33.5 Economic Significance

The total wealth gain (or loss) resulting from a marketing event is given by:

\[ \Delta W_t = CAR_t \times MKTVAL_0 \]

where:

• \(\Delta W_t\) = Change in firm value (gain or loss).
• \(CAR_t\) = Cumulative abnormal return up to date \(t\).
• \(MKTVAL_0\) = Market value of the firm before the event window.

Interpretation:

• If \(\Delta W_t > 0\): The event increased firm value.
• If \(\Delta W_t < 0\): The event decreased firm value.
• The magnitude of \(\Delta W_t\) reflects the economic impact of the marketing event in dollar terms.

By computing \(\Delta W_t\), researchers can translate stock market reactions into tangible financial implications, helping assess the real-world significance of marketing decisions.

---

# Load necessary libraries
library(tidyverse)

# Simulated dataset of event study results
df_event_study <- tibble(
    firm_id = 1:100,
    # 100 firms
    CAR_t = rnorm(100, mean = 0.02, sd = 0.05),
    # Simulated CAR values
    MKTVAL_0 = runif(100, min = 1e8, max = 5e9)  # Market value in dollars
)

# Compute total wealth gain/loss
df_event_study <- df_event_study %>%
    mutate(wealth_change = CAR_t * MKTVAL_0)

# Summary statistics of economic impact
summary(df_event_study$wealth_change)
#>       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
#> -182624158  -23329465   29601222   74140034  128382915  565137418

# Histogram of total wealth gain/loss
hist(
    df_event_study$wealth_change,
    main = "Distribution of Wealth Change from Event",
    xlab = "Wealth Change ($)",
    col = "blue",
    breaks = 30
)

---