Machine Learning in Business Valuation Using Merger and Acquisition Data

Business valuation deals with the estimation of a company’s value, using information from markets and the company’s financial statements. Such valuation is important when assessing mergers and acquisitions (M&A) of companies or the sale of an owner’s share in a business. Three different approaches are commonly used for business valuation: the income approach (estimating future income), the asset-based approach (valuating the current assets), and the market approach (comparing with similar businesses). The proposed project aims to extend the market approach by developing and applying modern computer science and statistical techniques to business valuation. As part of this project, the intern will develop a statistical method for business valuation, using available M&A transaction data. The partner organization will benefit from a more efficient process of business valuation as well as more precise valuation outcomes.

Faculty Supervisor:

Christoph Frei


Qianhong Huang


Vario Ventures


Statistics / Actuarial sciences



University of Alberta



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