A business enterprise, striving for maximum revenue generation and sustainable profitability, comprehensively and continuously engages in exploring the key factors to expand the market base aiming to clinch the top seat. Owing to plurality of financial variables, it has always been an issue of academic and practical importance to arrive at a smaller but reliable set of financial indicators. Post Great Depression, empirical literature emphasized largely upon predicting bankruptcy of companies in dichotomous categories through linear combination of a few variables, e.g., Altman Z, Zeta, and Discriminant functions, etc. With the recent advancements in the IT, Data Analysis and Research, newer and robust statistical techniques are being used to obtain linear vectors of multiple groups to explain the financial performance. The present study selected twenty-eight financial variables representing major financial dimensions of a business to derive a variate or discriminant function of the significant variables to predict the revenue of the top performing companies (three pre-defined groups). Analysis was made separately for each of the 2 years (2016 and 2017). The obtained functions accurately classified 70% of the companies in each year. The results have implications for investors, management, credit rating agencies, mergers and acquisitions, etc., as their decisions depend upon sound prediction and forecasting.

为争取最大的创收和可持续的盈利能力而努力的企业全面，持续地探索关键因素，以扩大市场基础，以争取头把交椅。由于存在多种财务变量，因此得出一套较小但可靠的财务指标一直是学术和实践上的重要问题。大萧条过后，经验文献主要强调通过线性组合几个变量（例如Altman Z，Zeta和判别函数等）来预测两分类类别的公司的破产情况。随着IT，数据分析和研究的最新发展，正在使用更新而强大的统计技术来获取多个组的线性向量，以解释财务绩效。本研究选择了代表企业主要财务维度的28个财务变量，以得出重要变量的变量或判别函数，以预测表现最佳的公司（三个预先定义的组）的收入。对这2年（2016年和2017年）的每一年分别进行了分析。每年获得的职能准确地对70％的公司进行了分类。结果对投资者，管理层，信用评级机构，并购等都有影响，因为他们的决策取决于合理的预测和预测。对这2年（2016年和2017年）的每一年分别进行了分析。每年获得的职能准确地对70％的公司进行了分类。结果对投资者，管理层，信用评级机构，并购等具有影响，因为他们的决策取决于合理的预测和预测。对这2年（2016年和2017年）的每一年分别进行了分析。每年获得的职能准确地对70％的公司进行了分类。结果对投资者，管理层，信用评级机构，并购等都有影响，因为他们的决策取决于合理的预测和预测。