环境保护作为新时代的风向标，逐渐引领与其息息相关的新能源汽车走向大众视野。但是新能源汽车行业在其蓬勃发展的同时，风险也紧随其后。我国新能源公司也面临产量和销量双重不足的威胁，导致盈利能力下降，引发如偿债能力下降、融资困难等财务风险和压力，从而需要建立管控自身风险的财务风险预警模型，能够实现其价值，为新能源汽车发展保驾护航。

本文从研究背景出发，介绍相关概念和理论，了解A新能源汽车公司的财务风险情况，以A新能源公司从2018年第三季度到2019年第三季度的数据作为数据来源和数据支撑，通过对该公司所实施的财务风险预警模型进行分析研究，在国家层面也有政策变化所带来的影响，研究发现其风险在于公司存在资金不足、成本过高、缺少对自身公司的实际运行考量、制度不完善、缺乏管理经验、财务分析方法匮乏、行业发展太快无法跟上等问题。针对这些问题，本文对新能源汽车公司投资风险预警作为一个整体进行财务分析。针对A新能源汽车公司的现状进行分析，并再此作为研究对象的基础上，设计出A新能源汽车公司财务风险预警体系，确定指标选取的原则、依据，选择相关系数法、熵值法、功效系数法等预警方法，以其近几年财务情况作为数据支撑，根据相关数据图示，分析数据，建立模型，确定指标数值，最后对整个建模论证过程进行分析总结，系统化地提出对策和建议，切实有效解决该公司目前存在的问题和未来潜在的危机和威胁，得出解决方案并对公司未来提出展望。

研究成果表明：（1）财务预警帮助公司科学纠正当前发展方向，优化经营管理，为实现风险抵抗提供可靠依据。（2）分析财务风险预警结果的过程中，尝试从四大能力予以深入研讨，发现公司在盈利、营运、偿债、发展风险都存在不小的风险因素（3）此次财务预警模型选取Z-Score模型进行体系设计完善，让财务风险预警具有更高精准度。（4）针对公司最近五个季度的财务风险开展科学系统评估，改进后能够较为准确感知A公司所面临的实际财务风险。（5）通过对模型预警进行对比发现，本文采用的财务风险预警模型体系对A新能源汽车公司的财务风险预警有效。

New energy vehicles, led by environmental protection, the wind vane of the new era, have become an essential part of people’s lives due to its close relation to environmental friendly energy. In spite of its rapid development, the new energy vehicle industry is also accompanied by the arrival of risks. New energy companies in China are also confronted with the threat of insufficient production and sales, which leads to a decline in profitability and financial risks and pressures such as reduced solvency and financing difficulties. Therefore, it is necessary to establish a financial risk warning model to control their own risks, which can realize its value and escort the development of new energy vehicles.

This paper starts from the research background to introduce relevant concepts and theories and mainly elaborate the financial risks of A New Energy Automobile Company. With the data of A New Energy Company from the third quarter of 2018 to the third quarter of 2019 as the data source and data support, through the analysis and research of the financial risk warning model implemented by the company, there is also the impact of policy changes at the national level. The study shows that the risks come form the company itself. For instants, it has insufficient funds. The cost is too high, the actual operation of the company is not considered. The system is not perfect. Besides, the management experience is lacking and also the financial analysis method, and the industry is developing too fast. In view of these problems, this paper analyzes the investment risk early warning of new energy vehicle companies as a whole. A new energy vehicle company is taken as the research object to analyze its current situation, financial situation, and the corresponding plans and strategies. This paper designs the financial risk early warning system of A New Energy Automobile Company, determines the principle and basis of indicator selection, and select the entropy method, efficiency coefficient method and other warning methods. It uses the company’s financial situation in recent years as the data support, completes relevant data diagrams to analyze data, establish models, and determine the standard value of indicators. Finally, the thesis analyzes and summarizes the whole modeling demonstration process, and systematically proposes countermeasures and suggestions which are expected to efficiently and effectively solve the current problems and potential crises and threats of the company, draws solutions and puts forward prospects for the future of the company.

