36.5 Marketing Return
Event Analysis
Nature of series
- Continuous
Univariate: Class Bass, Classic FDA
Multivariate Unidirectional: functional regression, classic Koyck, ADL, ARIMA
Multivariate Multidirectional: VAR, VARX, PVAR, Simultaneous Equation
- Punctuated
Event is dependent: Hazard models, split hazard, bivariate hazard
Evident is independent: Event analysis, synthetic control, DID
Decreasing rigor of causal inference
- Lab Experiment
- Field Experiment
- Nature Experiment
- Instrumental Variables
- Granger causality (improves with shocks)
- Times series regression (improves with shocks)
- Cross-sectional regression
Levels of testing causality in field
Correlation
Multiple regression: control for other plausible causes
Times series model (use of current and past values: Koyck, ADL, ARIMA)
First differences (effect of changes)
Lag of first differences (Arellano & Bond)
Granger causality (use of only past values of independent variables + control of past values of dependent variables (VAR), preferably in differences).
Intervention or event analysis
Natural experiments
RCT
Concept of Abnormal Return:
Stock price (\(P_t\)) = random walk
Return = \(P_t - P_{t-1}\) = white noise
Panel Regression
Sample similar firms, \(j\)
Identify each of their similar events: First stage regression (WRDS)
Estimate abnormal returns of each of these firms associated with each of those events \(e_{jt}\)
2nd stage: equation
Pool abnormal returns
Estimate factors that may affect the distribution of \(e_{jt}\)
Strength of event analysis
Increases with clearly defined event, narrow window of treatment, removal of confounding events
Long time series for baseline
large number of firms
diverse contexts of treatments
Extraction effects of known predictors
temporal dependent series (returns)
punctuated independent series: event
Focus on effects of event on series of returns
simulates a natural experiment
Define: a natural or artificial shock
Types of natural experiments:
Compare treated vs. untreated
compared before and after
DiD
Synthetic control
Types of pre-temporal controls
One prior period
baseline of prior period
synthetic control
function of known factors (Fama-French 4)
Cross-over (treated becomes control and rev)
Time capsule in Marketing
| Event | Source |
|---|---|
| market Entry | Factiva, Lexis-Nexis |
| new product | Factiva, Thomson Reuters |
| Consumers satisfaction | CSI |
| Innovation activities | Factiva, Cap IQ |
| Acquisitions | Factiva, SDC platinum |
| Quality | Web chat, product reviews |
| Advertising | TNS Stradegy, YouTube |
| Recalls | Govt web, others |
| Sales | Yahoo fin, 10k GFK, euromonitor, Nielsen |
| Earnings | SEC Filings |
| Stock Prices | CRSP, WRDS |
36.5.1 (Fornell et al. 2006) Customer satisfaction and stock return
Historically, people understand that customer satisfaction affects firm economic performance. But we haven’t studied the relationship between customer satisfaction and stock performance.
People don’t incorporate the info about customer satisfaction into the stock price right away (market is not so efficient)
From the literature, we understand that there are 4 determinants of a company’s market value
Acceleration of cash flow: speed of buyer response marketing efforts
increase in cash cash flows: repeat business and low marginal costs of sales
reduction in cash flow risk: lower by satisfaction
increase in the residual value of the business
Data: Compsutat + American Customer Satisfaction Index
Regression (correlation) analysis
\[ \ln Market value = \alpha + \beta_1 \ln Book value \\ + \beta_2 \ln Bookvalueliability + \beta_3 \ln ACSI \]
There is evidence for a correlation market value and customer satisfaction.
However, investors don’t always respond positively to increased satisfaction news
The firms is giving away consumer surplus
firms that already have leads over competition
Why trade-off between satisfaction and productivity
reverse causality
timing expectation (i.e., measurement of satisfaction)
36.5.1.1 Event study
- Suing market model to estimate abnormal return
\[ AR_{jt} = R_{jt} - (\alpha_j + \beta_j R_{mt}) \]
where \(j\) = firm, and \(t\) = day
estimation period = 255 days ending 46 days before the event date (McWilliams and Siegel 1997)
one-day event period = day when Wall Street Journal publish ACSI announcement.
5 days before and after event to rule out other news (PR Newswire, Dow Jones, Business Wires)
M&A, Spin-offs, stock splits
CEO or CFO changes,
Layoffs, restructurings, earnings announcements, lawsuits
No evidence for the effect of ACSI on CAR
36.5.2 (S. Srinivasan and Hanssens 2009) Marketing and Firm Value
Marketing investments don’t always translate to firm value readily.
