[SOLVED] CS finance # Assignment 1 Code Empirical Finance

# Assignment 1 Code Empirical Finance
# Author: Chris Hansman
# Email; [email protected]
# Date: 09/02/21
# install.packages(tidyverse)
# install.packages(glmnet)
# install.packages(glmnetUtils)

library(tidyverse)
library(readxl)
library(lubridate)
library(glmnet)
library(glmnetUtils)
library(stargazer)

#Problem 4
# Cleaning Data
msales<-read_xls(“rollingsales_manhattan.xls”, skip=4)names(msales) <- gsub(” “,”_”, names(msales))msales <- msales %>%
filter(SALE_PRICE!=0) %>%
mutate(log_price=log(SALE_PRICE), age=2020-YEAR_BUILT)

# Part a
neighborhood_price <- msales %>%
group_by(NEIGHBORHOOD) %>%
summarise(n_price=mean(SALE_PRICE)) %>%
arrange(n_price)

# Part b
msales %>%
lm(log_price ~ age + NEIGHBORHOOD, data=.) %>%
stargazer(type = text, digits = 4)

# Part c
msales %>%
filter(NEIGHBORHOOD==UPPER EAST SIDE (59-79)) %>%
lm(log_price ~ age, data=. ) %>%
stargazer(type = text, digits = 4)

# Part d
msales_summary <- msales %>%
mutate(month=month(SALE_DATE))%>%
mutate(SALE_PRICE=SALE_PRICE/100000) %>%
group_by(month) %>%
summarise(mean_price=mean(SALE_PRICE), median_price=median(SALE_PRICE), n = n())

ggplot(data=msales_summary) +
geom_line(aes(x=month, y=mean_price , color=Mean)) +
geom_line(aes(x=month, y=median_price, color=Median)) +
xlab(Month) + ylab(Price (100k USD)) +
ylim(0, 100) +
scale_x_continuous(breaks=c( 3, 6, 9, 12)) +
scale_colour_manual(,
breaks = c(Mean, Median),
values = c(red,blue)) +
theme_classic() +
theme(legend.position=top) +
ggsave(prices.pdf, width=6, height=4)

ggplot(data=msales_summary) +
geom_line(aes(x=month, y=n)) +
xlab(Month) + ylab(Quantity of Sales) +
ylim(0, 1200) +
scale_x_continuous(breaks=c( 3, 6, 9, 12)) +
theme_classic() +
ggsave(quantities.pdf, width=6, height=4)

# Problem 5
# Loading Data
patriots <- read_csv(“patriots.csv”)# Difference-in-Differencepatriots <- patriots %>%
mutate(d=dmy(date)) %>%
mutate(T=d>dmy(01022015)) %>%
mutate(D=exchange==nyse) %>%
mutate(DT=D*T)

patriots %>%
lm(price~D+T+DT, data=.) %>%
stargazer(type = text, digits = 4)

# Plotting
daily_mean <- patriots %>%
group_by(d, exchange) %>%
summarise(mean_p=mean(price))

ggplot(aes(y=mean_p, x=d,group=exchange, color=exchange), data=daily_mean) +
geom_line()+
theme_classic() +
xlab(Date) +
ylab(Price)+
ylim(0,200) +
ggsave(nysesse.pdf, width=6, height=4)

# Problem 6
#set.seed(1234)
train <- read_csv(“regularization_train.csv”)test<- read_csv(“regularization_test.csv”)cv_glmnet_10 <- cv.glmnet(y~.,data=train)cv_glmnet_20 <- cv.glmnet(y~.,data=train, nfolds=20)cv_glmnet_alpha <- cv.glmnet(y~.,data=train, alpha=0.1)#Part acv_glmnet_10$lambda.mincv_glmnet_10$lambda.1secoef(cv_glmnet_10, s=”lambda.1se”)y_pred_10 <- predict(cv_glmnet_10, s=”lambda.1se”, newdata=test)mse_10 <- mean((test$y-y_pred_10)^2)mse_10#Part bcv_glmnet_20$lambda.mincv_glmnet_20$lambda.1secoef(cv_glmnet_20, s=”lambda.1se”)y_pred_20 <- predict(cv_glmnet_20, s=”lambda.1se”, newdata=test)mse_20 <- mean((test$y-y_pred_20)^2)mse_20#Part ccv_glmnet_alpha$lambda.mincv_glmnet_alpha$lambda.1secoef(cv_glmnet_alpha, s=”lambda.1se”)y_pred_alpha <- predict(cv_glmnet_alpha, s=”lambda.1se”, newdata=test)mse_alpha <- mean((test$y-y_pred_alpha)^2)mse_alpha