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summary(cars)# Impor Package

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

library(quantmod)

## Loading required package: xts

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## Loading required package: TTR

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

library(PerformanceAnalytics)

## 
## Attaching package: 'PerformanceAnalytics'

## The following object is masked from 'package:graphics':
## 
##     legend

library(dplyr)

## 
## ######################### Warning from 'xts' package ##########################
## #                                                                             #
## # The dplyr lag() function breaks how base R's lag() function is supposed to  #
## # work, which breaks lag(my_xts). Calls to lag(my_xts) that you type or       #
## # source() into this session won't work correctly.                            #
## #                                                                             #
## # Use stats::lag() to make sure you're not using dplyr::lag(), or you can add #
## # conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop           #
## # dplyr from breaking base R's lag() function.                                #
## #                                                                             #
## # Code in packages is not affected. It's protected by R's namespace mechanism #
## # Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning.  #
## #                                                                             #
## ###############################################################################

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:xts':
## 
##     first, last

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
library(ggrepel)
library(patchwork)
library(scales)
library(tidyr)

# Impor Data
start_date <- "2020-01-01"
end_date   <- "2024-12-31"
tickers <- c("ANTM.JK", "PTBA.JK", "ADRO.JK", "MDKA.JK", "^JKSE")
getSymbols(tickers, src = "yahoo", from = start_date, to = end_date, auto.assign = TRUE)

## [1] "ANTM.JK" "PTBA.JK" "ADRO.JK" "MDKA.JK" "JKSE"

antm <- Ad(ANTM.JK)
ptba <- Ad(PTBA.JK)
adro <- Ad(ADRO.JK)
mdka <- Ad(MDKA.JK)
jkse <- Ad(JKSE)

# Daily Return
antm_ret <- dailyReturn(antm, type = "log")
ptba_ret <- dailyReturn(ptba, type = "log")
adro_ret <- dailyReturn(adro, type = "log")
mdka_ret <- dailyReturn(mdka, type = "log")
mkt_ret  <- dailyReturn(jkse, type = "log")
returns <- merge(antm_ret, ptba_ret, adro_ret, mdka_ret, mkt_ret)
colnames(returns) <- c("ANTM", "PTBA", "ADRO", "MDKA", "Market")
returns <- na.omit(returns)

# Risk-Free Rate (BI Rate 5%)
Rf_annual  <- 0.05
Rf_daily   <- Rf_annual / 252

# Excess Return
ex_antm <- returns$ANTM   - Rf_daily
ex_ptba <- returns$PTBA   - Rf_daily
ex_adro <- returns$ADRO   - Rf_daily
ex_mdka <- returns$MDKA   - Rf_daily
ex_mkt  <- returns$Market - Rf_daily

# Regresi OLS - Beta & Alpha
reg_antm <- lm(ex_antm ~ ex_mkt)
reg_ptba <- lm(ex_ptba ~ ex_mkt)
reg_adro <- lm(ex_adro ~ ex_mkt)
reg_mdka <- lm(ex_mdka ~ ex_mkt)

# Matriks
Rm_annual <- mean(returns$Market) * 252
MRP       <- Rm_annual - Rf_annual

hitung_capm <- function(reg, ret_series, nama) {
  alpha_daily  <- coef(reg)[1]
  beta         <- coef(reg)[2]
  alpha_annual <- alpha_daily * 252 * 100          # Jensen's Alpha % Ann.
  r2           <- summary(reg)$r.squared * 100     # R² %
  p_beta       <- summary(reg)$coefficients[2, 4]  # p-value beta
  # E(R) CAPM tahunan %
  e_return <- (Rf_annual + beta * MRP) * 100
  # Actual Return tahunan %
  actual_r <- mean(ret_series) * 252 * 100
  # Klasifikasi Beta
  klasif <- ifelse(beta > 1, "Agresif (β>1)",
                   ifelse(beta == 1, "Netral (β=1)", "Defensif (β<1)"))
  # Status
  status <- ifelse(actual_r > e_return, "Undervalued", "Overvalued")
  # Treynor Ratio (harian → tahunan)
  treynor <- ((mean(ret_series) - Rf_daily) * 252) / beta
  data.frame(
    Saham          = nama,
    Beta           = round(beta, 4),
    Klasifikasi    = klasif,
    Jensen_Alpha   = round(alpha_annual, 2),
    E_R_CAPM       = round(e_return, 2),
    Actual_R       = round(actual_r, 2),
    R2             = round(r2, 2),
    Treynor_Ratio  = round(treynor, 4),
    P_Value_Beta   = round(p_beta, 4),
    Status         = status
  )
}