Marketing investments are typically intangible:
brand equity
customer equity
customer satisfaction
R&D
product quality
specific marketing-mix actions
Market is not so efficient: e.g.
- Intangible-intensive firms are usually undervalued (Lev 1989)
Market Valuation Modeling:
Fame-French factor explains excess returns come from
market risk factor: excess return on a broad market portfolio
size risk factor: difference in return between a large and small cap portfolio
value risk factor: difference in return between high and low book-to-market stocks
Momentum: Carhart (1997)
Metrics:
Top-line (revenue)
bottom-line (earnings) surprises
Methods: 4-factor model can still have omitted variables
Metrics on Marketing and Firm value
Market cap: need to
isolate the book value (using Tobin’s q)
Incorporate random-walk behavior in stock prices (first difference of log(stock price))
stock returns
| Method | Characteristics | Litimations | Examples | Dependent/Independent |
|---|---|---|---|---|
| Four Factor Model | Assume efficient market theory | sensitive to benchmark portfolio correlation analysis can contain omitted variable bias examine cross-sectional variation only |
Tobin’s q/ Branding strategy Firm val/ brand value estimates Stock returns/ brand valuation |
|
| Event Study | Assume efficient market Causal Analysis |
can’t measure long-term effect | (Horsky and Swyngedouw 1987): name change (Chaney, Devinney, and Winer 1991): new product intro (Lane and Jacobson 1995): brand extension |
Stock returns/ name events Stock returns/ new product intro Stock returns/ brand extensions Stock returns/ Internet channel |
| Calendar protfolio | Include firms with certain to measure long-term impact more accurate than event studies |
Can’t measure per event effect might be sensitive to benchmark prtofolio |
(A. Sorescu, Shankar, and Kushwaha 2007) | Stock returns/ new product |
| Stock return response model | based on Carhart (1997) and EMH account dynamic properties of stock returns incorporate continuous events |
detailed data at the brand so business unit level marketing info must be public single equation model without temporal chain |
(D. A. Aaker and Jacobson 1994) |
Stock returns/ perceived quality Stock return / brand attitude stock return/ strategic shifts Stock returns/ marketing actions |
| Persistence modeling | system of equations: consumer (demand equation), manager (decision rule equation), competition, (competitive reaction equation), investor (stock price equation) VAR: examines both short-term and long-term robust to deviations from stationarity incorporate dynamic feedback loops |
detailed data at the business unit level time-series over a long horizon reduced-form models |
Firm value/ new product intro, sales promotions stock returns/ advertising |
4 factor model:
\[ R_{it} - R_{rf,t} = \alpha_i + \beta_i (R_{mt} - R_{rf,t}) + s_i SMB_t \\ h_i HML_t + u_i UMD_t + \epsilon-{it} \]
where
\(R_{it}\) = stock return for firm \(i\) at time \(t\)
\(R_{rf,t}\) = risk-free rate in period \(t\)
market factor = \(R_{mt}\) = market return in period \(t\)
Size factor = \(SMB_t\) = return on a value-weighted portfolio of small stocks - the return of big stocks
Value factor = \(HML_t\) = return on a vlaue-weighted portfolio of high book-to-market stocks - return on a value-wegihted portfolio of low book-to-market stocks
Momentum factor \(UMD_t\) = average return on 2 high prior-return portfolio - the average return on two low prior return portfolio
36.5.3 (Sood and Tellis 2009) Innovation and Stock Return
Innovation is important for firms
But firms are cautious when investing in R&D (long-term effect hard to justify)
Finding: innovations effect on stock prices is underestimated when events are distinct vs. aggregate
3 types of innovation activities
- Initiation: alliance, funding, expansions
- Development: Prototypes, patents
- Commercialization: Porudct Launch, awards
Takeaways
Total market returns to an innovation project: 643 mil (compared to 49 mil the return to an average event in the innovation project)
Positive events increase returns for all three types of events
Negative events decrease return for development and commercialization stages only
The absolute value of the market returns is higher for negative announcements than for positive announcements
36.5.4 (Jacobson and Mizik 2009b)
Disagreeing with previous research conclusion that there was a systemic mispricing of customer satisfaction into the stock price (Fornell et al. 2006) (Aksoy et al. 2008), the anomaly stem from only a small group of satisfaction leaders in the computer and internet sector. (i.e., sampling bias).
