shuba srinivasan, koen pauwels, jorge silva-risso ...shuba srinivasan, koen pauwels, jorge...

24Journal of MarketingVol. 73 (January 2009), 24–43

© 2009, American Marketing AssociationISSN: 0022-2429 (print), 1547-7185 (electronic)

Shuba Srinivasan, Koen Pauwels, Jorge Silva-Risso, & Dominique M. Hanssens

Product Innovations, Advertising,and Stock Returns

Under increased scrutiny from top management and shareholders, marketing managers feel the need to measureand communicate the impact of their actions on shareholder returns. In particular, how do customer value creation(through product innovation) and customer value communication (through marketing investments) affect stockreturns? This article examines, conceptually and empirically, how product innovations and marketing investmentsfor such product innovations lift stock returns by improving the outlook on future cash flows. The authors addressthese questions with a large-scale econometric analysis of product innovation and associated marketing mix in theautomobile industry. They find that adding such marketing actions to the established finance benchmark modelgreatly improves the explained variance in stock returns. In particular, investors react favorably to companies thatlaunch pioneering innovations, that have higher perceived quality, that are backed by substantial advertisingsupport, and that are in large and growing categories. Finally, the authors quantify and compare the stock returnbenefits of several managerial control variables. The results highlight the stock market benefits of pioneeringinnovations. Compared with minor updates, pioneering innovations have an impact on stock returns that is seventimes greater, and their advertising support is nine times more effective as well. Perceived quality of the new carintroduction improves the firm’s stock returns, but customer liking does not have a statistically significant effect.Promotional incentives have a negative effect on stock returns, indicating that price promotions may be interpretedas a signal of demand weakness. Managers can combine these return estimates with internal data on project coststo help decide the appropriate mix of product innovation and marketing investment.

Keywords: marketing investments, advertising, financial performance, product innovations, stock returns, marketingand firm value, stock return response models

Shuba Srinivasan is Associate Professor of Marketing, School of Man-agement, Boston University (e-mail: [email protected]).Koen Pauwels is Associate Professor of Marketing, Ozyegin University,Istanbul, and Associate Professor of Marketing, Tuck School of Business,Dartmouth College (e-mail: [email protected]). JorgeSilva-Risso is Associate Professor of Marketing, A. Gary Anderson Grad-uate School of Management, University of California, Riverside (e-mail:[email protected]). Dominique M. Hanssens is Bud Knapp Profes-sor of Marketing, Anderson School of Management, University of Califor-nia, Los Angeles (e-mail: [email protected]). Theauthors thank the three anonymous JM reviewers, as well as seminar par-ticipants at the 2004 University of California/University of Southern Cali-fornia Marketing Conference, the 2004 and 2005 Marketing Science Con-ferences, the 2004 MSI Conference on Collaborative Research at YaleUniversity, the Spring 2005 Marketing Science Institute Board of TrusteesMeeting, the 2005 Marketing Dynamics Conference, the Lally School ofManagement at Rensselaer Polytechnic Institute, and the University ofWaikato, New Zealand, for their useful suggestions. They also thankMarnik Dekimpe, Bill Francis, Canlin Li, and Harald van Heerde for helpfulcomments and suggestions. Finally, the authors are grateful to the Mar-keting Science Institute for financial support.

Marketing managers are under increasing pressure tomeasure and communicate the value created bytheir marketing actions to top management and

shareholders (Lehmann 2004; Marketing Science Institute2004). These demands create a need to translate marketingresource allocations and their performance consequencesinto financial and firm value effects (Rust et al. 2004). Inparticular, how do customer value creation (through product

innovation) and customer value communication (throughmarketing investments) affect stock returns? Several studieshave identified innovation success as a key contributor toboth long-term firm sales and financial and stock marketperformance (Pauwels et al. 2004). In the same vein,Drucker (1973) cites innovation and marketing as the twofactors crucial to long-term corporate health. However, newproduct failure rate is high (ranging from 33% to more than60%) and has not improved over the past decades (Bould-ing, Morgan, and Staelin 1997; Sivadas and Dwyer 2000).Hauser, Tellis, and Griffin (2006) note that for each newproduct success, the process begins with six to ten conceptsthat are evaluated as they move from opportunity identifica-tion to launch. The high costs and risks involved with newproducts are the main culprit for the decline in both new-to-the-world (–44%) and new-to-the-company (–30%) innova-tions between 1990 and 2004 (Cooper 2005). The stockmarket’s reaction to new products is not guaranteed to bewarm either. For example, Boeing’s stock price surged 7%when it scrapped development plans for the 747X in Janu-ary 1997, and it declined 1.7% when the company revivedthe idea two years later—at a cost of $4 billion—to com-pete with the Airbus 380 (Dresdner Kleinwort BensonResearch 2000; The Wall Street Journal 1997). Similarly,there is pressure on marketing managers to demonstrate thecontribution of advertising to financial performance. This isnot surprising given weak evidence for the profit contribu-tion of advertising spending (Hanssens, Parsons, andSchultz 2001).

Product Innovations, Advertising, and Stock Returns / 25

Although the consumer response effects of marketingare well researched, a better understanding is needed ofmarketing’s impact on investor response, which is typicallymeasured by stock returns. Investors are motivated by cashflow expectations—in particular, the prospect of enhancingand accelerating future cash flows and of reducing associ-ated risks (Srivastava, Shervani, and Fahey 1998). More-over, many marketing actions are costly, and investors con-sider both their (expected) benefits and their downsides.Furthermore, the stock return impact of marketing actionsneeds to be assessed in the presence of other important dri-vers, as identified in the accounting and finance literature(Fama and French 1992; Kothari 2001). Thus, our centralresearch question is, To what extent do marketing actionsimprove stock returns beyond the typical finance andaccounting benchmark measures?

Our empirical research focuses on one industry, auto-mobiles, to enhance its internal validity. Moreover, webelieve that findings in this industry will be generalizable toother settings because a meta-analysis (Capon, Farley, andHoenig 1996, p. 214) indicates few industry-specific effectsof innovation performance, and though high returns neednot be sustainable in any particular market, the process ofgenerating high returns can be sustainable.

The automobile industry is of substantial economicimportance, representing more than 3% of the U.S. grossdomestic product (J.D. Power and Associates 2002). Inaddition, the industry relies heavily on new products, pro-motional incentives, and advertising. The main thrust ofcompetition is in product development, with each companycompeting in multiple market segments “with a plethora ofniche models designed to attract a particular group of con-sumers, and to renew them rapidly enough to keep interestfresh” (The Economist 2004, p. 14). However, the costs ofsuch design changes can be substantial, and their success isfar from certain. Therefore, large automobile firms facesubstantial innovation investment decisions across distinctproduct categories (called “segments” in industry parlance)that differ in category attractiveness and competitive condi-tions. Furthermore, automobile manufacturers invest bil-lions of dollars every year in various forms of advertising toinfluence customers and prospects to buy their products andservices. General Motors alone spent more than $2.8 billionin 2004 to advertise its lines of automobiles (TNS MediaIntelligence 2005). However, concerns persist about thefinancial impact and wisdom of such substantial communi-cations spending.

