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Firm's Financial Performance and Sustainability Efforts: Application of Classifier Models

机译:企业的财务绩效和可持续性努力:分类器模型的应用

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Numerous studies in academic literature investigated the impact of socially responsible activities on the financial performance of companies; those are committed to social events relative to companies that do not meet the socially responsible criteria. The existing theoretical and empirical research has supported both contradictory positions. We have chosen a different approach to provide an alternative dimension to existing literature. We have taken voluntary CSR disclosure as the dependant variable and attempted to find how the past financial performances of companies influence CSR activities. We test this hypothesis with 100 Indian companies included in BSE 100 index. The director’s report in the latest annual reports of companies were analysed to get voluntary disclosure of CSR activities. The study includes different financial performance variables: ROA, ROE, ROCE, debt to equity ratio, market capitalization and ownership as independent variables for analysis. Several binary classifier models are used for our empirical analysis. The binary model performances are validated with different performance measurement techniques such as F -measure, accuracy rates, balance error rate (BER), Matthews correlation coefficient (MCC), Kappa coefficient and AUROC. The model performance results show a better accuracy while comparing between predicted and actual values.
机译:学术文献中的大量研究调查了社会责任活动对公司财务业绩的影响;那些致力于与不符合社会责任标准的公司有关的社会事件。现有的理论和实证研究都支持这两个矛盾的立场。我们选择了一种不同的方法来提供现有文献的替代维度。我们将自愿的CSR披露作为因变量,并试图找出公司过去的财务绩效如何影响CSR活动。我们用BSE 100指数中包含的100家印度公司检验了这一假设。对公司最新年度报告中的董事报告进行了分析,以自愿披露企业社会责任活动。该研究包括不同的财务绩效变量:ROA,ROE,ROCE,债务权益比率,市值和所有权作为独立变量进行分析。一些二元分类器模型用于我们的经验分析。使用不同的性能测量技术(例如F度量,准确率,平衡误差率(BER),马修斯相关系数(MCC),Kappa系数和AUROC)验证了二元模型的性能。在对预测值和实际值进行比较时,模型性能结果显示出更高的准确性。

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