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Debt Risk Analysis of Automotive Enterprises
In order to maintain their financial stability, businesses must recognize and manage the financial risks that are necessarily involved in their operations and output. Businesses may better comprehend their financial status and undertake efficient risk early warning by examining financial indicators. Academic research on the detection and mitigation of financial risks is extensive, and in an effort to increase the accuracy of assessments, an increasing number of academics are using multi-indicator systems for thorough analysis. Furthermore, non-statistical techniques including hierarchical analysis, B-S option pricing models, and artificial neural networks have shown extremely effective at handling complicated data and non-linear connections. As the economy transitions from high to medium-high growth, traditional and new energy vehicles in the automobile sector confront significant possibilities and problems. Traditional automakers aggressively converting to new energy cars include SAIC Motor, BAIC Group, and GAC Group. Companies that produce new energy vehicles, such XPeng, BYD, Tesla, Li Auto, and NIO, are becoming more competitive by developing new technologies and tapping into new markets. To enhance market competitiveness and sustainability, businesses must address issues like excessive inventory and price volatility that have been brought about by the new energy vehicle market’s explosive expansion through efficient financial risk management. The short- and long-term debt repayment capacities of traditional and new energy vehicle (NEV) enterprises are the main subjects of this study’s analysis of their financial statements. According to the findings, traditional automakers have more stable financial circumstances whereas NEV enterprises exhibit stronger short-term solvency and lower long-term financial risk in specific years with superior liquidity and lower debt ratios. The study attempts to offer resources for the automobile industry’s deleveraging, sustainable development, and inventory reduction.
References
- Ma H. Debt Risk Analysis of Real Estate Enterprises—A Case Study of “Hengda Real Estate”. Journal of Shaan xi Xueqian Normal University 2016; (08): 22–26.
- Liu X, Liu H.The Application of ANN-FLUS Model In Reconstructing Historical Cropland Distribution Changes: A Case Study of Vietnam from 1885 to 2000. Journal of Natural Resources 2024; 39(6): 1473–1492
- Liang J, Liu W. The Impact of Innovation Capability on Financial Performance in the New Energy Vehicle Industry-An Empirical Study of Listed Companies in China. Review of Industrial Economics 2023; (06): 5–26.
- Song Y. Discussion on the Feasibility Analysis of Financial Data of the Automotive Industry under the Dimension of OTA Technology and Policy to Help Vehicle Consumption—Taking Tesla as an Example. Auto Time 2023; (22): 183–185+198.
- Feng L. Parameter Optimization of End Face Robot Die Forging Based on GA Optimization ANN Method. Forging Equipment and Manufacturing Technology 2024; 59(03): 99–101.
- Pauwels K, Silva-Risso J, Srinivasan S, et al. New Products, Sales Promotions, and Firm Value: The Case of the Automobile Industry. Journal of Marketing 2004; 68(4): 142–156.
- Kumar G, Maqbool J. Financial Technology in the Automobile Industry. Proceedings of the Applied Management Conference 2018; 1(2).
- Dai L. Study on Financial Competitiveness of Listed Companies in New Energy Vehicle Industry—BYD as an Example. Advances in Economics, Management and Political Sciences 2023; 30: 94–102.
- Xu X. Construction of Financial Early Warning Model forListed Enterprises Based on SVM and ANN. Techniques of Automation and Applications 2021; 40(05):171–174.
- Huang H, Jiang W, Wang S. Research of Financial Early-Warning for Listed Companies Based on SVM. In Proceedings of the 2015 International Conference on Computational Science and Engineering, Qingdao, China, 20–21 July 2015; pp. 278–281.
- Yan X, Zhou Q. Research on Financial Development Capability of Listed Companies Based on Multiple Regression Model—Take BYD as an Example. Academic Journal of Business & Management 2022; 4(11).
- He Y, Rachev S. Exploring Implied Certainty Equivalent Rates in Financial Markets: Empirical Analysis and Application to the Electric Vehicle Industry. Journal of Risk and Financial Management 2023; 16(7): 344.
- Qian J. Research on Financial Risk Assessment of Xiaopeng Motors. E-Commerce Letters 2024; 13: 5024.
- Tao W. Research on Financial Risk Issues in the New Energy Vehicle Industry: Taking C Automobile Company as an Example. Modern Management 2024; 14: 2089.
Supporting Agencies
- Funding: Not applicable.