hasil <- rbind(
  hitung_capm(reg_antm, returns$ANTM, "ANTM.JK"),
  hitung_capm(reg_ptba, returns$PTBA, "PTBA.JK"),
  hitung_capm(reg_adro, returns$ADRO, "ADRO.JK"),
  hitung_capm(reg_mdka, returns$MDKA, "MDKA.JK")
)

# Output Tabel
cat("\n")

cat("   HASIL CAPM - Capital Asset Pricing Model\n")

##    HASIL CAPM - Capital Asset Pricing Model

cat("   Saham: ANTM, PTBA, ADRO, MDKA | Periode: 2020-2024 | Daily\n")

##    Saham: ANTM, PTBA, ADRO, MDKA | Periode: 2020-2024 | Daily

cat("   Rf = 5%/tahun | Market = IHSG (^JKSE)\n")

##    Rf = 5%/tahun | Market = IHSG (^JKSE)

print(hasil, row.names = FALSE)

##    Saham   Beta   Klasifikasi Jensen_Alpha E_R_CAPM Actual_R    R2
##  ANTM.JK 1.5174 Agresif (β>1)        14.32     1.18    15.50 25.75
##  PTBA.JK 1.2496 Agresif (β>1)        16.37     1.85    18.23 25.19
##  ADRO.JK 1.3690 Agresif (β>1)        31.17     1.55    32.72 22.47
##  MDKA.JK 1.0419 Agresif (β>1)         6.32     2.38     8.70 12.60
##  Treynor_Ratio P_Value_Beta      Status
##         0.0692            0 Undervalued
##         0.1058            0 Undervalued
##         0.2025            0 Undervalued
##         0.0355            0 Undervalued

cat("\n")

cat("   RINGKASAN MATRIKS UTAMA\n")

##    RINGKASAN MATRIKS UTAMA

cat(sprintf("\n  Beta Tertinggi  : %.4f → %s\n",
            max(hasil$Beta), hasil$Saham[which.max(hasil$Beta)]))

## 
##   Beta Tertinggi  : 1.5174 → ANTM.JK

cat(sprintf("  Beta Terendah   : %.4f → %s\n",
            min(hasil$Beta), hasil$Saham[which.min(hasil$Beta)]))

##   Beta Terendah   : 1.0419 → MDKA.JK

cat(sprintf("  Jensen Alpha Terbaik : %.2f%% → %s\n",
            max(hasil$Jensen_Alpha), hasil$Saham[which.max(hasil$Jensen_Alpha)]))

##   Jensen Alpha Terbaik : 31.17% → ADRO.JK

cat(sprintf("  R² Terbaik      : %.2f%% → %s\n",
            max(hasil$R2), hasil$Saham[which.max(hasil$R2)]))

##   R² Terbaik      : 25.75% → ANTM.JK

cat("\n")

cat("   KEPUTUSAN INVESTASI\n")

##    KEPUTUSAN INVESTASI

for (i in 1:nrow(hasil)) {
  cat(sprintf("\n  %s:\n", hasil$Saham[i]))
  cat(sprintf("    E(R) CAPM    : %.2f%%\n", hasil$E_R_CAPM[i]))
  cat(sprintf("    Return Aktual: %.2f%%\n", hasil$Actual_R[i]))
  cat(sprintf("    Status       : %s\n",     hasil$Status[i]))
  cat(sprintf("    Beta         : %.4f (%s)\n", hasil$Beta[i], hasil$Klasifikasi[i]))
  cat(sprintf("    Jensen Alpha : %.2f%% per tahun\n", hasil$Jensen_Alpha[i]))
  cat(sprintf("    Treynor Ratio: %.4f\n", hasil$Treynor_Ratio[i]))
}