We organize the remainder of this article as follows:First, we develop the research framework and specify acomprehensive stock return response model to quantify therelationships. Second, we discuss the marketing and finan-cial data sources and estimate the models. Third, we formu-late conclusions, cross-validate the empirical results, anddiscuss their implications for marketing strategies.

Research FrameworkWe begin with the established financial benchmark—that is,Fama and French’s (1992, 1993) three-factor model—andthen augment it with Carhart’s (1997) proposed momentum

1A good example of these intertemporal effects in the car indus-try is a “lease pull ahead” program. Analysts at car manufacturerskeep track of the patterns of lease expirations. When they spot amonth in the future with an unusually large volume of leasereturns, they offer some lessees the option to return the car aheadof time, coinciding with a period of lower expected lease returns,or offer a promotional extension of the lease term. Furthermore, itis a common practice to target lease programs to terms coincidingwith an expected “valley” in lease returns. By seeking a stableflow of lease returns, manufacturers aim to generate a stable flowof new leases.

factor to obtain the four-factor model. This model producesa better estimate of expected stock returns than the capitalasset pricing model. The four-factor model posits that theexpected rate of return of a stock portfolio is a function ofrisk factors that reflect the market, size, book-to-market,and momentum factors. In addition, previous literature inaccounting and finance has demonstrated that stock returnsreact to changes in firm financial measures, including firmresults such as revenues and earnings (e.g., Kothari 2001).Controlling for these factors, we develop a conceptualframework to capture the effects of marketing activity onstock returns. We argue that such impact on firm valuationmay occur through one or more of four routes: (1) enhanc-ing cash flows, (2) accelerating cash flows, (3) reducingvulnerability in cash flows, and (4) increasing the residualvalue of the firm.

First, marketing investments, which can involve sub-stantial costs in the short run, can increase shareholdervalue by enhancing the level of cash flows (i.e., more cash),namely by increasing revenues and lowering costs. Forexample, automobile innovations that are responsive tounmet customer needs in specific segments, including theFord Mustang for young drivers and the Chrysler minivanfor families with children, have resulted in substantial reve-nue increases for these companies. Second, marketinginvestments can enhance shareholder value by acceleratingthe receipt of cash flows (i.e., faster cash). This is especiallyimportant in high-fixed-cost industries that depend on fastturnovers to finance their operations. For example, aggres-sive advertising helps develop instant awareness of newproducts that may accelerate the diffusion process. Third,marketing investments can increase shareholder value bylowering the vulnerability and volatility of these cash flows(i.e., safer cash), which results in a lower cost of capital ordiscount rate (Srivastava, Shervani, and Fahey 1998).1Thus, all else being equal, cash flows that are predictableand stable have a higher net present value and thus createmore shareholder wealth. For example, advertising mayhelp smooth out the variability in highly seasonal demandpatterns or, alternatively, may accentuate them (e.g., Fis-cher, Shin, and Hanssens 2007). Fourth, marketing invest-ments may increase the residual value of the firm. Buildingbrands and keeping them relevant and distinctive (e.g., bypioneering innovations) increase the equity of the brandsowned by the firm and, thus, its residual value.

Marketing actions can influence the outlook forinvestors on enhancing, accelerating, and stabilizing thefirm’s cash flows and increasing its residual value. We for-

26 / Journal of Marketing, January 2009

mulate the hypotheses in this section in terms of whichbrand-level marketing actions influence the stock returns,modeled through the main effect and the interaction effectwith new product introductions. Figure 1 and Table 1 pre-sent a summary of these drivers and their hypothesizedeffects.

Marketing Actions and Stock Returns

Innovativeness. The innovativeness, or relative advan-tage of new products, is a consistently important determi-nant of accelerated consumer adoption rate (Holak andLehmann 1990) and new product success (Montoya-Weissand Calantone 1994). On the basis of venture portfoliotheory (Booz Allen Hamilton 1982), we can classify theextent of innovation in new products on two dimensions: (1)

new to the company and (2) new to the market.2 The firstdimension measures the extent to which the new productintroduction is innovative compared with the firm’s existingproducts. The second dimension measures the extent towhich the firm’s new product is a new introduction to themarket. An example of a new-to-the-company innovationwithin the automobile industry context is the PorscheCayenne, which was the first sport-utility vehicle (SUV)developed by the company (and thus scores high on the firstdimension, offering Porsche loyals the opportunity to drive

FIGURE 1Conceptual Framework

2Specifically, we are interested in the extent to which a newautomobile model introduced is different from current offerings ofthe firm and those in the market. We do not consider specific inno-vations in processes or components.


Hypotheses and Drivers EnhancingCash Flows

AcceleratingCash Flows

ReducingVolatility of Cash Flows

Residual Value

Net Effect onUnexpected

Stock Returns

H1: Innovation level + ? + + ++H2: Pioneering innovation + ? ? + ++H3: Advertising support + + ? + ++H4: Promotional support +/– + ? – –H5: Customer liking + ? + + ++H6: Perceived quality + ? + + ++

TABLE 1Marketing Drivers of Stock Returns

Notes: + denotes that the driver has a positive effect, – denotes that the driver has a negative effect, +/– denotes that the driver could haveeither a negative or a positive effect, ? denotes no a priori hypothesis on the driver’s effect, and ++ denotes that the driver has a netpositive effect.

an SUV) but which entered a market already full of SUVs,including the sports car–based BMW X5 (and thus scoreslow on the second dimension). An example of a new-to-the-market innovation is the Toyota Prius hybrid. We discussthe impact of these innovation dimensions in turn.

New-to-the-company innovations. Renewing products iswidely regarded as necessary for long-term survival and asan engine of growth, thus enhancing cash flows and futureprofitability (Chaney, Devinney, and Winer 1991; Sorescu,Shankar, and Kushwaha 2007). Recent evidence on newproduct introductions in the context of the personal com-puter market suggests that enhancement in cash flowsoccurs as a result of reduced selling and general administra-tive expenses (Bayus, Erickson, and Jacobson 2003). Onaverage, the greater the new product’s improvement overprevious versions, the better is its long-term financial per-formance, and the greater is its firm value impact (Pauwelset al. 2004). In line with the J.D. Power and Associatesexpert rating scale, we consider the range from mere trim-ming and styling changes (Levels 1 and 2) to design andnew benefit innovations (Levels 3 and 4) to brand entry in anew category (Level 5) in the empirical analysis (Pauwels etal. 2004).

Developing new products faster and moving them intoproduction can accelerate cash flows from product innova-tion (Srivastava, Shervani, and Fahey 1999). In contrast,many products have failed to realize their potential becauseof insufficient attention to speeding up the market accep-tance cycle for these products (Robertson 1993). Largecompanies, especially, have been criticized for delaying therenewal and upgrade of their product offerings in the face ofchanging consumer preferences (Ghemawat 1991). Further-more, the success of innovations depends on consumers’timely adoption of the innovation, with both consumer andmarket factors being important drivers of the trial probabil-ity (e.g., Gielens and Steenkamp 2003).

