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作 者:邱宇 田烁楠 梁宇鹏 肖柔慈 Qiu Yu;Tian Shuonan;Liang Yupeng;Xiao Rouci
机构地区:[1]东北财经大学,辽宁大连116012 [2]北京字节跳动科技有限公司,北京100089 [3]招商银行股份有限公司大连分行,辽宁大连116001
出 处:《中国资产评估》2024年第2期17-27,共11页Appraisal Journal of China
基 金:国家社会科学基金一般项目“科技型中小企业经营风险的预警研判与规避策略”(项目编号:22BTJ056);辽宁省社科规划基金项目“经济转型背下东北老工业基地环境质量的综合评价研究”(项目编号:L14CTJ005)。
摘 要:引导更多资金投向先进制造业和战略性新兴企业是当前的重要任务。本研究以2021-2022年432支战略性新兴企业样本股的财务数据为基础,采用相关分析和Lasso回归进行两步指标筛选。为了补充战略性新兴企业信贷风险评估指标体系,使用KMV模型来测算违约距离DD,并将其作为变量加入CatBoost模型评估战略性新兴企业的信贷风险水平,剖析信贷违约产生的根源。研究发现:CatBoost算法能够准确评估战略性新兴企业的信贷风险水平,相较于逻辑回归和随机森林,在预测性能和精度上都有明显的提升。此外,除了企业利润收益财务效率指标外,科技创新和外部宏观环境对信贷风险也有显著影响,基于企业价值成长视角为商业银行在支持战略性新兴企业的融资实践中提供了经验借鉴和研判指导。Guiding more funds to invest in advanced manufacturing and strategic emerging enterprises is an important task at present.This study is based on the panel data of 432 sample stocks of strategic emerging companies in 2021-2022,and uses correlation analysis and Lasso regression to screen two-step indicators.In order to supplement the credit risk assessment index system of strategic emerging enterprises,the KMV model is used to measure the default distance DD,and it is added to the CatBoost model as a variable to evaluate the credit risk level of strategic emerging enterprises and analyze the root causes of credit default.It is found that CatBoost algorithm can accurately evaluate the credit risk level of strategic emerging enterprises,and has obvious improvement in forecasting performance and accuracy compared with logistic regression and random forest.In addition,in addition to the financial efficiency index of corporate profits and returns,technological innovation and external macro environment also have a significant impact on credit risk.Based on the perspective of corporate value growth,it provides experience reference and guidance for commercial banks to support the financing practice of strategic emerging enterprises.
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