33.3 Steps for Conducting an Event Study

33.3.1 Step 1: Event Identification

An event study examines how a particular event affects a firm’s stock price, assuming that stock markets incorporate new information efficiently. The event must influence either the firm’s expected cash flows or discount rate (A. Sorescu, Warren, and Ertekin 2017, 191).

Common Types of Events Analyzed

Event Category	Examples
Corporate Actions	Dividends, mergers & acquisitions (M&A), stock buybacks, name changes, brand extensions, sponsorships, product launches, advertising campaigns
Regulatory Changes	New laws, taxation policies, financial deregulation, trade agreements
Market Events	Privatization, nationalization, entry/exit from major indices
Marketing-Related Events	Celebrity endorsements, new product announcements, media reviews
Crisis & Negative Shocks	Product recalls, data breaches, lawsuits, financial fraud scandals

To systematically identify events, researchers use WRDS S&P Capital IQ Key Developments, which tracks U.S. and international corporate events.

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33.3.2 Step 2: Define the Event and Estimation Windows

33.3.2.1 (A) Estimation Window ($T_0 \to T_1$)

The estimation window is used to compute normal (expected) returns before the event.

Study	Estimation Window
(Johnston 2007)	250 days before the event, with a 45-day gap before the event window
(Wiles, Morgan, and Rego 2012)	90-trading-day estimation window ending 6 days before the event
(A. Sorescu, Warren, and Ertekin 2017, 194)	100 days before the event

Leakage Concern: To avoid biases from information leaking before the event, researchers should check broad news sources (e.g., LexisNexis, Factiva, RavenPack) for pre-event rumors.

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33.3.2.2 (B) Event Window ($T_1 \to T_2$)

The event window captures the market’s reaction to the event. The selection of an appropriate window length depends on event type and information speed.

Study	Event Window
(Balasubramanian, Mathur, and Thakur 2005; Boyd, Chandy, and Cunha Jr 2010; Fornell et al. 2006)	1-day window
(Raassens, Wuyts, and Geyskens 2012; Sood and Tellis 2009)	2-day window
(Cornwell, Pruitt, and Clark 2005; A. B. Sorescu, Chandy, and Prabhu 2007)	Up to 10 days

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33.3.2.3 (C) Post-Event Window ($T_2 \to T_3$)

Used to assess long-term effects on stock prices.

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33.3.3 Step 3: Compute Normal vs. Abnormal Returns

The abnormal return measures how much the stock price deviates from its expected return:

$$\epsilon_{it}^* = \frac{P_{it} - E(P_{it})}{P_{it-1}} = R_{it} - E(R_{it} | X_t)$$

where:

• $\epsilon_{it}^*$ = abnormal return

• $R_{it}$ = realized return

• $P_{it}$ = dividend-adjusted stock price

• $E(R_{it} | X_t)$ = expected return

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33.3.3.1 (A) Statistical Models for Expected Returns

These models assume jointly normal and independently distributed returns.

1. Constant Mean Return Model
   $$E(R_{it}) = \frac{1}{T} \sum_{t=T_0}^{T_1} R_{it}$$
2. Market Model
   $$R_{it} = \alpha_i + \beta_i R_{mt} + \epsilon_{it}$$
3. Adjusted Market Return Model
   $$E(R_{it}) = R_{mt}$$

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33.3.3.2 (B) Economic Models for Expected Returns

1. Capital Asset Pricing Model (CAPM)
   $$E(R_{it}) = R_f + \beta (R_m - R_f)$$
2. Arbitrage Pricing Theory (APT)
   $$R_{it} = \lambda_0 + \lambda_1 F_1 + \lambda_2 F_2 + ... + \lambda_n F_n + \epsilon_{it}$$

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33.3.4 Step 4: Compute Cumulative Abnormal Returns

Once abnormal returns are computed, we aggregate them over the event window:

$$CAR_{i} = \sum_{t=T_{\text{event, start}}}^{T_{\text{event, end}}} AR_{it}$$

For multiple firms, compute the Average Cumulative Abnormal Return (ACAR):

$$ACAR = \frac{1}{N} \sum_{i=1}^{N} CAR_{i}$$

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33.3.5 Step 5: Statistical Tests for Significance

To determine if abnormal returns are statistically significant, use:

1. T-Test for Abnormal Returns $$t = \frac{\bar{CAR}}{\sigma(CAR)}$$
2. Bootstrap & Monte Carlo Simulations
   • Used when returns are non-normally distributed.

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References

Balasubramanian, Siva K, Ike Mathur, and Ramendra Thakur. 2005. “The Impact of High-Quality Firm Achievements on Shareholder Value: Focus on Malcolm Baldrige and JD Power and Associates Awards.” Journal of the Academy of Marketing Science 33 (4): 413–22.

Boyd, D Eric, Rajesh K Chandy, and Marcus Cunha Jr. 2010. “When Do Chief Marketing Officers Affect Firm Value? A Customer Power Explanation.” Journal of Marketing Research 47 (6): 1162–76.

Cornwell, T Bettina, Stephen W Pruitt, and John M Clark. 2005. “The Relationship Between Major-League Sports’ Official Sponsorship Announcements and the Stock Prices of Sponsoring Firms.” Journal of the Academy of Marketing Science 33 (4): 401–12.

Fornell, Claes, Sunil Mithas, Forrest V Morgeson III, and Mayuram S Krishnan. 2006. “Customer Satisfaction and Stock Prices: High Returns, Low Risk.” Journal of Marketing 70 (1): 3–14.

Johnston, Margaret A. 2007. “A Review of the Application of Event Studies in Marketing.” Academy of Marketing Science Review 2007: 1.

Raassens, Néomie, Stefan Wuyts, and Inge Geyskens. 2012. “The Market Valuation of Outsourcing New Product Development.” Journal of Marketing Research 49 (5): 682–95.

Sood, Ashish, and Gerard J Tellis. 2009. “Do Innovations Really Pay Off? Total Stock Market Returns to Innovation.” Marketing Science 28 (3): 442–56.

———. 2007. “Why Some Acquisitions Do Better Than Others: Product Capital as a Driver of Long-Term Stock Returns.” Journal of Marketing Research 44 (1): 57–72.

Sorescu, Alina, Nooshin L Warren, and Larisa Ertekin. 2017. “Event Study Methodology in the Marketing Literature: An Overview.” Journal of the Academy of Marketing Science 45: 186–207.

Wiles, Michael A, Neil A Morgan, and Lopo L Rego. 2012. “The Effect of Brand Acquisition and Disposal on Stock Returns.” Journal of Marketing 76 (1): 38–58.