Issue |
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
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Article Number | 02032 | |
Number of page(s) | 9 | |
Section | Machine Learning and Energy Industry Structure Forecast Analysis | |
DOI | https://doi.org/10.1051/e3sconf/202021402032 | |
Published online | 07 December 2020 |
Financial Performance Analysis of Backdoor Listed Companies
Huazhong University of Science and Technology, Room 502, Building Deya, LuohuHuayuan, ShishanTownship, Foshan City, China
IPO listing threshold requirements are high, many enterprises have chosen to backdoor listing due to the restrictions of objective factors. In order to study the impact of backdoor listing on corporate financial performance, this paper adopts the method of case analysis and takes SF Express, a typical representative of express delivery industry, as an example to analyze whether SF Express has improved its financial performance after backdoor listing by using financial indicators such as debt paying ability, operating ability, profitability and growth ability. The results show that the overall financial performance of SF Express has been improved due to the sufficient capital and the expansion of business scope. The innovation of this paper lies in the horizontal short-term comparison of financial data of SF Express which is backdoor listed and DEPPON Express which is IPO listed in express industry. The research shows that backdoor listing is more conducive to the improvement of the financial performance of enterprises in the short term, providing certain reference value for enterprises that want to go public by backdoor listing in the express industry in the future. However, when deciding to go public, different enterprises should choose suitable listing schemes according to their own financial characteristics and understand the risk and the strategic goal of the enterprise’s own development.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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