3FactorModel_R

rm(list = ls())

library(quantmod)
setwd('Set your Working Directory')

#Download stock data (from the quantmod package)
GOOG <- getSymbols("GOOG", src="yahoo", periodicity = "monthly", from="2011-12-31", to = "2016-12-31", auto.assign = FALSE, adjust = TRUE)
colnames(GOOG) <- c("Open", "High", "Low", "Close", "Volume", "Adj")

#Calculate monthly return
GOOG$Returns <- diff(log(GOOG$Adj))


#Drop first N rows so dates align
N <- 2
GOOG <- GOOG[-(1:N), , drop = FALSE]


#Read in Fama and French data
French <- read.csv(url("http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_CSV.zip"))

temp <- tempfile()
download.file("http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_CSV.zip",temp, mode="wb")
unzip(temp, "F-F_Research_Data_Factors.CSV")

French <- readLines("F-F_Research_Data_Factors.CSV")
ix <- which(French == "")
French <- read.csv(text = French[ix[1]:ix[2]])
French <- subset(French, X >= 201202 & X <= 201612) #Here we select only the months we want


French <- apply(French, 2, rev) #flipping the df 180 degrees
GOOG <- apply(GOOG, 2, rev)


#Necessary calculations, restructuring and renaming variables before running OLS
#df <- as.data.frame(French[, "Mkt.RF"])
df <- subset(French, select = c("Mkt.RF", "SMB", "HML", "RF"))
colnames(df) <- c("Mkt.RF2", "SMB2", "HML2", "RF2")
#colnames(df) <- c("Mkt.RF2")
#df <- as.data.frame(df$Mkt.RF2 / 100)
df <- as.data.frame(df / 100)
#colnames(df) <- "Mkt.RF2"

French <- cbind(French, df)
#French <- subset(French, select = - Mkt.RF)
French <- subset(French, select = c("X", "Mkt.RF2", "SMB2", "HML2", "RF2"))
#French <- French[, c(1, 5, 2, 3, 4)]
colnames(French)
colnames(French) <- c("X", "Mkt.RF", "SMB", "HML", "RF")

df <- cbind(GOOG, French)
df$X <- NULL

#We now have all our variables in one df and of correct proportions

#Excess Monthly Return (Monthly return - Rf)
df$ExcessReturns <- df$Returns - df$RF

#Regression
model <- lm(ExcessReturns ~ Mkt.RF + SMB + HML, data = df)
summary(model)


#Market risk coefficient (beta) from OLS
Mkt.RF_coeff <- coef(summary(model))["Mkt.RF", "Estimate"]
print(Mkt.RF_coeff)
SMB_coeff <- coef(summary(model))["SMB", "Estimate"]
print(SMB_coeff)
HML_coeff <- coef(summary(model))["HML", "Estimate"]
print(HML_coeff)


#Calculating averages
Mkt.RF_mean <- mean(df[,"Mkt.RF"])
print(Mkt.RF_mean)
SMB_mean <- mean(df[,"SMB"])
print(SMB_mean)
HML_mean <- mean(df[,"HML"])
print(HML_mean)
RF_mean <- mean(df[,"RF"])
print(RF_mean)

#Cost of equity
monthlyCoC <- RF_mean + (Mkt.RF_coeff * Mkt.RF_mean) + (SMB_coeff * SMB_mean) + (HML_coeff * HML_mean)
yearlyCoC <- monthlyCoC * 12
in_percent <- yearlyCoC* 100
print(in_percent)