Companies can reduce the vulnerability of their cashflows by completing their product portfolio with new-to-the-company products that enable them to address new con-sumer segments. For example, Toyota reduces cash flowvolatility by offering a full line of products and managingthe migration of customers from its economy models to itsluxury cars—for example, from Yaris to Corolla or fromCamry to Lexus ES. Furthermore, synergies between and

within product lines, including sharing components anddesign elements across such different products, can reduceproduction costs and inventory risk (Fisher, Ramdas, andUlrich 1999). In addition, a higher innovation level mayalso increase the residual value of the company. In the faceof shifting demand and fickle consumer preferences for thenewest products, brands with more improvements from onemodel to the next are more likely to remain fresh and, thus,relevant to today’s and tomorrow’s consumers.

Finally, recent empirical evidence has suggested a non-linear effect of the innovation level on new product success.On the demand side, Gielens and Steenkamp (2003) find aU shaped effect of product novelty on product trial proba-bility. Within a range of (nonradical) innovations, such asthose in their and our study, consumers prefer either lowcomplexity (minor update) or high relative advantage (newmarket entry). Moderate innovations typically do not offer(much) more advantage over minor innovations (Klein-schmidt and Cooper 1991) and thus appear stuck in themiddle. On the supply side (Sherman and Hoffer 1971),design and new benefit innovations (Levels 3 and 4 on ourscale) are much costlier than mere trimming and stylingchanges (Levels 1 and 2). For example, Cadillac’s EscaladeSUV innovation cost General Motors approximately $4 bil-lion (White 2001). Thus, combined with the U-shapeddemand impact, financial performance shows a U-shapedimpact of innovation level (Pauwels et al. 2004). Betweenminor updates and new market entries, the latter are betternews for the firm’s future value because “products high onnewness provide an especially strong platform for growth”(Gielens and Steenkamp 2007, p. 104). Although minorinnovations are necessary to maintain the stable stream ofcash flows from “bread-and-butter” products (e.g., Toyota’sfrequent minor updates to Camry), major product updatesare better able than minor product updates to enhance cashflows (Kleinschmidt and Cooper 1991) and, thus, stockreturns. In summary, we expect that the stock return bene-fits have a U-shaped relationship to each innovation level inthis scale, with a preference for new market entries overminor updates.

H1: (a) New-to-the-company innovations increase stock mar-ket returns, and (b) stock returns are U shaped in the levelof new-to-the-company innovation.


New-to-the-market (pioneering) innovations. Althoughnew product introductions benefit stock returns on average,new-to-the-market products have a greater impact (Chaney,Devinney, and Winer 1991). Indeed, the new product litera-ture has consistently related innovation success to a prod-uct’s ability to provide benefits and features not offered byalternative products (Henard and Szymanski 2001; Holakand Lehmann 1990). Pioneering innovations have a greaterpotential to unlock previously unmet customer needs and,thus, ultimately to surpass “me-too” innovations in terms ofenhancing cash flows (Kleinschmidt and Cooper 1991;Moorman and Miner 1997).

It is not clear a priori whether pioneering innovationsaccelerate cash flows compared with other innovations. Onthe one hand, relative advantage is a consistently importantdeterminant of accelerated adoption rate (Holak andLehmann 1990). On the other hand, consumers may alsoconsider pioneering innovations riskier, which delays adop-tion (Gatignon and Robertson 1985).

Finally, pioneering innovations also stand out as reduc-ing cash flow vulnerability and raising residual value.Indeed, although the short-term risk may appear to behigher, pioneering products also have option value; that is,they offer “the possibility of greater long-term financialgain given the possibility of their revolutionizing a productcategory” (Moorman and Miner 1997, p. 94). Indeed, firmscan reduce the vulnerability of their cash flows by stayingahead of competition in product innovation and introducingdifficult-to-copy new products. Moreover, investors mayview such pioneering innovations both as platforms forfuture product introductions and as signals that the firm issuccessful in the innovation process itself. Therefore, theirview of the residual value of the firm is likely enhanced.Finally, pioneering innovations offer new strategic choicesfor the firm by providing the opportunity to leverage theseinnovations to future products. For example, DuPont hasleveraged its invention of nylon and Teflon in a series ofsuccessful new product introductions in a variety of cate-gories. At the same time, radical pioneering innovations arelikely to increase the volatility of cash flows in the short runbut can eventually lead to stable cash flows. A notableexample of a radical pioneering innovation is the ToyotaPrius hybrid, which is tracking to commercial success as aresult of radical but visionary strategy. Overall, we postulatethe following:

H2: Pioneering (new-to-the-market) innovations have a greaterstock return impact than no-pioneering innovations.

Advertising support. Research over the past decade hasshown that marketing activity, such as advertising, can leadto more differentiated products characterized by lowerown–price elasticity (Boulding, Lee, and Staelin 1994). Inturn, this enables companies to charge higher prices, attaingreater market share and sales (Boulding, Lee, and Staelin1994), command consumer loyalty (Kamakura and Russell1994), and thus ward off competitive initiatives. Empiricalevidence from the automobile market suggests that advertis-ing expenditures generate greater cash flows for pioneersthan for later entrants (Bowman and Gatignon 1996).Therefore, advertising support for innovations, especially

pioneering innovations, can enhance cash flows for thecompany.

In addition, advertising builds awareness, which is anessential component of new product success. For example,Bly (1993, p. 125) notes that the “new-product innovatorwill spend more than twice as much on advertising and pro-motion as a business with fewer new products.” Recent evi-dence suggests that firms that invest more in marketingresources can better sustain the innovation and thus acceler-ate the adoption rate of their new products (Chandy and Tel-lis 2000). These benefits can lead to cash flow acceleration.

Finally, investments in the brand through advertisingcan reduce consumers’ perceived risk, particularly for radi-cal innovations (Dowling and Staelin 1994). As such, dif-ferentiation of a brand through advertising may lead tomonopolistic power, which can be leveraged to extractsuperior product-market performance, thus leading to morestable (i.e., less vulnerable to competition) earnings in thefuture (Srivastava, Shervani, and Fahey 1998). Conversely,advertising spending can exacerbate or smooth seasonaldemand patterns, leading to either an increase or a decreasein volatility, respectively.

Likewise, the increased brand differentiation throughadvertising should increase the residual value of the firm.Moreover, investors may perceive enhanced residual valuethrough advertising exposure beyond its impact on firmfinancial performance (Joshi and Hanssens 2007). There-fore, we hypothesize the following:

H3a: Advertising support for new-to-the-company innovationsincreases the stock market returns of these innovations.

H3b: Advertising support for pioneering innovations increasesthe stock market returns of these innovations.

