Paper: arXiv 2405.12991

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.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 2.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper relies on established financial formulas (CAPM, WACC) and clustering algorithms (spectral, agglomerative) but presents them descriptively without complex derivations or novel mathematical advancement. Empirically, it outlines a theoretical methodology using publicly available tools but lacks any reported backtests, dataset descriptions, or statistical validation of results.

Research Flowchart

  flowchart TD
  A["Research Goal: Systematic Comps &<br>Cost of Equity for Private Firms"] --> B["Data: Financial &<br>Market Inputs"]
  B --> C["Methodology: Spectral &<br>Agglomerative Clustering"]
  C --> D["Computation: Cluster-based<br>Cost of Equity Calculation"]
  D --> E{"Key Outcomes"}
  E --> F["Reduced Time by<br>Orders of Magnitude"]
  E --> G["Enhanced Consistency<br>& Reliability"]