This study is consistent with (O’Sullivan, Hutchinson, and O’Connell 2009)
36.5.5 (Jacobson and Mizik 2009a)
36.5.6 (Borah and Tellis 2014) Choice of Payoff from announcements (Innovations)
- Whether a firm should make, buy or ally regarding new technologies
Innovation phases:
- Initiation
Make
Buy
Ally
- Development
- Commercialization
New product launch
initial shipments
new app and markets for the new products
awards
Models
- Model of returns
- Model of investment choice: multinomial logit model
- Model of payoffs:
36.5.7 (Tirunillai and Tellis 2012) Chatter effect on stock performance
Research questions:
Cor(UGC, stock performance)
What is the direction of causality
Among the UGC metrics, which best relates to stock performance
What are the dynamics of the relationship in terms of wear-in, war-out, and duration?
Data: 4 years, 6 markets , 15 firms
Findings:
Volume of chatter increases abnormal returns by a few day (using Granger causality tests) and trading volume
Positive UGC has no effect on abnormal returns
Negative UGC has negative effect on abnormal returns with a short “wear-in” and long “wear-out”
Interaction between chatter volume and negative chatter have a positive effect on trading volume
negative UGC positively correlates with idiosyncratic risk
Positive UGC has no effect on the idiosyncratic risk
Offline ad also increases the volume of chatter and decreases negative chatter
UGC:
- Product reviews + product ratings
Stock performance:
A measure of shareholder value
Available at the daily level
Assumption:
Market is not efficient: it takes time for the market to reflect info about UGC.
Asymmetric response across UGC metrics:
Losses loom larger than gain
investors discount positive info because it’s unreliable
Positive messages are usually influenced by the firms, but not negative
Sampling:
Product categories that have rich data on UGC (digital, high tech and popular consumer durable)
Product categories that reviews are related to sales
Public firm only
No M&A during the period
The sample markets should be representative of the whole market.
Time: June 2005 - Jan 2010
Media:
Product reviews instead of text or videos, etc because intuitively people use this form to express their opinion
Consumer reviews instead of evaluations, blogs, forums, because it’s more focused and greater signal-to-noise ratio
Consumer reviews instead of expert review because of wisdom of the crowds
3 popular websites: Amazon.com, Epinions.com, Yahoo! Shopping.
ratings + text reviews
Measures
UGC: ratings, volume chatter, positive valence, negative valence
Stock market performance
Abnormal returns: Fame-French (1993) three-factor + Carhart 1997 momentum factor.
Idiosyncratic risk: same model as abnormal returns
Trading volume: = daily turnover = volume of trade / shares outstanding at the end of the day
Using EGARCH specification:
\[ R_{i,t} - R_{f,t} = \alpha_i + \beta_{i, MKT} (R_{MKT, t} - R_{f,t}) + \beta_{i, SMB} SMB_t \\ + \beta_{i, HML} HML_t + \beta_{i, MOM} MOM_t + \epsilon_{i,t} \]
where
- \(\epsilon_{i,t} \sim N(0, \sigma_{i,t})\)
\[ \ln(\sigma^2_{i,t} ) = a_i + \sum_{j = 1}^p b_{i,j} \ln (\sigma^2_{i,t-j}) \\ + \sum_{k=1}^q c_{i,k}\{ \Theta (\frac{\epsilon_{i, t - k}}{\sigma_{i, t - k}}) + \Gamma (| \frac{\epsilon_{i, t-k}}{\sigma_{i, t-k}}| - (\frac{2}{\pi})^{1/2})\} \]
Control Variables
Analysts’ Forecasts: IBES Database
Advertising: TV ad from TNS media Intelligence
Media Citations: Number of articles in print media from LexisNexis (with relevancy score above 60%) and Factiva (using company tag)
New product Announcement: also LexisNexis and Factiva (following (Sood, James, and Tellis 2009))
Models
Vector Auto-regression (VAR)
can handle continuous events (instead of discrete events used in event studies)
account for immediate and lagged-term of the independent variables
capture the carryover effects over time with the generalized impulse response function
Controls for trends, seasonality, non-stationary, serial correlation, and reserve causality (Luo 2009)
Procedure
- Estimate the stationary (unit roots + co-integration) properties of stock performance and UGC
Stationarity test: Augmented Dickey-Fuller test + Kwiatkowski-Philips-Schmidt-Shin test
Co-integration: Johansen’s procedure (Johansen et al. 1992)
- Granger causality test
- Estimate dynamics of carryover effect using impulse response function
- Not sensitive to the causal ordering to the causal ordering of the variable in the system of equations
- Estimate the effect of UGC using variance decomposition: relative importance of metrics of UGC