Although we expect that advertising works for bothnew-to-the-company and new-to-the-market innovations,the latter should benefit most. Indeed, advertising worksbest when the firm has something new to offer the con-sumer (Lodish et al. 1995). When the product innovation isso pioneering that it (temporarily) dominates the competi-tion, firms may even reap permanent benefits from theiradvertising campaigns (Hanssens and Ouyang 2002).Therefore, we expect the following:

H3c: Advertising support benefits the stock market returnsmore for pioneering innovations than for new-to-the-company innovations.

Promotional support. The power of sales promotions toenhance future cash flows has been investigated extensivelyin empirical research. On the one hand, sales promotionsare effective demand boosters because they often have sub-stantial immediate effects on sales volume and profits(Dekimpe and Hanssens 1999). In terms of the conceptualframework, the main power of price promotions is to accel-erate cash flows, which is why managers often use them toreach sales quotas on time (e.g., Lee, Padmanabhan, andWhang 1997). On the other hand, promotions also signal aweakness in the customer value of the product relative tocompetition, particularly in the context of new productintroductions (Pauwels et al. 2004).


To the extent that sales promotions have positive short-term effects on top-line and bottom-line performance (Nijset al. 2001; Srinivasan et al. 2004), the use of sales promo-tions would accelerate cash flows. However, because pro-motion effects on sales are typically short lived, any posi-tive cash flow response will dissipate quickly.

In addition, for durable products (and particularly forautomobiles), manufacturers need to build and commitcapacity before the product is launched. Promotions andprice discounts could signal that the new product is per-forming below expectation in terms of sales, which in turnwould lead to either low-capacity utilization or a chronicdependence on price discounts. Thus, price discounts couldbe interpreted as signaling profit compression in the future.Especially important for automobiles, price promotions onnew vehicles may reduce the secondhand and trade-in mar-ket for used vehicles, which in turn affects the residualvalue of the firm’s portfolio of leased cars. Therefore, wepostulate the following:

H4: Promotional support for new-to-the-company innovationsdecreases the stock market returns of these innovations.

Customer perceptions of brand defects and brand per-ceived quality. In general, marketing theory predicts greatersuccess for firms that serve the needs of their customers bet-ter, especially by providing products that are superior to thecompetition in the customers’ eyes (Griffin and Hauser1993). Within the automobile industry, management cansignificantly improve a company’s fortunes by introducingnew products with superior features and minimal deficien-cies (e.g., General Motors’ recent push for more pleasingnew cars with fewer defects). Customer-focused measuresof these improvements include customer liking, quality, andsatisfaction. In markets for pioneering innovations, priorevidence suggests that the initial growth in customer baseand revenue is largely due to perceived quality improve-ments by incumbents as well as new entrants (Agarwal andBayus 2002). In other words, innovations that create anddeliver added consumer value contribute significantly to thesuccess of brands (Kashani, Miller, and Clayton 2000).

Apparently, investors view the quality signal as provid-ing useful information about the future-term prospects ofthe firm: Changes in perceived quality are associated withchanges in stock returns (Aaker and Jacobson 1994; Tellisand Johnson 2007). Favorable perceptions of product qual-ity and value by customers lead to differentiation and higherbrand loyalty, which in turn lead to higher buyer switchingcosts that can be exploited to enhance current profitabilityand cash flows or to increase the residual value of the firm.

A priori, it is unclear whether customer liking and per-ceived quality will also accelerate cash flows. Regardingcash flow stability, brands with favorable perceptions ofproduct quality likely enjoy a greater degree of “monopolis-tic competition” power. In other words, high customer qual-ity perceptions represent competitive barriers that reduceprice elasticity and generate more stable (i.e., less vulnera-ble to competition) earnings in the future. In summary, wepostulate the following:

3Within the context of the automobile industry, there are sixcategories based on the accepted industry classifications: SUVs,minivans, midsize sedans, compact cars, compact pickups, andfull-size pickups.

H5: Customer liking of new product introductions increasesstock returns.

H6: Perceived quality of new product introductions increasesstock returns.

Category Characteristics

We consider four category characteristics the controlvariables—category size, category growth rate, a firm’sshare of the category, and category concentration—on thebasis of previous literature (e.g., Capon, Farley, and Hoenig1996). Although previous marketing literature enabled us toformulate hypotheses on the impact of marketing actions onstock returns (H1–H6), our empirical analysis is exploratory,given the need for studies that examine the impact of cate-gory characteristics on stock returns. As such, we formulateexpectations about the direction of the effects rather thanformal hypotheses at this juncture.

Category size.3 The strength of category demand is animportant factor in brand success, and firms neglect marketsize assessment at their own peril (Cooper and Klein-schmidt 1993; Henard and Szymanski 2001). On the onehand, large categories enable firms to spread their fixedresearch-and-development and launch costs over a greaternumber of potential customers. On the other hand, largecategories are also attractive to competitors and thus willdraw more competitive innovation and attention. Goingafter larger categories may also reduce the vulnerability of afirm’s cash flows. If the new product introduction misses itsintended mark, other consumers in the large category mayhave an interest. For example, when Cadillac launched aredesigned Escalade SUV in 2002, it became highly suc-cessful with an unintended market segment—professionalathletes, rappers, and celebrities. In turn, Cadillac has begunto pursue these trendsetters by giving them previews of thenext-generation Escalade, by offering them limited-editionversions, and so forth (Eldridge 2004). Moreover, largecategories may provide a better cushion against damage by competitive marketing actions or exogenous changes(Aaker and Jacobson 1990).

Category growth rate. Firms that target high-growthcategories achieve higher sales and better financial perfor-mance, leading to enhanced cash flows (Capon, Farley, andHoenig 1996). Moreover, competitive reactions to newproduct introductions are likely to be less aggressive whenthe incumbent sales continue to grow at a satisfactory rate,which would be the case when the product innovationincreases primary demand (Frey 1988). Likewise, advertis-ing reactions to new product introductions are less likely ingrowing versus static categories (Cubbin and Domberger1988). In turn, this lower competitive intensity leads toenhanced cash flows. Moreover, investments are preferen-tially directed toward high-growth categories and awayfrom established businesses in slower-growth categories


(Wensley 1981) because the expected payoff is better inhigh-growth categories. Similarly, when the categorydemand is growing, it is easier for all competitors to acquirecustomers rapidly, leading to acceleration in cash flows(Cooper 1999; Scherer 1980).

Finally, commitment of marketing resources inemerging-growth categories reduces risk in the future.Indeed, investors are likely to reward share gains in growingcategories because the returns are expected to grow as thecategory grows.

