https://github.com/m-muecke/firmvaluesim

Monte Carlo simulation for company valuation
https://github.com/m-muecke/firmvaluesim

financial-analysis monte-carlo-simulation r risk-management valuation wacc

Last synced: over 1 year ago
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Monte Carlo simulation for company valuation

• Host: GitHub
• URL: https://github.com/m-muecke/firmvaluesim
• Owner: m-muecke
• License: other
• Created: 2018-03-04T22:06:39.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2022-09-26T17:50:53.000Z (almost 4 years ago)
• Last Synced: 2025-04-01T23:14:14.753Z (over 1 year ago)
• Topics: financial-analysis, monte-carlo-simulation, r, risk-management, valuation, wacc
• Language: R
• Homepage:
• Size: 14.8 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE

Awesome Lists containing this project

README

          
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# firmValueSim

[![R-CMD-check](https://github.com/maximilian-muecke/firmValueSim/workflows/R-CMD-check/badge.svg)](https://github.com/maximilian-muecke/firmValueSim/actions)

[![Codecov test coverage](https://codecov.io/gh/maximilian-muecke/firmValueSim/branch/master/graph/badge.svg)](https://codecov.io/gh/maximilian-muecke/firmValueSim?branch=master)

## Overview

To minimize the downside of the assumptions made in an intrinsic valuation approach, the possibility exists to model the risk via a Monte Carlo simulation. Each variable in the model that is derived through assumption that can be stochastically modeled. The main purpose of a Monte Carlo in the context of a valuation is to achieve a risk management component, by integrating the expected value of multiple parameter outcomes. The two main approaches of risk management in valuation are either via a tree-based or simulation approach. The advantages of using simulations instead of decision-trees is that the flexibility of not only choosing a binary input approach, but instead choosing an underlying distribution. The first-step in the simulation is assigning a distribution of the variable, either through historical data, most likely outcome or market consensus. After the distributions are assigned, a single value of each parameter distribution gets sampled and the valuation is done as per usual.

## Installation

```{r eval = FALSE}

devtools::install_github("maximilian-muecke/firmValueSim")

```

## Usage

```{r}

library(firmValueSim)

library(ggplot2)

# simulate stock prices via the Gordon Growth model

prices <- ddm_sim(

  1.5, r = 0.07, g_mu = 0.03, g_sigma = 0.01, n_sim = 10000, seed = 12345

)

# visualize distribution of prices

df <- data.frame(prices = prices)

ggplot(df, aes(x = prices)) +

  geom_histogram(binwidth = 5)

```

## References

- Abrams, J. B. (2001). Quantitative business valuation. New York: McGraw-Hill.