Paper: arXiv 2305.19708

Abstract

In this study, we introduce new estimation methods for the required rate of returns on equity and liabilities of private and public companies using the stochastic dividend discount model (DDM). To estimate the required rate of return on equity, we use the maximum likelihood method, the Bayesian method, and the Kalman filtering. We also provide a method that evaluates the market values of liabilities. We apply the model to a set of firms from the S&P 500 index using historical dividend and price data over a 32–year period. Overall, the suggested methods can be used to estimate the required rate of returns.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced stochastic models like Markov-Switching VAR, Kalman filtering, and Bayesian estimation, which indicates high mathematical complexity. The empirical application uses 32 years of S&P 500 data to test the methods, showing solid backtesting and data implementation.

Research Flowchart

  flowchart TD
  A["Research Goal: Estimate Required Rate of Return"] --> B["Input: Historical Data"]
  B --> C["Stochastic Dividend Discount Model DDM"]
  C --> D["Estimation Methods"]
  D --> E["Computational Processes"]
  E --> F["Key Findings Outcomes"]
  subgraph D ["Estimation Methods"]
      D1["Maximum Likelihood"]
      D2["Bayesian Method"]
      D3["Kalman Filtering"]
      D4["Liability Valuation"]
  end
  subgraph E ["Computational Processes"]
      E1["Estimate R_e Equity"]
      E2["Estimate R_d Debt"]
      E3["32-Year Application"]
  end
  subgraph F ["Key Findings Outcomes"]
      F1["Validated Methods"]
      F2["Applicable to Private Public Firms"]
      F3["S&P 500 Results"]
  end