Finance with R

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1. A one year financial model: sales - variable costs - fixed costs - taxes = net result

# Turn off scientific notation
options(scipen=999)
# Define starting values
t <- 0
sales <- 1000000
varcosts <- sales * 0.7
fixcosts <- 50000
gresult <- sales - varcosts - fixcosts
taxes <- gresult * 0.25
nresult <- gresult - taxes
finres <- data.frame(t, sales, varcosts, fixcosts, gresult, taxes, nresult)
finres

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2. A 10 year projection with growth rate

# Define a function for calculating growing end values
growthcalc <- function(startvalue, rate) {
  
  endvalue <- startvalue * (1 + rate)
  return(endvalue)
  
}
# Calculate a growing independent series and combine them in data frame
growth <- 0.15
inflation <- 0.012
for (s in c(1:10)) {
  
  t <- c(t, s)
  sales <- c(sales,
             growthcalc(sales[s],
                        growth)
             )
  fixcosts <- c(fixcosts,
                growthcalc(fixcosts[s],
                           inflation)
                )
  
}
finres <- data.frame(t, sales, fixcosts)
# Calculate dependent series
library(dplyr)
finres <- mutate(finres,
                 varcosts = sales * 0.7,
                 gresult = sales - varcosts - fixcosts,
                 taxes = gresult * 0.25,
                 nresult = gresult - taxes
                 )
# Plot series
library(ggplot2)
ggplot(finres, aes(x = t, y = nresult)) +
  geom_col(fill = rainbow(max(t)+1)) +
  theme(legend.position="none")

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3. A 10 year projection with risky growth rates

# Define starting values
t <- 0
sales <- 1000000
fixcosts <- 50000
# Calculate a growing independent series and combine them in data frame with uncertainty
stdevg <- 0.20
stdevi <- 0.005
for (s in c(1:10)) {
  
  t <- c(t, s)
  sales <- c(sales,
             growthcalc(sales[s],
                        rnorm(1, mean = growth, sd = stdevg)
                        )
             )
  fixcosts <- c(fixcosts,
                growthcalc(fixcosts[s],
                           rnorm(1, mean = inflation, sd = stdevi)
                           )
                )
  
}
finres.uncertain <- data.frame(t, sales, fixcosts)
# Calculate dependent series
finres.uncertain <- mutate(finres.uncertain,
                 varcosts = sales * 0.7,
                 gresult = sales - varcosts - fixcosts,
                 taxes = gresult * 0.25,
                 nresult = gresult - taxes
                 )
# Plot series
ggplot(finres.uncertain, aes(x = t, y = nresult)) +
  geom_col(fill = heat.colors(max(t)+1)) +
  theme(legend.position="none")

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4. Net present value calculations

# Function for calculating present values
presval <- function(futurevalue, discountrate, nperiods) {
  
  presentvalue <- futurevalue / (1 + discountrate)^nperiods
  
}
# Calculate present values of net results and add to data frame
discrate <- 0.05
finres.uncertain <- mutate(finres.uncertain,
                 nresult.pres = presval(nresult, discrate, t)
                 )
finres.uncertain

investment <- 4000000
netpresval <- -investment + sum(finres.uncertain$nresult.pres[-1])

The resulting Net Present Value equals: -8924

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Question

Go back to part 2 and build a model that assumes a growth rate of 15% p.a. for the first eight years and zero for the remaining two years.
HINT: make two adjustments.