Firm’s share of category. The firm’s current marketshare in a category may affect its long-term performance inseveral ways. A firm’s high market share typically resultsfrom a strong relative advantage in the served segment(Phillips, Chang, and Buzzell 1983), which in turn enhancescash flows. At the same time, dominant firms have more tolose from cannibalization (Chandy and Tellis 2000), whichcould jeopardize the price premiums on their establishedproducts. This has an opposite impact on cash flows. Inaddition, it is unclear a priori to what extent the firm’s shareof the category affects the acceleration of cash flows fromnew product introductions. Finally, the volatility of cashflows is reduced when the firm has a dominant market shareand therefore is more likely to retain a large proportion ofcustomers on an ongoing basis (Srivastava, Shervani, andFahey 1998). However, firms with a large share of a cate-gory may become complacent in that category as theirmanagerial priorities shift to other, higher-growth opportu-nities (Kashani 2003), leading to increased vulnerability incash flows. Given these opposing forces, we explore theeffect of firm category share.

Category concentration. A brand’s success criticallydepends on competitive category conditions, including cate-gory concentration (Capon, Farley, and Hoenig 1996;Cooper and Kleinschmidt 1993). Economic theory suggeststhat in concentrated categories, profit margins are higher.Moreover, companies in concentrated categories are lessmotivated to engage in price wars because they dissipate theattractive margins. Thus, increases in category concentra-tion are more likely to increase cash flows and, thus, stockreturns. Finally, faced with only a few competitors, a firm isless likely to be surprised by disruptive innovations thataffect the stability of its income streams. Therefore, cate-gory concentration will likely reduce the vulnerability ofcash flows.

Research MethodologyWe use stock return response modeling to assess the degreeto which marketing actions and category conditionsimprove the outlook on a firm’s cash flows and thus lift itsstock price. In essence, stock return response modelingestablishes whether the information contained in a measureis associated with changes in expectations of future cashflows and, thus, stock price and returns (for a review, seeMizik and Jacobson 2004). We present a “unified” estima-tion of firm stock returns by specifying a model that enablesus to assess the proposed hypotheses directly.

Stock Return Response Modeling

It is well known that the economic return to a marketingactivity, such as a new product introduction, is obtainedover the long run (e.g., Pauwels et al. 2004). Therefore, wemay consider a firm’s marketing activity an intangible assetthat influences future cash flows. As such, “the value of amarketing strategy to the firm can be depicted as a dis-counted present value of the future cash flows generatedthrough the use of this marketing strategy” (Mizik andJacobson 2003, p. 67).

The stock market valuation of a firm depicts the marketexpectations of these discounted future cash flows. The effi-cient market hypothesis implies that stock prices followrandom walks; the current price reflects all known informa-tion about the firm’s future earnings prospects (Fama andFrench 1992). For example, investors may expect the firmto maintain its usual level of advertising and price promo-tions. Developments that positively affect future cash flowsresult in increases in stock price, whereas those that nega-tively affect cash flows result in decreases. Whereaschanges to typical marketing time series, such as consumersales, are mostly temporary (Dekimpe and Hanssens 2000;Ehrenberg 1988), changes to stock prices are predominantlypermanent (Fama and French 1992; Malkiel 1973). Bytaking the first differences of the logarithm of stock prices, a stationary time series of stock returns is obtained as adependent variable. In the context of this article, regressingstock returns against changes in the marketing mix providesinsights into the stock market’s expectations of the associ-ated long-term changes in cash flows.

Assessing the Impact of Marketing Actions onStock Returns

The framework for assessing the information content of ameasure enjoys a long tradition in both finance (e.g., Balland Brown 1968) and marketing (see, e.g., Jacobson andAaker 1993; Madden, Fehle, and Fournier 2006). The latterresearch stream has attempted to assess the stock marketreactions to nonfinancial information, including firms’customer-based brand equity (Aaker and Jacobson 2001;Barth et al. 1998), brand extension announcements (Laneand Jacobson 1995), online channel addition (Geyskens,Gielens, and Dekimpe 2002), and a shift in strategic empha-sis from value creation to value appropriation (Mizik andJacobson 2003). In the tradition of stock return responsemodeling, these studies test for incremental informationcontent—that is, the degree to which a series explains stockprice movements beyond the impact of current accountingmeasures, such as revenue and earnings.

We begin with a well-established benchmark in thefinance literature—that is, the four-factor explanatorymodel, which estimates the expected returns (Eretit) as afunction of risk factors that reflect the general stock mar-ket, size, the relative importance of intangibles (book-to-market ratio), and momentum (Fama and French 1992,1993). Riskier stocks are characterized by higher returns, sosmaller firms are expected to outperform larger firms,stocks with higher book-to-market ratios are expected tooutperform stocks with lower book-to-market ratios, and


4More details on the four factors and related data are availableon Kenneth French’s Web site (see http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).

stocks with higher momentum (i.e., high past return) areexpected to outperform stocks with lower momentum. Thetypical financial benchmark model for stock returns is esti-mated as follows:

where Rit is the stock return for firm i at time t, Rrf,t is therisk-free rate of return in period t, Rmt is the average marketrate of return in period t, SMBt is the return on a value-weighted portfolio of small stocks less the return of bigstocks, HMLt is the return on a value-weighted portfolio ofhigh book-to-market stocks less the return on a value-weighted portfolio of low book-to-market stocks, andUMDt is the average return on the two high prior returnportfolios less the average return on the two low prior returnportfolios (i.e., momentum). Moreover, εit is the error term;αi is the model intercept; and βi, si, hi, and ui are parameterestimates of the four factors used in the model. The SMBand HML factors are constructed using portfolios formedon size and book to market, and the UMD factor is con-structed using portfolios formed on prior 2- to 12-monthreturns.4 If the stock’s performance is “normal,” the four-factor model captures the variation in Rit, and αi is zero.

Next, we augment the financial benchmark model(Equation 1) with marketing variables to test hypotheses ontheir impact on future cash flows. As argued previously, weexpress the marketing variables in unanticipated changes—that is, deviations from prior behaviors that are alreadyincorporated in investor expectations. We define the modelat the brand level and the category level as follows:

( )2 1 2

1

R R Eret U INC U REVit ft it it it−

=

= + +

+

β βΔ Δ

l

55

3 4

5

∑ +

+ +

β β

β β

U INND U CINN

PION

ijkt ijkt

ijkt

Δ Δ,l

66

71

4

CPION

U OMKT

ijkt

own ijkt ownown

+

+

=∑ β

β

, ,Δ

881

4

9

, ,

,

cross ijkt crosscross

nn

U CMKT

U

Δ

Δ

=∑

+ β=

∑ + ×

+

1

4

1

2

CAT U INC U INN

U REV

kt n it ijkt

i

, γ

γ

Δ Δ

Δ tt ijkt

own ijkt ownown

U INN

U OMKT

×

+ ×=

Δ

Δγ 31

4

, ,(∑∑ × U INNijktΔ )

