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  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.
Cite this paper
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