The implied ERP is very sensitive to assumptions, in particular G2

The following implements the implied ERP approach in Professor Damodaran’s post on the The Price of Risk. My intention is to briefly explore its sensitivity to assumptions.

```
library(tidyverse)
library(scales)
solve_for_R <- function(RF, ER_vector, CP_vector, G2, PV) {
# Calculate cash flow vector
CF_vector <- ER_vector * CP_vector
# Define the objective function
objective_function <- function(R) {
# This is effectively two-stage dividend discount model except the initial stage is explicated
# such that there exists no G1 and G2 refers to the subsequent period of growth
PV_calculated <- sum(CF_vector[1:5] / (1 + R)^(1:5)) + CF_vector[6] / ((R - G2) * (1 + R)^5)
return((PV - PV_calculated)^2)
}
# Use the optim function to minimize the objective function
result <- optim(par = RF, fn = objective_function, method = "Brent", lower = -1, upper = 2)
return(result$par)
}
RF <- 0.04 # I have rounded his riskfree rate of 3.97% to 4.00%
ER_vector <- c(217.8, 245.2, 273.7, 295.1, 308.9, 324.9) # A. Damodaran's earnings vector
CP_vector <- c(0.84, 0.82, 0.80, 0.78, 0.77, 0.77) # Cash payout ratios
G2 <- 0.04 # His model sets the stable growth equal to the RF rate
PV <- 4600 # I rounded 4588.96 to 4,600
implied_equity <- solve_for_R(RF, ER_vector, CP_vector, G2, PV)
implied_ERP <- implied_equity - RF
# Number of simulations
n_simulations <- 10000
coeff_variation <- 0.10 # Arbitrarily suggesting that COV of 10% is tight
# Assumed means and standard deviations for inputs
mean_RF <- RF; sd_RF <- RF * coeff_variation
mean_ER <- ER_vector; sd_ER <- ER_vector * coeff_variation
mean_CP <- CP_vector; sd_CP <- CP_vector * coeff_variation
mean_G2 <- G2; sd_G2 <- G2 * coeff_variation
mean_PV <- PV; sd_PV <- PV * coeff_variation
# MC simulation
set.seed(379)
R_values <- replicate(n_simulations,
solve_for_R(
# RF = rnorm(1, mean_RF, sd_RF),
RF = RF,
ER_vector = rnorm(6, mean_ER, sd_ER),
CP_vector = rnorm(6, mean_CP, sd_CP),
G2 = rnorm(1, mean_G2, sd_G2),
PV = PV
)
)
# Histogram to visualize the distribution of R values
R_values <- R_values[R_values > 0]
ERP_values <- R_values - RF
ERP_values_mean <- mean(ERP_values)
ERP_values_df <- as_data_frame(ERP_values)
ERP_values_df %>% ggplot(aes(value)) +
geom_histogram(color = "darkblue", fill = "lightblue") +
geom_vline(aes(xintercept = ERP_values_mean), color = "darkgreen", size = 1.5) +
scale_x_continuous(labels = percent_format(0.01)) +
labs(title = "Implied equity risk premium, ERP (n = 10,000 sims)",
subtitle = "Under tight assumption dispersion (CV = σ/μ =10%). Green vertical line is the mean.",
y = "Count") +
# xlab("X label") +
# ylab("Count") +
theme_classic() +
theme(axis.title = element_blank(),
axis.text = element_text(size = 12, face = "bold"))
```

Quick check on the distribution:

`[1] 0.08903814`

`kurtosis(ERP_values_df$value)`

`[1] 2.997992`

```
quantiles_v <- c(0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.975, 0.99)
quantile(ERP_values_df$value, probs = quantiles_v)
```

```
1% 2.5% 5% 10% 25% 50%
0.03041180 0.03248300 0.03433620 0.03649075 0.04004356 0.04410500
75% 90% 95% 97.5% 99%
0.04832614 0.05209407 0.05447292 0.05638444 0.05881702
```

What is the relationship between the sustainable growth rate, G2, and the ERP?

```
G2_values <- seq(from = 0.02, to = 0.06, by = 0.001)
R_values <- map_dbl(G2_values, function(G2) {
solve_for_R(
RF = RF,
ER_vector = ER_vector,
CP_vector = CP_vector,
G2 = G2,
PV = PV
)
})
ERP_values <- R_values - RF
G_vs_ERP <- tibble(
G2 = G2_values,
ERP = ERP_values
)
G_vs_ERP %>% ggplot(aes(x = G2, y = ERP)) +
geom_point() +
coord_cartesian(ylim = c(.02, .08)) +
labs(title = "Implied ERP as function of sustainable growth rate, G2",
subtitle = "Unlike prior/next visualization, predicted vectors are not randomized")
```

And just for fun, let’s add randomness to the earnings and cash payout vectors:

```
G2_values <- seq(from = 0.02, to = 0.06, by = 0.001)
R_values <- map(G2_values, function(G2) {
replicate(30, {
solve_for_R(
RF = RF,
ER_vector = rnorm(6, mean_ER, sd_ER),
CP_vector = rnorm(6, mean_CP, sd_CP),
G2 = G2,
PV = PV
) - RF # subtracting RF here inside replicat
})
})
df <- tibble(
G2 = G2_values,
ERP = R_values
) %>% unnest()
model_line <- lm(ERP ~ G2, data = df)
rsq <- summary(model_line)$r.squared
label_R2 <- sprintf("R^2 = %.2f", rsq)
df %>% ggplot(aes(x = G2, y = ERP)) +
geom_point() +
coord_cartesian(ylim = c(.02, .08)) +
geom_smooth(method = "lm", se = TRUE, color = "blue") +
labs(title = "Restores 10% CV randomness to earnings and payout vectors") +
annotate("text", x=0.025, y=0.065, label=label_R2, fontface="bold", hjust=0)
```

` # geom_text(aes(label = label_R2))`