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

Abstract

This research study is conducted on companies in three prominent sectors: Automobile, Banking and Steel — all three diverse and affected by different economic, fiscal and financial policies. The author Gupta [1] attempts to extend the scope of study done earlier using simple linear regression for valuation of companies. Highlighting the limitations of linear regression: multicollinearity and normality, the present study is conducted by applying regularization techniques of machine learning. Ridge regression, LASSO and elastic net techniques are employed to underscore this commonality of the set of valuation multiples. These regularization techniques are tested on data of Indian listed firms spanning across twelve years from FY 07 to FY 2018 and the four multiples identified for the 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 based on root mean square errors and learning curves, which corroborate the least prediction errors in P/S for auto sector, EV/EBIDTA for steel sector and P/BV for banking sector. As a byproduct, the author has also been able to pinpoint which one of the variables among them is the most important. The study concludes that, in spite of differing sectors, a certain set of common variables can be used across them to effectively assess company valuation (valuation multiples). The present work contributes to emerging market literature by evaluating the key multiples that drive sectors to apply non-traditional regression techniques.