( ) ( ), ,1 R R R R

s SMB h HML

it rf t i i mt rf t

i t i t

− = + −

+ +

α β

++ +u UMDi t itε ,

5It would be ideal to run the regression RETBRAND = bX + μ,where RETBRAND is the return associated exclusively with the par-

where Rit is the stock return for firm i at time t, Eretit is theexpected return from the Fama–French benchmark model inEquation 1, and the subscripts j and k denote the brand andcategory and l denotes the innovation level. The inclusion ofbrand and category subscripts is relevant for two reasons:First, since the stock return impact is likely to be differentacross brands and categories because of cross-sectionalheterogeneity, it is important to account for such hetero-geneity from an econometric perspective. Second, managerswant to pinpoint which brands (e.g., those with more versusless advertising support, innovation level, and quality) and/or targeted categories contribute more or less to the firm’sstock return. The subscripts “own” and “cross” denote ownand competitive marketing variables (advertising, promo-tional incentives, liking, and quality). The subscript ndenotes the category variables (size, growth rate, concentra-tion, and the market share of the firm in the category). Theunexpected components of stock returns are of two kinds:results and actions or signals. Results include unanticipatedaccounting earnings (UΔINC) and revenues (UΔREV). Spe-cific marketing actions or signals are the unanticipatedchanges to brand innovation (UΔINND); to pioneeringinnovations (PION); and to advertising, promotions, cus-tomer liking, and perceived product quality to the brand(UΔOMKT). Competitive actions or signals in the modelare the unanticipated changes to competitive brand innova-tion (UΔCINN); to competitive pioneering (CPION); and tocompetitive advertising, promotions, customer liking, andperceived quality (UΔCMKT). Finally, category variables(UΔCAT) include category size, category growth rate, cate-gory concentration, and the firm’s market share in the cate-gory (to capture cannibalization effects), and εit is the errorterm. Note that we include the possibility for interactions ofeach marketing variable with both the innovation level(UΔINN) and the pioneering nature of the product innova-tion (PION). The unanticipated components are modeledwith either survey data of analysts’ expectations or time-series extrapolations (Cheng and Chen 1997). This studyfollows the latter approach, using the residuals from a time-series model as the estimates of the unanticipatedcomponents.

In stock return response models, such as the one wedescribed previously, a test of “value relevance” of unex-pected changes to firm results and actions is a test for sig-nificance of the β and γ coefficients; significant valuesimply that these variables provide incremental informationin explaining stock returns.5 Empirically, we estimate using

+ ×=

U OMKTown ijkt ownown

Δ(, ,γ 41

44

5

∑ ×

+ ×

PION

U CMKT

ijkt

cross ijkt crosscro

)

(, ,γ Δsss

ijkt

n kt n i

U INN

U CAT U INN

=∑ ×

+ × ×

1

4

6

Δ

Δ Δ

)

(, ,γ jjktn

) ,=

∑ +1

4

ε


ticular brand information X. However, given the corporate natureof stock returns, the regression we estimate is RET = βX + ε,where RET is the total corporate stock return, which is composedof RETBRAND and RETNOT-BRAND (i.e., the stock return that is notassociated with the brand). Because RET = (RETBRAND +RETNOT-BRAND), it can be shown that the least squares estimate ofE[β] = E[(X′X)–1X′(RETBRAND + RETNOT-BRAND)] = b (Gey-skens, Gielens, and Dekimpe 2002; Lane and Jacobson 1995),leading to an unbiased estimate under the reasonable assumptionthat RETNOT-BRAND and X are uncorrelated. As a further test ofthis assumption, we estimate an expanded model that includescannibalization effects of other brands owned by the same firmand find that our substantive results are robust to this issue (fordetails, see n. 12).

6With the exception of price promotions, all the other variablescome from national sources. In the auto industry, it is typical forpromotions to be planned and executed at the national level, andthey are advertised nationally through television networks. Assuch, the price promotions data from California are representativeof the price promotions of other U.S. regions.

7Moreover, this data set is at the detailed “vehicle” level,defined as every combination of model year, make, and model(e.g., 1999 Honda Accord, 2000 Toyota Camry); body type (e.g.,convertible, coupe, hatchback); doors (e.g., two-door, four-door,four-door extended cabin); trim level (e.g., for Honda Accord, DX,EX, LX); drive train type (e.g., two-wheel drive, four-wheeldrive); transmission type (e.g., automatic, manual); cylinders (e.g.,four cylinder, six cylinder); and displacement (e.g., 3.0 liters, 3.3liters).

a fixed-effects cross-sectional time-series panel model tocontrol for unobserved brand and firm characteristics. Wetest for pooling versus estimating a fixed-effects cross-sectional time-series panel model to evaluate the signifi-cance of the cross-section effects using the sums-of-squaresF-test and the likelihood function using EViews 6.0 (2007,p. 568). Because we have multiple observations by firm(i.e., multiple brands of the same firm in up to six cate-gories, as we describe in the “Data and Variable Operation-alization” section), we use seemingly unrelated regressionestimation to account for the contemporaneous correlations.

Data and Variable Operationalization

We focus on the 1996–2002 automobile industry’s “bigsix”—Chrysler, Ford, General Motors, Honda, Nissan, andToyota—which represent approximately 86% of the U.S.car market. Sales transaction data from J.D. Power andAssociates are available for a sizable sample of dealershipsin the major metropolitan areas in the United States. For thesales promotions, we use data from California dealerships,which contain every new car sales transaction in a sampleof 1100 dealerships from October 1996 to June 2002.6 Eachobservation in the J.D. Power and Associates database con-tains the transaction date, manufacturer, model year, make,model, trim and other car information, transaction price,and sales promotions (operationalized as the monetaryequivalent of all promotional incentives per vehicle).7 Thevehicle information is aggregated to the brand, representinga brand’s presence in each category (e.g., Chevrolet SUV).Table 2 clarifies the variables, their definitions, specific datasources, and the temporal and cross-sectional aggregationof each variable. Importantly, note that a certain brand canexperience an innovation at several weeks during a year

8For reasons of parsimony, we restrict our attention to brandsthat together account for at least 80% of the share of the categoryunder consideration.

9The customer liking and perceived quality variables are aggre-gated to the brand level as the market share–weighted average ofthe respective ratings of the models for brand j. This enables us toincorporate the effects of changes in the vehicle model mix onfirm valuation. For example, if market conditions cause a drop insales of full-size SUVs relative to midsize SUVs, the product linesof General Motors and Ford become less attractive than those ofToyota and Nissan.

because its (sub)models introduce their new versions at dif-ferent times. We consider 53 brands in six major productcategories: SUVs, minivans, midsize sedans, compact cars,compact pickups, and full-size pickups (see Table 3).8

Another source of J.D. Power and Associates data isexpert opinions on the innovation level of each vehicleredesign or introduction. We obtained these data from thework of Pauwels and colleagues (2004), which provides anextensive discussion of those data.

For the “pioneering” innovation variable, in line withthe J.D. Power and Associates (1998) guidelines, expertsrate innovativeness as pioneering or not. An example ofLevel 1 for the premium car category is the 2001 ToyotaPrius, the first gasoline–electric hybrid that could functionas a versatile family car. For the SUV category, an exampleof a pioneering innovation is the 1999 Lexus RX 300, thefirst car-based SUV designed to compete in the luxury SUVsegment. Table 4 provides specific illustrations of pioneer-ing innovations.

