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Evaluating the Accuracy of Valuation Multiples on Indian Firms Using Regularization Techniques of Penalized Regression


This research study is conducted on companies in threeprominent sectors: Automobile, Banking and Steel—all threediverse and affected by different economic, fiscal and financial policies. Theauthor Gupta [1] attempts to extend the scope of study done earlier usingsimple linear regression for valuation of companies. Highlighting thelimitations of linear regression: multicollinearity and normality, the presentstudy is conducted by applying regularization techniques of machine learning. Ridge regression, LASSO and elastic nettechniques are employed to underscore this commonality of the set ofvaluation multiples. Theseregularization techniques are tested on data of Indian listed firms spanningacross twelve years from FY 07 to FY 2018 and the four multiples identified forthe study are 1) price to earnings (P/E), 2) price to sales (P/S), 3)enterprise value to earnings before interest tax depreciation and amortization(EV/EBIDTA) and 4) price to book value (P/BV). The empirical findings are bas......

【作者名称】: Vandana Gupta
【关 键 词】: Valuations, Prediction, Earnings, Book Value, Enterprise Value, Ridge
【网站名称】: 美国科学研究出版社
【期刊论文数据库】: [DBS_Articles_01]
【期刊论文编号】: 103,355,261
【摘要长度】: 1,000
【栏目名称】: 期刊
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