zhihao Yu. 2018. Big Data Platform Architecture under The Background of Financial Technology: In The Insurance Industry As An Example. In Proceedings of the 2018 International Conference on Big Data Engineering and Technology (BDET 2018). ACM, New York, NY, USA, 31-35.
Rolim C, Baptista P, Duarte G, et al. Real-Time Feedback Impacts on Eco-Driving Behavior and Influential Variables in Fuel Consumption in a Lisbon Urban Bus Operator [J]. IEEE Transactions on Intelligent Transportation Systems, 2017, PP (99): 1-11.
Adepetu A, Keshav S. The relative importance of price and driving range on electric vehicle adoption: Los Angeles case study [J]. Transportation, 2017, 44 (2): 1-21.