HW

By following the 5 factor model and 10 industry monthly returns based on Fama-French database website.

(https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html),

you can construct the MVP portfolio and its monthly returns using the previous 5-year monthly returns data. Show its cumulative returns starting from 1969.

library(tidyverse)

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library(tidyr)
library(tibble)
library(tseries)

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library(PerformanceAnalytics)

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library(ggplot2)
library(readr)

ff_factors <- read_csv("F-F_Research_Data_5_Factors_2x3.csv", skip = 3, col_names = FALSE, col_types = cols(
  X1 = col_character(),
  X2 = col_double(),
  X3 = col_double(),
  X4 = col_double(),
  X5 = col_double(),
  X6 = col_double(),
  X7 = col_double()
))

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
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##   dat <- vroom(...)
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colnames(ff_factors) <- c("Date", "Mkt_RF", "SMB", "HML", "RMW", "CMA", "RF")
ff_factors <- ff_factors %>%
  filter(as.Date(paste0(Date, "01"), format = "%Y%m%d") >= as.Date("1969-01-01"))
ff_factors$Date <- as.Date(paste0(ff_factors$Date, "01"), format = "%Y%m%d")
industry_returns <- read_csv("10_Industry_Portfolios.CSV", skip = 11, col_names = FALSE, col_types = cols(
  X1 = col_character(),
  X2 = col_double(),
  X3 = col_double(),
  X4 = col_double(),
  X5 = col_double(),
  X6 = col_double(),
  X7 = col_double(),
  X8 = col_double(),
  X9 = col_double(),
  X10 = col_double(),
  X11 = col_double()
))

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

colnames(industry_returns) <- c("Date", "Ind1", "Ind2", "Ind3", "Ind4", "Ind5", "Ind6", "Ind7", "Ind8", "Ind9", "Ind10")

# Filter data from 1969
industry_returns <- industry_returns %>%
  filter(as.Date(paste0(Date, "01"), format = "%Y%m%d") >= as.Date("1969-01-01"))

# Convert dates to Date type
industry_returns$Date <- as.Date(paste0(industry_returns$Date, "01"), format = "%Y%m%d")

# Calculate cumulative returns for Fama-French 5 factors
ff_factors_cumulative <- ff_factors %>%
  mutate(
    Mkt_RF_Cumulative = cumprod(1 + Mkt_RF/100),
    SMB_Cumulative = cumprod(1 + SMB/100),
    HML_Cumulative = cumprod(1 + HML/100),
    RMW_Cumulative = cumprod(1 + RMW/100),
    CMA_Cumulative = cumprod(1 + CMA/100)
  )

# Plot cumulative returns for Fama-French 5 factors
ff_factors_long <- ff_factors_cumulative %>%
  select(Date, Mkt_RF_Cumulative, SMB_Cumulative, HML_Cumulative, RMW_Cumulative, CMA_Cumulative) %>%
  pivot_longer(cols = -Date, names_to = "Factor", values_to = "Cumulative_Return")

ggplot(ff_factors_long, aes(x = Date, y = Cumulative_Return, color = Factor)) +
  geom_line() +
  labs(title = "Cumulative Returns of Fama-French 5 Factors",
       x = "Date",
       y = "Cumulative Return") +
  theme_minimal()