The research results show that: (1) Financial early warning helps the company scientifically correct the current development direction and optimize operation and management, and provides a reliable basis for risk resistance. (2) In the process of analyzing the early warning results of financial risks, we attempt to conduct in-depth research from the four capabilities, and find that the company has the highest financial risk in terms of profitability and operation, followed by development risk, among which the risk level corresponding to debt repayment risk is low. (3) The Z-Core model is used in the construction of the financial early warning model to make the financial risk warning model more accurate. (4) After carrying out scientific and systematic assessment on the financial risks of the Company in the last five quarters and providing further improvements, this model is able to more accurately perceive the actual financial risks faced by Model A Company. (5) By comparing the model and prediction effect, it is found that the financial risk early warning model adopted in this thesis is effective for the warning of A New Energy Automobile Company's financial risk.

[1] BirghamE. F , WachowicXJ. M. fundamentals offinancial management[J]. Journal of Finance,1979(5):123-125.

[2] Beaver W H , Mcnichols M F , Rhie J W . Have Financial Statements Become Less Informative? Evidence from the Ability of Financial Ratios to Predict Bankruptcy[J]. Review of Accounting Studies, 2005, 10(1):88-145.

[3] Li-Hong L I . Research on China Energy Financial Risk Early Warning:Based on PCA&ARMA Model[J]. On Economic Problems,2015,15(13):26-41.

[4] Liza. Financial statement analysis:a practitioner's guide[M]. John Wiley& Sons, 2002 :110-128.

[5] Shanshan Shao.Analysis on the Early Warning Index System of Financial Risk of New Energy Enterprises[J]. IOP Conference Series: Earth and Environmental Science,2019, 252(3) : 32-47 .

[6] Fitzpatrick P.A comparison of rations of successful industrial enterprises with those of failed firms,Certified Public Accountant[J]. Certified Public Account, 1932(02):588-615.

[7] Zhu Lei and Li Menghao and Metawa N.Financial Risk Evaluation Z-Score Model for Intelligent

IoT-based Enterprises[J]. Information Processing and Management, 2021, 58(6):121-155.

[8] Altman E. Financial rations, discriminant analysis and the prediction of corporate bankruptcy[J].

Journal of Finance,1968,32(13):579-620.

[9] Altman E. I, Hartzell J.,Peck M. Emerging Markets Corporate Bonds:A Scoring System [M]. New York: Salomon Brothers Inc., 1998,12(11):198-207.

[10] Edward I. Altman,Małgorzata Iwanicz‐Drozdowska,Erkki K.Laitinen,Arto Suvas.Financial Distress Prediction in an International Context:A Review and Empirical Analysis of Altman's Z‐ Score Model[J]. ournal of International Financial Management& Accounting,2017,28(2).32-42.

[11] Sumaira Ashraf,Elisabete G. S. Félix,Zélia Serrasqueiro. Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?[J].Journal of Risk and Financial Management,2019,12(2):12-45.

[12] Ohlson J. Financial Ratios and the Probabilistic Prediction of Bankruptcy[J].Journal of Accounting Research,1980,18(1):76-125.

[13] Pereira, J.M., M.Basto and A.F.D. Silva, The Logistic Lasso and Ridge Regression in Predicting Corporate Failure[J]. Procedia Economics and Finance,2016,20(2):152-198.

[14] M.D. Odom, Sharda R .A neural network model for bankruptcy prediction[C]// Neural Networks, 1990,20(6):32-76.

[15] Sezer, O.B. and A.M. Ozbayoglu,Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach[J]. Applied Soft Computing, 2018. 70(5):510-544.

[16] Hosaka, T., Bankruptcy prediction using imaged financial ratios and convolutional neural networks[J]. Expert Systems with Applications,2019,117(132):277-301.

[17] Feng Qi and Chen Han and Jiang Ruohan. Analysis of early warning of corporate financial risk via deep learning artificial neural network[J].Microprocessors and Microsystems, 2021, 87(65):45-121.

[18] Li Shuanbao and Liu Xiaoyan and Li Chengfei. Research on Risk Prediction Model of Internet Finance Based on Cloud Computing[J]. Journal of Mathematics, 2022, 20(17):20-122.

