造型EPS印度汽车专业

Ranjan Chaudhuri
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引用次数: 0

摘要

每股收益(EPS)对组织的财务管理至关重要。本文研究了EPS在最不复杂的结构形式下的建模。本文通过应用数据处理的群体方法(GMDH)算法,增加了计量经济建模的现有知识体系。GMDH的工作原理是选择标准或从少量数据点中选择模型。GMDH通过对大量模型的变换,确定最优复杂度模型。因此,在此预测过程中得到了最佳模型。该模型的主观误差最小。该分析得到了印度汽车专业学生案例研究的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling EPS of an Indian auto major
The earnings per share (EPS) are critical for financial management of organisations. This paper examines the modelling of the EPS in the least complex structural form. The paper adds to the existing body of knowledge in econometric modelling through application of the group method of data handling (GMDH) algorithms. The GMDH works on the principle of criteria of choice or selection of the model to be made from a small number of data points. GMDH then determines the model of optimum complexity by shifting through a large number of models. Consequently, the best model is obtained in this prediction process. The subjective error of the model is minimal. The analysis is supported by case study of an Indian auto major.
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