首页|New Findings Reported from Shandong University Describe Advances in Machine Lear ning (Machine Learning for Predicting Corporate Violations: How Do Ceo Character istics Matter?)
New Findings Reported from Shandong University Describe Advances in Machine Lear ning (Machine Learning for Predicting Corporate Violations: How Do Ceo Character istics Matter?)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting out of Weihai, People's Republic of China, by NewsRx editors, research stated, "Based on upper echelon theory, we e mploy machine learning to explore how CEO characteristics influence corporate vi olations using a large-scale dataset of listed firms in China for the period 201 0-2020. Comparing ten machine learning methods, we find that eXtreme Gradient Bo osting (XGBoost) outperforms the other models in predicting corporate violations ." Financial support for this research came from Ministry of Education, China. Our news journalists obtained a quote from the research from Shandong University , "An interpretable model combining XGBoost and SHapley Additive exPlanations (S HAP) indicates that CEO characteristics play a central role in predicting corpor ate violations. Tenure has the strongest predictive power and is negatively asso ciated with corporate violations, followed by marketing experience, education, d uality (i.e., simultaneously holding the position of chairperson), and research and development experience. In contrast, shareholdings, age, and pay are positiv ely related to corporate violations. We also analyze violation severity and viol ation type, confirming the role of tenure in predicting more severe and intentio nal violations."
WeihaiPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningShandong University
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