Python for Finance

Monte Carlo Simulation
R_and_D=200000
# Research and development
costRawMaterials=0.3 # percentage cost of raw materials
tax=0.38
# corporate tax rate
thousand=1e3
# unit of thousand
million=1e6
# unit of million
#
# three uncertainties: price, unit and discount rate
nSimulation=100
# number of simulation
lowPrice=10
# low price
highPrice=30
# high price
lowUnit=50*thousand # low units expected to sell
highUnit=200*thousand # high units expected to sell
lowRate=0.15
# lower discount rate
highRate=0.25
# high discount rate
#
n2=nSimulation
sp.random.seed(123)
price0=sp.random.uniform(low=lowPrice,high=highPrice,size=n2)
units0=sp.random.uniform(low=lowUnit,high=highUnit,size=n2)
R0=sp.random.uniform(lowRate,highRate,size=n2)
#
npv=[]
for i in sp.arange(nSimulation):
units=sp.ones(n)*units0[i]
price=price0[i]
R=R0[i]
sales=units*price
sales[0]=0
# sales for 1st year is zero
cost1=costRawMaterials*sales
cost2=sp.ones(n)*otherCost
cost3=sp.ones(n)*sellingCost
cost4=sp.zeros(n)
cost4[0]=costEquipment
RD=sp.zeros(n)
RD[0]=R_and_D
# assume R&D at time zero
D=sp.ones(n)*costEquipment/nYear # straight line depreciation
D[0]=0 # no depreciation at time 0
EBIT=sales-cost1-cost2-cost3-cost4-RD-D
NI=EBIT*(1-tax)
FCF=NI+D
# add back depreciation
npvProject=sp.npv(R,FCF)/million # estimate NPV
npv.append(npvProject)
print("mean NPV of project=",round(sp.mean(npv),0))
print("min NPV of project=",round(min(npv),0))
[ 448 ]