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Uncertainty in a financial model
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#First we are going to set up probability distributions for our beliefs about the inputs | |
#We've been told ARPU is about £7 and it's very unlikely to be higher than £10 or lower than £4 | |
#So we'll go for a normal distribution centred at 7 with 5% and 95% quantiles at 4 and 10 | |
#Show how we get the variance | |
arpu.sd<-3/1.96 | |
x<-seq(0, 15,by=0.5) | |
d<-dnorm(x, 7, arpu.sd) | |
plot(x, d, type='l') | |
#Do the same for acquistion and churn | |
acq.sd<-0.02/1.96 | |
x<-seq(0, 0.2,by=0.001) | |
d<-dnorm(x, 0.05, acq.sd) | |
plot(x, d, type='l') | |
ch.sd<-0.01/1.96 | |
x<-seq(0, 0.2,by=0.001) | |
d<-dnorm(x, 0.02, ch.sd) | |
plot(x, d, type='l') | |
#I'm to lazy to do the maths for this so I'll write a function | |
n.cust<-50000 | |
revenue<-function(arpu, acq, ch, n.cust){ | |
num.cust<-n.cust | |
for (m in 1:12){ | |
num.cust<-num.cust+acq*num.cust-ch*num.cust | |
} | |
return(num.cust*arpu) | |
} | |
#Now let's simulate 10k values from each of our distributions | |
sim.arpu<-rnorm(10000, 7, arpu.sd) | |
sim.acq<-rnorm(10000, 0.05, acq.sd) | |
sim.ch<-rnorm(10000, 0.02, ch.sd) | |
#We can then apply the function to these values to get a distribution for end of year revenue | |
sim.rev<-mapply(revenue, sim.arpu, sim.acq, sim.ch) | |
summary(sim.rev) | |
hist(sim.rev) | |
plot(density(sim.rev)) | |
#Note the distribution is slightly skewed | |
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