##################################
#  Buffett: Berkshire Hathaway
##################################


library(fImport)

Y = yahooSeries("BRK-A", from = "1990-01-01")

# take a look of the data
head(Y)

plot(Y[,6],type="l",col=20,main="BRKA Share Price",
     ylab="Price",xlab="Time")

# Calculate Returns

y = rev(Y$`BRK-A.Adj.Close`)
n <- length(y)
rets <- y[-1]/y[-n]-1

summary(rets)

# Two plots in one
# par(mfrow=c(1,2))

plot(rets,pch=20,col=24)
abline(0,0)

# summarise returns

mean(rets)
sd(rets)
skewness(rets)
hist(rets,breaks=50,freq=FALSE,main="Berkshire Returns",col="red")

lines(seq(-1,1,0.001),dnorm(seq(-1,1,0.001),mean(rets),sd(rets)),lwd=2.5,col="green")

legend("topright",c("Actual Distribution","Normal Distribution"),
       lty=c(1,1),lwd=c(2.5,2.5),col=c("red","green"))


# to save the plot,click "Export" ans "save as image"
# ts.plot(y,main="BRK-A price time series",ylab="price",xlab="day")




##################################
#  Buffett Regressiuon
##################################

buffett = read.table("http://faculty.chicagobooth.edu/nicholas.polson/teaching/41000/buffett.txt",header=T)
attach(buffett)


# Plot the data

plot(Market, Buffett, xlab="Market Return", ylab="Buffett Return",pch=20)
legend(x="topleft",legend=c("Buffett","Market"),pch=15,col=c(2,4),bty="n")

# correlation matrix
cor(cbind(Buffett,Market))

# Fit the least-squares line and superimpose this line on the plot

model = lm(Buffett~Market)
abline(model)
title("Buffett vs Market")


# Extract the model coefficients 

coef(model)
summary(model)

# Improvement in Fit

sd(model$residuals)
sd(Buffett)

# To remove datapoint 10
# buffett_10 = buffett[-10,]