Relative Valuation with Machine Learning

97 Pages Posted: 17 Dec 2020 Last revised: 9 Dec 2021

See all articles by Paul Geertsema

Paul Geertsema

University of Auckland Business School

Helen Lu

University of Auckland Business School; University of Auckland - Department of Accounting and Finance

Date Written: December 9, 2021

Abstract

We use machine learning to conduct relative valuation and identify comparable firms. The machine learning algorithm learns optimal decision rules to predict valuation multiples. We express these predictions as weighted averages of peer firm multiples. These weights are a measure of peer-firm comparability and can be used for selecting peer-groups. Machine valuations behave like fundamental value; over-valued stocks decrease in price and under-valued stocks increase in price in the following month. Our machine learning approach identifies valuation drivers that are consistent with theory. Profitability ratios, growth measures and efficiency ratios are the most important value drivers throughout our sample period. Interestingly, a number of accounting ratios belonging to the same category display diverging trends in their importance over time. Some of these offsetting trends are more pronounced for New Economy firms compared to Old Economy firms.

Keywords: Relative Valuation, Comparable Companies, Fundamental Analysis, New Economy, Value Relevance, Machine Learning

JEL Classification: G12, G14, M41, C5

Suggested Citation

Geertsema, Paul G. and Lu, Helen, Relative Valuation with Machine Learning (December 9, 2021). Available at SSRN: https://ssrn.com/abstract=3740270 or http://dx.doi.org/10.2139/ssrn.3740270

Paul G. Geertsema

University of Auckland Business School ( email )

12 Grafton Rd
Private Bag 92019
Auckland, 1010
New Zealand

Helen Lu (Contact Author)

University of Auckland Business School ( email )

12 Grafton Rd
Private Bag 92019
Auckland, 1010
New Zealand

HOME PAGE: http://www.business.auckland.ac.nz/people/hlu079

University of Auckland - Department of Accounting and Finance ( email )

Private Bag 92019
Auckland 1001
New Zealand

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