[19] Li Y C , Li H F , Wu Z R .Building Comprehensive Financial Early-Warning System Based on the AHP and Z - SCORE Model[C]// International Conference on Information Science & Engineering. IEEE, 2009.

[20] Tao Y H , Qi A L .Based on the Efficiency Coefficient-BP Neural Network Study of the Risk of Early Warning[J]. Management Science & Engineering, 2009, 3(3):27-66.

[21] DECHOW, P.M., et al., Predicting Material Accounting Misstatements*[J]. Contemporary Accounting Research,2011. 28(1):15-91.

[22] Aneta Masternak‐Janus, Magdalena Rybaczewska‐Błażejowska. Comprehensive Regional Eco‐Efficiency Analysis Based on Data Envelopment Analysis: The Case of Polish Regions[J]. Journal of Industrial Ecology,2017,21(1):170-200.

[23] Luciano Rodrigues, Lucas Rodrigues. Economic-financial performance of the Brazilian sugarcane energy industry: An empirical evaluation using financial ratio, cluster and discriminant analysis[J]. Biomass and Bioenergy,2018,108(3):278-298.

[24] Hou, Y. and Z. Yuan, Financial Risk Analysis and Early Warning Research Based on Data Mining Technology[J]. Journal of Physics: Conference Series, 2019. 1187(5): 51-106.

[25] 王克敏, 姬美光, 赵沫. 宏观经济环境、公司治理与财务困境研究[J]. 经济与管理研究, 2006(9):18-25.

[26] 赵国忠. 中国上市公司财务困境成因及相关对策实证研究[D]. 长春:吉林大学, 2008., 23(04):139-156.

[27] 艾学蛟.中国汽车业的危与机[J].长三角,2009(07):10-15.

[28] 饶静, 万良勇. 公司财务危机解救机制选择的影响因素研究——基于中国上市公司的经验证据[J]. 华东经济管理, 2017, 31(03):179-184.

[29] 石爽.企业财务风险预警方案优化路径研究[J].商场现代化,2022(02):152-154.

[30] 黄晓波,王慧.从公司治理视角看农业上市公司财务风险[J].财会月刊,2017(02):47-54.

[31] 王进朝,张永仙.公司生命周期、内部控制质量与财务风险[J].财会月刊,2017(02):44-55.

[32] 黄世忠,叶丰滢,李诗.碳中和背景下财务风险的识别与评估[J].财会月刊,2021(22):7-11.

[33] 于建玲,佟孟华,朱泽君.公司金融化对财务风险的影响——基于经济政策不确定性的调节效应研究[J].国际金融研究,2021(10):87-99.

[34] 杨嵩,张涤新.多重关联下PE/VC持股对公司财务风险影响的传导效应[J].审计与经济研究,2021,36(05):106-117.

[35] 张涛,潘燕晶.基于现金流管理的中小公司财务风险控制研究[J].财会通讯,2012(23):115-119.

[36] 伦淑娟.多元化战略下公司财务风险与控制——以恒大地产为例[J].财会通讯,2018(32):109-132.

[37] 黄贤环,吴秋生,王瑶.金融资产配置与公司财务风险:“未雨绸缪”还是“舍本逐末”[J].财经研究,2018,44(12):100-125.

[38] 黄敏.互联网公司财务风险控制研究[J].财会通讯,2018(14):109-112.

[39] 李帆,杜志涛,李玲娟.公司财务预警模型:理论回顾及其评论[J].管理评论,2011,23(09):139-157.

[40] 雷英.中国饲料行业上市公司的财务风险预警研究[J].时代金融,2020(26):65-68.

[41] 黄逸坤,曹电康,李晓栋,雷猛猛.基于Z-Score模型的体育用品公司财务风险预警研究[J].湖北体育科技,2020,39(12):1055-1101.

[42] 张玲. 财务危机预警分析判别模型及其应用[J]. 预测, 2000, 11(6):33-46.

[43] 于翠芳,王素娟.煤炭公司财务风险预警及实证分析[J].经济师,2011(09):160-163.