## 
##   ANTM.JK:
##     E(R) CAPM    : 1.18%
##     Return Aktual: 15.50%
##     Status       : Undervalued
##     Beta         : 1.5174 (Agresif (β>1))
##     Jensen Alpha : 14.32% per tahun
##     Treynor Ratio: 0.0692
## 
##   PTBA.JK:
##     E(R) CAPM    : 1.85%
##     Return Aktual: 18.23%
##     Status       : Undervalued
##     Beta         : 1.2496 (Agresif (β>1))
##     Jensen Alpha : 16.37% per tahun
##     Treynor Ratio: 0.1058
## 
##   ADRO.JK:
##     E(R) CAPM    : 1.55%
##     Return Aktual: 32.72%
##     Status       : Undervalued
##     Beta         : 1.3690 (Agresif (β>1))
##     Jensen Alpha : 31.17% per tahun
##     Treynor Ratio: 0.2025
## 
##   MDKA.JK:
##     E(R) CAPM    : 2.38%
##     Return Aktual: 8.70%
##     Status       : Undervalued
##     Beta         : 1.0419 (Agresif (β>1))
##     Jensen Alpha : 6.32% per tahun
##     Treynor Ratio: 0.0355

cat("   KETERANGAN STATUS\n")

##    KETERANGAN STATUS

cat("  Undervalued - Actual Return > E(R) CAPM - LAYAK DIBELI\n")

##   Undervalued - Actual Return > E(R) CAPM - LAYAK DIBELI

cat("  Overvalued  - Actual Return < E(R) CAPM - TIDAK DISARANKAN\n")

##   Overvalued  - Actual Return < E(R) CAPM - TIDAK DISARANKAN

# ── VISUALISASI ──────────────────────────────────────────────────────────────

warna_saham <- c("ANTM" = "#E63946", "PTBA" = "#2196F3",
                 "ADRO" = "#4CAF50", "MDKA" = "#FF9800")

# Rebuild hasil dengan nama pendek untuk grafik
hasil_plot <- rbind(
  hitung_capm(reg_antm, returns$ANTM, "ANTM"),
  hitung_capm(reg_ptba, returns$PTBA, "PTBA"),
  hitung_capm(reg_adro, returns$ADRO, "ADRO"),
  hitung_capm(reg_mdka, returns$MDKA, "MDKA")
)

# GRAFIK 1 — BETA vs E(R) CAPM (Scatter)
p1 <- ggplot(hasil_plot, aes(x = Beta, y = E_R_CAPM, color = Saham, label = Saham)) +
  geom_point(size = 6, alpha = 0.9) +
  geom_text_repel(size = 4.5, fontface = "bold", nudge_y = 0.3,
                  box.padding = 0.4, show.legend = FALSE) +
  scale_color_manual(values = warna_saham) +
  labs(
    title    = "Beta vs E(R) CAPM",
    subtitle = "Semakin tinggi beta, semakin tinggi ekspektasi return",
    x        = "Beta (β)",
    y        = "E(R) CAPM (%/tahun)",
    color    = "Saham"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title      = element_text(face = "bold", size = 14),
    plot.subtitle   = element_text(color = "grey50", size = 10),
    legend.position = "bottom"
  )

# GRAFIK 2 — JENSEN'S ALPHA PER SAHAM (Bar Chart)
p2 <- ggplot(hasil_plot, aes(x = reorder(Saham, Jensen_Alpha),
                              y = Jensen_Alpha, fill = Saham)) +
  geom_col(width = 0.55, alpha = 0.9) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "grey40", linewidth = 0.8) +
  geom_text(aes(label = paste0(Jensen_Alpha, "%"),
                vjust = ifelse(Jensen_Alpha >= 0, -0.5, 1.3)),
            fontface = "bold", size = 4.5) +
  scale_fill_manual(values = warna_saham) +
  labs(
    title    = "Jensen's Alpha per Saham",
    subtitle = "Alpha > 0 - manajer menghasilkan return di atas CAPM",
    x        = NULL,
    y        = "Jensen's Alpha (%/tahun)",
    fill     = "Saham"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title      = element_text(face = "bold", size = 14),
    plot.subtitle   = element_text(color = "grey50", size = 10),
    legend.position = "none"
  )

