  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.
