33.4 Event Studies in Marketing

A key challenge in marketing-related event studies is determining the appropriate dependent variable (Skiera, Bayer, and Schöler 2017). Traditional event studies in finance use cumulative abnormal returns (CAR) on shareholder value ( $CAR^{SHV}$ ). However, marketing events primarily affect a firm’s operating business, rather than its total shareholder value, leading to potential distortions if financial leverage is ignored.

According to valuation theory, a firm’s shareholder value ( $SHV$ ) consists of three components (Schulze, Skiera, and Wiesel 2012):

$SHV = \text{Operating Business Value} + \text{Non-Operating Assets} - \text{Debt}$

Many marketing-related events primarily impact operating business value (e.g., brand perception, customer satisfaction, advertising efficiency), while non-operating assets and debt remain largely unaffected.

Ignoring firm-specific leverage effects in event studies can cause:

Inflated impact for firms with high debt.
Deflated impact for firms with large non-operating assets.

Thus, it is recommended that both $CAR^{OB}$ and $CAR^{SHV}$ be reported, with justification for which is most appropriate.

Few event studies have explicitly controlled for financial structure. Exceptions include:

(Gielens et al. 2008): Studied marketing spending shocks while accounting for leverage.
(Chaney, Devinney, and Winer 1991): Examined advertising expenses and firm value, controlling for financial structure.

33.4.1 Definition

Cumulative Abnormal Return on Shareholder Value ( $CAR^{SHV}$ )

$CAR^{SHV} = \frac{\sum \text{Abnormal Returns}}{SHV}$

Shareholder Value ( $SHV$ ): Market capitalization, defined as:

$SHV = \text{Share Price} \times \text{Shares Outstanding}$

Cumulative Abnormal Return on Operating Business ( $CAR^{OB}$ )

To correct for leverage effects, $CAR^{OB}$ is calculated as:

$CAR^{OB} = \frac{CAR^{SHV}}{\text{Leverage Effect}}$

where:

$\text{Leverage Effect} = \frac{\text{Operating Business Value}}{\text{Shareholder Value}}$

Key Relationships:

Operating Business Value = $SHV -$ Non-Operating Assets $+$ Debt.
Leverage Effect ( $LE$ ) measures how a 1% change in operating business value translates into shareholder value movement.

Leverage Effect vs. Leverage Ratio

Leverage Effect ( $LE$ ) is not the same as the leverage ratio, which is typically:

$\text{Leverage Ratio} = \frac{\text{Debt}}{\text{Firm Size}}$

where firm size can be:

Book value of equity
Market capitalization
Total assets
Debt + Equity

33.4.2 When Can Marketing Events Affect Non-Operating Assets or Debt?

While most marketing events impact operating business value, in rare cases they also influence non-operating assets and debt:

Marketing Event	Impact on Financial Structure
Excess Pre-ordering (G. C. Hall, Hutchinson, and Michaelas 2004)	Affects short-term debt
CMO Turnover (Berger, Ofek, and Yermack 1997)	Higher debt due to manager turnover
Unique Product Development (Bhaduri 2002)	Alters debt levels

These exceptions highlight why controlling for financial structure is crucial in event studies.

33.4.3 Calculating the Leverage Effect

We can express leverage effect ( $LE$ ) as:

$\begin{aligned} LE &= \frac{\text{Operating Business Value}}{\text{Shareholder Value}} \\ &= \frac{(\text{SHV} - \text{Non-Operating Assets} + \text{Debt})}{\text{SHV}} \\ &= \frac{prcc_f \times csho - ivst + dd1 + dltt + pstk}{prcc_f \times csho} \end{aligned}$

where:

$prcc_f$ = Share price
$csho$ = Common shares outstanding
$ivst$ = Short-term investments (Non-Operating Assets)
$dd1$ = Long-term debt due in one year
$dltt$ = Long-term debt
$pstk$ = Preferred stock

33.4.4 Computing Leverage Effect from Compustat Data

# Load required libraries
library(tidyverse)


# Load dataset
df_leverage_effect <- read.csv("data/leverage_effect.csv.gz") %>%
    
    # Filter active firms
    filter(costat == "A") %>%
    
    # Drop missing values
    drop_na() %>%
    
    # Compute Shareholder Value (SHV)
    mutate(shv = prcc_f * csho) %>%
    
    # Compute Operating Business Value (OBV)
    mutate(obv = shv - ivst + dd1 + dltt + pstk) %>%
    
    # Compute Leverage Effect
    mutate(leverage_effect = obv / shv) %>%
    
    # Remove infinite values and non-positive leverage effects
    filter(is.finite(leverage_effect), leverage_effect > 0) %>%
    
    # Compute within-firm statistics
    group_by(gvkey) %>%
    mutate(
        within_mean_le = mean(leverage_effect, na.rm = TRUE),
        within_sd_le = sd(leverage_effect, na.rm = TRUE)
    ) %>%
    ungroup()

# Summary statistics
mean_le <- mean(df_leverage_effect$leverage_effect, na.rm = TRUE)
max_le <- max(df_leverage_effect$leverage_effect, na.rm = TRUE)

# Plot histogram of leverage effect
hist(
    df_leverage_effect$leverage_effect,
    main = "Distribution of Leverage Effect",
    xlab = "Leverage Effect",
    col = "blue",
    breaks = 30
)


# Compute coefficient of variation (CV)
cv_le <-
    sd(df_leverage_effect$leverage_effect, na.rm = TRUE) / mean_le * 100

# Plot within-firm coefficient of variation histogram
df_leverage_effect %>%
    group_by(gvkey) %>%
    slice(1) %>%
    ungroup() %>%
    mutate(cv = within_sd_le / within_mean_le) %>%
    pull(cv) %>%
    hist(
        main = "Within-Firm Coefficient of Variation",
        xlab = "CV",
        col = "red",
        breaks = 30
    )

References

Berger, Philip G, Eli Ofek, and David L Yermack. 1997. “Managerial Entrenchment and Capital Structure Decisions.” The Journal of Finance 52 (4): 1411–38.

Bhaduri, Saumitra N. 2002. “Determinants of Corporate Borrowing: Some Evidence from the Indian Corporate Structure.” Journal of Economics and Finance 26 (2): 200–215.

Chaney, Paul K, Timothy M Devinney, and Russell S Winer. 1991. “The Impact of New Product Introductions on the Market Value of Firms.” Journal of Business, 573–610.

Gielens, Katrijn, Linda M Van de Gucht, Jan-Benedict EM Steenkamp, and Marnik G Dekimpe. 2008. “Dancing with a Giant: The Effect of Wal-Mart’s Entry into the United Kingdom on the Performance of European Retailers.” Journal of Marketing Research 45 (5): 519–34.

Hall, Graham C, Patrick J Hutchinson, and Nicos Michaelas. 2004. “Determinants of the Capital Structures of European SMEs.” Journal of Business Finance & Accounting 31 (5-6): 711–28.

Schulze, Christian, Bernd Skiera, and Thorsten Wiesel. 2012. “Linking Customer and Financial Metrics to Shareholder Value: The Leverage Effect in Customer-Based Valuation.” Journal of Marketing 76 (2): 17–32.

Skiera, Bernd, Emanuel Bayer, and Lisa Schöler. 2017. “What Should Be the Dependent Variable in Marketing-Related Event Studies?” International Journal of Research in Marketing 34 (3): 641–59.