# GRAFIK 3 — SECURITY MARKET LINE (SML)
beta_range <- seq(0, max(hasil_plot$Beta) * 1.3, length.out = 200)
sml_df     <- data.frame(
  Beta    = beta_range,
  E_R_SML = (Rf_annual + beta_range * MRP) * 100
)

p3 <- ggplot() +
  geom_line(data = sml_df, aes(x = Beta, y = E_R_SML),
            color = "#333333", linewidth = 1.2, linetype = "solid") +
  geom_point(aes(x = 0, y = Rf_annual * 100),
             color = "black", size = 3, shape = 18) +
  annotate("text", x = 0.03, y = Rf_annual * 100 + 0.3,
           label = paste0("Rf = ", Rf_annual * 100, "%"),
           size = 3.5, hjust = 0, color = "grey30") +
  geom_point(aes(x = 1, y = Rm_annual * 100),
             color = "black", size = 3, shape = 18) +
  annotate("text", x = 1.03, y = Rm_annual * 100 + 0.3,
           label = paste0("Rm = ", round(Rm_annual * 100, 2), "%"),
           size = 3.5, hjust = 0, color = "grey30") +
  geom_point(data = hasil_plot, aes(x = Beta, y = Actual_R, color = Saham),
             size = 7, alpha = 0.9) +
  geom_text_repel(
    data = hasil_plot,
    aes(x = Beta, y = Actual_R,
        label = paste0(Saham, "\n(", Status, ")"),
        color = Saham),
    size = 3.8, fontface = "bold", box.padding = 0.5,
    nudge_x = 0.05, show.legend = FALSE
  ) +
  geom_segment(data = hasil_plot,
               aes(x = Beta, xend = Beta,
                   y = E_R_CAPM, yend = Actual_R,
                   color = Saham),
               linetype = "dotted", linewidth = 0.9, show.legend = FALSE) +
  scale_color_manual(values = warna_saham) +
  labs(
    title    = "Security Market Line (SML) — CAPM",
    subtitle = "Di atas SML = Undervalued | Di bawah SML = Overvalued",
    x        = "Beta (β)",
    y        = "Return (%/tahun)",
    color    = "Saham"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title      = element_text(face = "bold", size = 14),
    plot.subtitle   = element_text(color = "grey50", size = 10),
    legend.position = "bottom"
  )

# GRAFIK 4 — ACTUAL vs E(R) CAPM (Grouped Bar)
hasil_long <- hasil_plot %>%
  select(Saham, Actual_R, E_R_CAPM) %>%
  pivot_longer(cols = c(Actual_R, E_R_CAPM),
               names_to  = "Tipe",
               values_to = "Return") %>%
  mutate(Tipe = recode(Tipe,
                       "Actual_R" = "Return Aktual",
                       "E_R_CAPM" = "E(R) CAPM"))

p4 <- ggplot(hasil_long, aes(x = Saham, y = Return, fill = Tipe)) +
  geom_col(position = position_dodge(width = 0.6), width = 0.5, alpha = 0.9) +
  geom_text(aes(label = paste0(round(Return, 1), "%")),
            position = position_dodge(width = 0.6),
            vjust = -0.4, size = 3.8, fontface = "bold") +
  scale_fill_manual(values = c("Return Aktual" = "#1565C0", "E(R) CAPM" = "#EF6C00")) +
  labs(
    title    = "Return Aktual vs E(R) CAPM per Saham",
    subtitle = "Return Aktual > E(R) CAPM - Undervalued (layak beli)",
    x        = NULL,
    y        = "Return (%/tahun)",
    fill     = NULL
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title      = element_text(face = "bold", size = 14),
    plot.subtitle   = element_text(color = "grey50", size = 10),
    legend.position = "bottom"
  )

p1

p2

p3

p4

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