Parameter Estimation Methods of Required Rate of Return on Stock

TL;DR

This paper introduces new statistical methods, including maximum likelihood, Bayesian, and Kalman filtering, to estimate the required rate of return in stock valuation models, demonstrated on S&P 500 firms over 32 years.

Contribution

It presents novel estimation techniques for the required rate of return in dividend discount and private company valuation models using advanced statistical methods.

Findings

Methods effectively estimate required return on stocks.

Application to S&P 500 data validates the approaches.

Provides practical tools for stock valuation analysis.

Abstract

In this study, we introduce new estimation methods for the required rate of return of the stochastic dividend discount model (DDM) and the private company valuation model, which will appear below. To estimate the required rate of return, we use the maximum likelihood method, the Bayesian method, and the Kalman filtering. 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, suggested methods can be used to estimate the required rate of return.