Another important set of J.D. Power and Associates datais the annual surveys of the Automotive Performance, Exe-cution and Layout (APEAL) Study and of the Initial Qual-ity of cars, based on feedback from more than 60,000 cus-tomers on the experience of the first months of ownership.The former is a customer-driven metric of “things goneright,” which measures customers’ perceptions on thedesign, content, layout, and performance of their new vehi-cles during the first three to seven months of ownership. Weuse this measure to operationalize customer liking. The lat-ter, which is our measure of perceived quality, is based onfeedback from more than 60,000 customers on the experi-ence of the first 90 days of ownership and measures thenumber of problems by each brand; essentially, this is ameasure of “things gone wrong” (see Table 2).9 A finalsource of data is advertising data from TNS Media Intelli-gence on monthly advertising expenditures by make andmodel in each of the six categories.

We obtain stock returns from the Center for Research inSecurity Prices. The data source for the four factors is Ken-neth French’s Web site at Dartmouth (see n. 4). For firm-specific and quarterly accounting information, such as bookvalue, revenues, and net income, we use Standard & Poor’sCOMPUSTAT database. The COMPUSTAT data set alsoprovides monthly indexes of the Consumer Price Index,which is used to deflate the monetary variables. To obtainweekly Consumer Price Index data, we linearly interpolatedthe monthly numbers (see Franses 2002). Table 5 providesthe descriptive statistics for the measures that form the basisof our analysis. We choose the week as the time interval of


TABLE 2Data: Variable Definitions

Variable Definition Source

Dependent VariableStock returns (Rit) (Pricet + Dividendt – Pricet – 1)/(Pricet – 1) Center for Research in Security

Prices

Four FactorsMarket risk (Rm-Rf)

Size risk (SMB)

Value risk (HML)

Momentum (UMD)

Rm is the average market rate of return, and Rf is the risk-freerate of return.

Difference of returns on a value-weighted portfolio of smallstocks and the return on large stocks.

Difference of returns on a value-weighted portfolio of high andlow book-to-market stocks.

Difference of the average return on the two high-prior-returnportfolios and on two low-prior-return portfolios.

Kenneth French’s Web site (seen. 4)

Firm ActionsFirm income Firm income, scaled by dividing by firm assets, is the earnings

of firm i in week t.COMPUSTAT

Firm revenue Firm revenue, scaled by dividing by firm assets, is the revenueof firm i in week t.

COMPUSTAT

Seasonal andholiday variables

It is set to 1 for one week before the event, during the week ofthe event, and one week following the event and to 0

otherwise, around the following holidays: Labor Day weekend,Memorial Day weekend, and the end of each quarter.

Product innovation Brand innovation variable for brand j in category k for firm i attime t is defined as the maximum of the innovation variable forall vehicle model transactions in that week, as in Pauwels and

colleagues (2004). This variable is used to create theinnovation variables that measure each innovation level from 1

to 5.

J.D. Power and Associates expertopinions; J.D. Power and

Associates weekly transactionsdata

Competitive productinnovation

Market share–weighted average of the product innovation ofall the other brands (other than the focal brand) in the

category.

J.D. Power and Associates expertopinions; J.D. Power and

Associates weekly transactionsdata

Pioneeringinnovation

A dummy variable indicates whether the J.D. Power andAssociates experts rate innovations as pioneering (1) or not.

This variable is set to 1 in the week of introduction of thepioneering innovation and to 0 otherwise.

J.D. Power and Associates expertopinions

Competitivepioneeringinnovation

This variable is set to 1 in the week of introduction of thepioneering innovation of the other brands (other than the focal

brand in the category) and to 0 otherwise.

J.D. Power and Associates expertopinions

Advertising support Advertising expenditure in millions of dollars for brand j incategory k for firm i at time t, scaled by firm assets.

TNS Media Intelligence

Competitiveadvertisingsupport

Market share–weighted average of the advertising support ofall the other brands (other than the focal brand) in the

category.

TNS Media Intelligence

Promotional support The monetary equivalent of promotional incentives for brand jin category k for firm i at time t; the brand-level calculated bythe market share–weighted average of the incentives for all

models of brand j in category k, scaled by firm assets.

J.D. Power and Associates weeklytransactions data


TABLE 2Continued

Variable Definition Source

Competitivepromotionalsupport

Market share–weighted average of the promotional support ofall the other brands (other than the focal brand) in the

category.


Brand’s customerliking

The brand-level measure is calculated as the marketshare–weighted average of the perceived APEAL rating of themodels for brand j in category k at time t. This is a customer-

driven measure of “things gone right,” which measurescustomer perceptions on the design, content, layout, andperformance of their new vehicles during the first three to

seven months of ownership.

J.D. Power and Associates surveydata for the APEAL measure; J.D.

Power and Associates weeklytransactions data

Competitivecustomer liking

Market share–weighted average of the perceived APEAL of allthe other brands (other than the focal brand) in the category.

J.D. Power and Associates surveydata for the APEAL measure; J.D.

Power and Associates weeklytransactions data

Brand’s perceivedquality

The brand-level measure is calculated as the marketshare–weighted average of the perceived quality of the modelsfor brand j in category k at time t. This survey, which is based

on feedback from more than 60,000 customers on theexperience of the first 90 days of ownership, measures the

number of problems by each brand and is a measure of“things gone wrong.” Thus, the latter Initial Quality Survey

measure is negatively signed to obtain the perceived qualitymetric.

J.D. Power and Associates InitialQuality Survey for the APEAL

measure; J.D. Power andAssociates weekly transactions

data

Competitiveperceived quality

Market share–weighted average of the perceived quality of allthe other brands (other than the focal brand) in the category.

J.D. Power and Associates InitialQuality Survey for the APEAL

measure; J.D. Power andAssociates weekly transactions

data

Category Control Variables Category size Category size is the total sales in week t for category k. J.D. Power and Associates weekly

transactions data

Category growthrate

The metric of interest is the ratio of category growth rate ofcategory k to total growth rate for all auto sales, which yields a

measure of relative attractiveness of a category.


Market share Market share of the firm i in category k in week t. J.D. Power and Associates weeklytransactions data

Categoryconcentration

Category concentration is the sum of the market share of the top three brands within category k in week t.


analysis because (1) previous stock return modeling studieshave demonstrated that a few days suffice for studyingproduct innovation (e.g., Chaney, Devinney, and Winer1991), (2) weekly return data guard against noisy day-to-day (or even hour-to-hour) day-trading patterns, and (3) theproduct innovation variable is available at the weekly level.