[44] 杨蕴毅, 孙中和, 卢靖. 基于Z-Score指标决策树的财务风险预警模型研究[D].模型, 2017(03):21-33.

[45] 张金贵, 陈凡, 王斌. 基于POS优化SVM制造业公司财务风险预警研究[J]. 会计之友, 2017(14):52-56.

[46] 李凯风, 丁宁. 低碳经济视角下基于功效系数法的财务风险预警——以W公司为例[J]. 会计之友, 2017(23):49-57.

[47] 喻旻昕.统计类单变量预警模型在国有资本投资运营公司财务风险预警中的运用[J].审计与理财,2019(07):4-36.

[48] 吴应宇, 吴凡, 仲伟俊. 基于单变量分析的我国上市公司两阶段财务预警模型实证研究——纺织业为例[J]. 东南大学学报, 2006(02):16-29.

[49] 霍雨佳. 公司债券违约的财务预警模型——基于单变量分析[J]. 现场营销（下旬刊）, 2016(09):88-119.

[50] 何玉梅. 公司财务危机预警模型之有效性选择[J]. 现代财经, 2011(05):66-85.

[51] 王文红, 杨慧琳. 财务预警的Z计分模型实证研究——来自中国汽车制造业上市公司的新证据[J]. 商业会计, 2015(01):13-21.

[52] 李光荣,李风强.基于几种神经网络方法的公司财务风险判别研究[J].经济经纬,2017,34(02):122-127.

[53] 周敏, 王新宇. 基于模糊优选和神经网络的公司财务危机预警[J]. 管理科学学报, 2002(03):83-90.

[54] 陈超, 李君. 化工类上市公司财务危机预警研究——基于BP人工神经网络模型[J]. 会计之友, 2012(07):77-87.

[55] 吴冲, 刘佳明, 郭志达. 基于改进粒子群算法的模糊聚类——概率神经网络模型的公司财务危机预警模型研究[J]. 运筹与管理, 2018(02):96-118.

[56] 崔笛.熵值法和功效系数法在企业财务风险预警应用研究——以A企业为例[J].商场现代化,2021(23):129-131.

[57] 顾晓安. 公司财务预警系统的构建[J]. 财经论丛（浙江财经学院学报）, 2000(04):58-74.

[58] 黄辉. 功效系数法在制药类公司财务预警中的应用[J]. 现代财经, 2003(04):38-48.

[59] 吴本杰. 基于功效系数法的财务危机预警分析[J]. 财会通讯, 2012(01):9-18.

[60] 韩彦峰. 基于功效系数法的餐饮业财务预警实例研究[J]. 中国商贸, 2012(03):98-106.

[61] 赵振智. 功效系数法在油气钻井公司财务预警中的应用[J]. 财会通讯, 2014(11):111-119.

[62] 杨秀端, 卢月根, 贾强. 出版公司财务风险预警系统的建立[J]. 财务与会计, 2015(10):69-78.

[63] 李霞, 干胜道. 基于功效系数法的非盈利组织财务风险模型[J]. 财经问题研究, 2016(04):70-102.

[64] 田任均.新能源企业财务风险预警指标构建与应对策略[J].财会学习,2020(01):26-29.

[65] 孙晓琳, 田也壮, 王文彬. 基于Kalman滤波的公司财务危机动态预警模型[J]. 系统管理, 2010(19):370-451.

[66] 鲍新中, 何思婧. 公司财务预警的研究方法及其改进——基于文献综述[J]. 南京审计学院学报, 2012(05):50-81.

[67] 蔡立新与李嘉欢, 大数据时代公司财务风险预警机制与路径探究[J]. 财会月刊, 2018(15): 33-45.

[68] 李慧,温素彬,焦然.基于盈利质量的DANP变权财务预警模型[J].系统工程理论与实践,2019,39(07):1630-1668.

[69] 侯旭华与彭娟, 基于熵值法和功效系数法的互联网保险公司财务风险预警研究[J]. 财经理论与实践, 2019. 40(05): 39-49.

[70] 侯旭华.基于模糊综合评价法的互联网保险公司财务风险预警研究[J].湖南社会科学,2019(04):87-103.