摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsRx记者从中国人民代表大会长沙发来的消息，研究称，本研究以中国A股上市公司为研究对象，考察了2018年至2021年绿色金融(GF)和绿色创新(GI)对公司信用评级(CR)绩效的影响。首次采用最小绝对收缩和选择算子(LASSOs)机器学习算法来选择影响公司信用绩效的关键驱动因素。我们的新闻记者引用了中南大学的研究，“然后，我们应用部分外拉索线性回归(POLR)和双选择拉索线性回归(DSLR)机器学习技术来检验GF和GI对CR的影响，主要结果表明，GF增加1%，CR降低0.26%，GI提高0.15%。”异质性分析显示，GF对西部地区重污染企业、非国有企业和企业的CR绩效有更显著的负面影响。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Changsha, People’s Rep ublic of China, by NewsRx correspondents, research stated, “This study investiga tes the impact of green finance (GF) and green innovation (GI) on corporate cred it rating (CR) performance in Chinese A-share listed firms from 2018 to 2021. Th e least absolute shrinkage and selection operators (LASSOs) machine learning alg orithms are first used to select the critical drivers of corporate credit perfor mance.” Our news journalists obtained a quote from the research from Central South Unive rsity, “Then, we applied partialing-out LASSO linear regression (POLR) and doubl e selection LASSO linear regression (DSLR) machine learning techniques to check the impact of GF and GI on CR. The main results reveal that a 1% i ncrease in GF diminishes CR by 0.26%, whereas GI promotes CR perfor mance by 0.15%. Moreover, the heterogeneity analysis reveals a more significant negative effect of GF on the CR performance of heavily polluting fi rms, non-state-owned enterprises, and firms in the Western region.”
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