Relative Valuation with Machine Learning

• 成果类型: Article
• 署名作者: Geertsema, Paul; Lu, Helen
• 署名单位: University of Auckland
• 刊物名称: JOURNAL OF ACCOUNTING RESEARCH
• ISSN/ISSBN: 0021-8456
• DOI: 10.1111/1475-679X.12464
• 发表日期: 2023
• 页码: 329-376
• 关键词: VALUE-RELEVANCE; peer; selection; earnings; mergers; cost

摘要

We use machine learning for relative valuation and peer firm selection. In out-of-sample tests, our machine learning models substantially outperform traditional models in valuation accuracy. This outperformance persists over time and holds across different types of firms. The valuations produced by machine learning models behave like fundamental values. Overvalued stocks decrease in price and undervalued stocks increase in price in the following month. Determinants of valuation multiples identified by machine learning models are consistent with theoretical predictions derived from a discounted cash flow approach. Profitability ratios, growth measures, and efficiency ratios are the most important value drivers throughout our sample period. We derive a novel method to express valuation multiples predicted by our machine learning models as weighted averages of peer firm multiples. These weights are a measure of peer-firm comparability and can be used for selecting peer-groups.