Calculate and interpret the linear relationship between companies using the correlation coefficient. Here, you should calculate the correlation between pairs of companies. use the following companies: verizon vs AT&T, verizon vs T-mobile, etc. I need to include the section of your R code that allows me to perform the computation.

code example

#####1. Install the Packages if necessary#### if(!require("tidyverse")) install.packages("tidyverse") if(!require("tidyquant")) install.packages("tidyquant") if(!require("lubridate")) install.packages("lubridate") if(!require("PerformanceAnalytics")) install.packages("PerformanceAnalytics") if(!require("openxlsx")) install.packages("openxlsx")

#####2. Load the Packages#### library(tidyverse) library(tidyquant) library(lubridate) library(PerformanceAnalytics) library(openxlsx)

#####3. Data Wrangling####

######3.1.Asset Prices (Stocks) ######## Daily_Price <- c("NFLX","AMZN","AAPL") %>% tq_get(get = "stock.prices", from = "2018-01-01", to = "2023-06-30")

######3.2.Asset Returns (Stocks) ######## Daily_Ret <- c("NFLX","AMZN","AAPL") %>% tq_get(get = "stock.prices", from = "2018-01-01", to = "2023-06-30") %>% group_by(symbol) %>% tq_transmute(select = adjusted, mutate_fun = periodReturn, period = "daily", col_rename = "dRet")

Monthly_Ret <- c("NFLX","AMZN","AAPL") %>% tq_get(get = "stock.prices", from = "2018-01-01", to = "2023-06-30") %>% group_by(symbol) %>% tq_transmute(select = adjusted, mutate_fun = periodReturn, period = "monthly", col_rename = "mRet")

######3.3.Baseline Returns (Portfolio) ######## Monthly_Ret_Port <- "XLK" %>% tq_get(get = "stock.prices", from = "2018-01-01", to = "2023-06-30") %>% tq_transmute(select = adjusted, mutate_fun = periodReturn, period = "monthly", col_rename = "Rb")

######3.4.Join (Merge) ########

Monthly_Ret_Join <- left_join(Monthly_Ret,Monthly_Ret_Port,by="date")

######3.5.Reshape ########

Daily_Ret_Wide <- pivot_wider(Daily_Ret,names_from = symbol, values_from = dRet)%>% mutate(Period = if_else(date<=ymd("2019-12-31"),"Pre-Covid","Post-Covid"))

Monthly_Ret_Wide <- pivot_wider(Monthly_Ret,names_from = symbol, values_from = mRet) %>% left_join(.,Monthly_Ret_Port,by="date")

Monthly_Ret_Wide_TS <- read.zoo(Monthly_Ret_Wide, format = "%Y-%m-%d")

#####4. Analysis####

######4.1.Tables####

Table_summary <- table.Stats(Monthly_Ret_Wide_TS) %>% tibble::rownames_to_column(.,"Statistic")

Table_var <- table.Variability(Monthly_Ret_Wide_TS) %>% tibble::rownames_to_column(.,"Statistic")

Table_semi <- SemiSD(Monthly_Ret_Wide_TS, method = "full") %>% as_tibble(.) %>% mutate(Statistic="Semi-Deviation") %>% dplyr::select(Statistic,everything())

Table_semi2 <- DownsideDeviation(Monthly_Ret_Wide_TS, method = "full") %>% as_tibble(.) %>% mutate(Statistic="Downside-Deviation (MAR=0%)") %>% dplyr::select(Statistic,everything())

Table_summary2 <- table.AnnualizedReturns(Monthly_Ret_Wide_TS) %>% tibble::rownames_to_column(.,"Statistic")

Table_cor <- Monthly_Ret_Join %>% group_by(symbol) %>% summarise(Num_of_Months=n(), mRet_Mean=mean(mRet, na.rm= TRUE), mRet_SD=sd(mRet, na.rm= TRUE), Rb_SD=sd(Rb, na.rm= TRUE), Cov_mRet_Rb=cov(x=mRet,y=Rb, use="pairwise.complete.obs"), Cor_mRet_Rb=cor(x=mRet,y=Rb, use="pairwise.complete.obs"), Cor_mRet_Rb2=Cov_mRet_Rb/(mRet_SD*Rb_SD))

######4.2.Graphs####

chart.Boxplot(Monthly_Ret_Wide_TS)

chart.RiskReturnScatter(Monthly_Ret_Wide_TS)

chart.SnailTrail(Monthly_Ret_Wide_TS, legend.loc = "topleft")

#####5.Export & Communicate####

######5.1.Tables####

wb <- createWorkbook()

addWorksheet(wb,"Daily_Prices") writeData(wb, sheet = "Daily_Prices", x = Daily_Price)

addWorksheet(wb,"Daily_Returns") writeData(wb, sheet = "Daily_Returns", x = Daily_Ret_Wide)

addWorksheet(wb,"Monthly_Returns") writeData(wb, sheet = "Monthly_Returns", x = Monthly_Ret_Wide)

addWorksheet(wb,"T1_summary") writeData(wb, sheet = "T1_summary", x = Table_summary)

addWorksheet(wb,"T2_variability") writeData(wb, sheet = "T2_variability", x = Table_var)

addWorksheet(wb,"T3_semi") writeData(wb, sheet = "T3_semi", x = Table_semi)

addWorksheet(wb,"T4_semi2") writeData(wb, sheet = "T4_semi2", x = Table_semi2)

addWorksheet(wb,"T5_summary2") writeData(wb, sheet = "T5_summary2", x = Table_summary2)

saveWorkbook(wb, "FING6702_Mod6_R_Tables.xlsx", overwrite = TRUE)

remove(wb)

######5.2.Charts####

jpeg("FING6702_Mod6_R_Graph1.jpeg", quality = 75) chart.Boxplot(Monthly_Ret_Wide_TS) dev.off()

jpeg("FING6702_Mod6_R_Graph2.jpeg", quality = 75) chart.RiskReturnScatter(Monthly_Ret_Wide_TS) dev.off()

jpeg("FING6702_Mod6_R_Graph3.jpeg", quality = 75) chart.SnailTrail(Monthly_Ret_Wide_TS, legend.loc = "topleft") dev.off()