Relative Valuation with Machine Learning

80 Pages Posted: 17 Dec 2020

See all articles by Paul Geertsema

Paul Geertsema

University of Auckland - Department of Accounting and Finance

Helen Lu

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

Date Written: December 1, 2020

Abstract

We use a decision-tree-based machine learning approach to perform relative valuation. Stocks are valued using market-to-book, enterprise-value-to-assets and enterprise-value-to-sales multiples. Our machine learning models reduce median absolute valuation errors by a minimum of 5.6 to 31.4 percentage points relative to traditional models, depending on the multiple used. The identified valuation drivers are consistent with theoretical predictions derived from a discounted cash flow approach. Accounting variables related to profitability, growth, efficiency and financial soundness are important valuation drivers. The valuations produced by machine learning models behave like fundamental values. A value-weighted strategy that buys (sells) undervalued (overvalued) stocks generates highly significant abnormal returns. When we use models trained on listed firms to value IPOs, machine learning models outperform traditional models in valuation accuracy and are better at identifying overpriced IPOs.

Keywords: Relative Valuation, Fundamental Analysis, Comparable Companies, IPOs, Machine Learning, Gradient Boosting Machines

JEL Classification: G12, G14, M41, C5

Suggested Citation

Geertsema, Paul G. and Lu, Helen, Relative Valuation with Machine Learning (December 1, 2020). Available at SSRN: https://ssrn.com/abstract=3740270 or http://dx.doi.org/10.2139/ssrn.3740270

Paul G. Geertsema

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

12 Grafton Rd
Private Bag 92019
Auckland, 1010
New Zealand

Helen Lu (Contact Author)

University of Auckland - University of Auckland, Business School ( email )

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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