Systematic Comparable Company Analysis and Computation of Cost of Equity using Clustering

TL;DR

This paper introduces a systematic, clustering-based method to efficiently and reliably compute the cost of equity and perform comparable company analysis for both private and public firms, reducing time and subjectivity.

Contribution

It presents a novel spectral and agglomerative clustering approach that improves consistency and significantly speeds up comparable company analysis and cost of equity computation.

Findings

Reduces analysis time by orders of magnitude.

Increases consistency and reliability of comps.

Applicable to both private and public companies.

Abstract

Computing cost of equity for private corporations and performing comparable company analysis (comps) for both public and private corporations is an integral but tedious and time-consuming task, with important applications spanning the finance world, from valuations to internal planning. Performing comps traditionally often times include high ambiguity and subjectivity, leading to unreliability and inconsistency. In this paper, I will present a systematic and faster approach to compute cost of equity for private corporations and perform comps for both public and private corporations using spectral and agglomerative clustering. This leads to a reduction in the time required to perform comps by orders of magnitude and entire process being more consistent and reliable.