Because the stock market reacts only to unexpectedinformation, explanatory factors should only reflect unan-ticipated changes. To obtain a measure of unanticipatedchanges, we estimate a time-series model and use the resid-uals as the estimates of unanticipated components. As anillustration, a first-order autoregressive model has beenwidely used to depict the time-series properties of firm per-formance, such as earnings (Yit):

where the coefficient θ1 is the first-order autoregressivecoefficient depicting the persistence of the series and ηit

provides a measure of unanticipated portion of Yit and isused as the explanatory variable in the estimation of thestock return response model in Equation 2. (In other words,the residuals in Equation 3 provide an estimate of UΔYit).

Empirical ResultsTable 6 shows the correlations among the variables. Thevariance inflation factors range from 1.18 to 1.72, which is

( ) ,3 0 1 1Y Yit it it= + +−θ θ η


TABLE 3Brands of the Six Leading Car Manufacturers

Category Brands Chrysler FordGeneralMotors Honda Nissan Toyota

SUVs 15 Dodge, Jeep FordLincoln

ChevroletCadillac

GMCOldsmobile

Buick

HondaAcura

NissanInfiniti

ToyotaLexus

Minivans 9 Dodge,Chrysler

FordMercury

ChevroletOldsmobile

Honda Nissan Toyota

Premiummidsize cars

9 Chrysler FordMercury

ChevroletOldsmobile

Buick

Honda Nissan Toyota

Premiumcompact cars

8 Chrysler Ford ChevroletPontiacSaturn

Honda Nissan Toyota

Compactpickups

6 Dodge Ford ChevroletGMC

Nissan Toyota

Full-sizepickups

7 Dodge FordLincoln

ChevroletGMC

Cadillac

Toyota

TABLE 4Examples of Pioneering Innovations

CategoryPioneeringInnovation Description

Compactcar

2001 ToyotaPrius

First gasoline hybrid

Truck 2002 ChevroletAvalanche

Unique convertible cabsystem to transform from afive-passenger SUV into a

standard cab pickup

Truck 2002 LincolnBlackwood

Introduced as a crossbetween a luxury SUV and a

pickup truck

SUV 1999 Lexus RX300

First car-based SUV in theluxury segment

Minivan 1999 HondaOdyssey

First introduced the hideawayor “magic seat”

acceptable and suggests that multicollinearity among thevariables is not a concern.

We first estimate the benchmark four-factor financialmodel in Equation 1, followed by the focal model in Equa-tion 2.10 The stock return models are statistically significantat p < .05 for both the benchmark four-factor financial

model and the focal model, including firm results and firmactions. We discuss our main results on the benchmark four-factor financial model, Pauwels and colleagues’ (2004)model, and the focal model, as well as the robustness of theimplied causality from marketing mix to stock marketreturns.

Results of the Benchmark Models

The benchmark four-factor model. Are stock returnsaffected by the four risk factors of size (SMB), the impor-tance of intangibles (HML), risk class (Rm-Rf), andmomentum (UMD)? As Columns 3 and 4 of Table 7 show,the benchmark four-factor financial model is statisticallysignificant at p < .05 with the adjusted R-square of .154.The market-risk coefficient is positive and significant (.308,p < .01) and different from 1.00, suggesting that the big-sixautomobile firms have below-average market risk. Indeed,consistent with the capital asset pricing model and the four-factor financial model, the coefficient for market risk domi-nates all other explanatory variables in our models in termsof t-values. The coefficient for size risk, SMB, is positiveand significant (.041, p < .05), and the coefficient for valuerisk is also positive and significant (.302, p < .01). Thus,these results confirm well-established findings that (1)small caps and (2) stocks with a high book-to-market ratiotend to do better than the market as a whole. Notably, theonly variable that does not significantly explain stockreturns in our data, momentum, represents a later additionto the four-factor model.

Pauwels and colleagues’ (2004) variables. In Columns5 and 6 of Table 7, we report the results of the stock returnresponse model, adding Pauwels and colleagues’ (2004)marketing variables. As for the Fama–French factors, SMB,

10Unit root tests reveal evolution in income and revenue but sta-tionarity in stock returns, the marketing variables, and thecategory-specific variables. A cointegration test for the existenceof a long-term equilibrium among these evolving variables pro-duced a negative result.


TABLE 5Characteristics of the Six Leading Car Manufacturers 1996–2002

Characteristic FordGeneralMotors Chrysler Honda Nissan Toyota

U.S. market share 21% 28% 15% 8% 4% 10%Market capitalization (in billions of dollars) 48.6 36.7 44.3 34.1 18.5 112.2Quarterly firm earnings (in millions of dollars) 01,570 01,040 00,845 00,530 750 01,015Quarterly firm revenue (in millions of dollars 37,025 40,600 29,080 12,090 10,765 25,220Stock market returns (%) –.077% .086% .199% –.063% .102% .165%Brand advertising (yearly, in millions of dollars) 00,720 0,1430 00,660 00,250 00,290 00,400Number of new product introductions (Levels 1–5) 00,113 00,093 00,067 00,042 00,024 00,056Sales promotions per vehicle ($) 00,390 00,640 00,640 00,025 00,200 00,120Customer liking score 00,603 00,588 00,644 00,601 00,626 00,613Perceived quality score 00,203 00,248 00,187 00,232 00,230 00,226

Notes: The values reported are the sample mean of the time series. The exception is new product introductions, for which we report the totalnumber.

11An argument in favor of a random coefficients model couldalso be constructed using tests to detect such departure from theconstant parameter assumption. As such, we tested for this usingthe variation of the Lagrange-multiplier test (Hsiao 2003, pp.147–49). A chi-square test (using a significance level of p < .05)did not reveal departure from the assumption of fixed coefficients.

HML, and Rm-Rf remain significant. Moreover, the mar-keting actions regarding innovation and promotion and theirfirm results regarding revenue and income significantlyaffect stock returns beyond the four factors. Indeed, the“marketing + finance” model explains twice as much vari-ance in stock returns as the nested “finance-only” model.Still, our richer focal model outperforms Pauwels and col-leagues’ (2004) nested benchmark. Therefore, we focus ondiscussing the results of the focal model and return toPauwels and colleagues’ benchmark results in the “Manage-rial Implications” section for comparison purposes.

Focal Model Results

The focal model reported in Columns 7 and 8 of Table 7 isstatistically significant (F-value at p < .05). The sums-of-squares F-test and the likelihood function test statistics forpooling versus fixed effects reject the null that the fixedeffects are redundant (p < .05).11 Notably, the estimatedeffect of size (SMB) becomes statistically insignificant,indicating that the size effect is likely captured by the firm-specific marketing actions and the results included in thefocal modal. We tested for autoregressive conditional het-eroskedasticity (ARCH) in the residuals using Engle’sLagrange-multiplier ARCH test (Engle 1982; Van Dijk,Franses, and Lucas 1999) and fail to reject the null hypothe-ses of no ARCH (p < .01).

Do Firm Results Drive Stock Returns?

A key question on the firm results side is, Do firm revenuesurprises and firm earnings surprises affect stock returns?As Columns 7 and 8 in Table 7 show, the impact of unex-pected changes to revenue, or top-line performance, onstock returns is positive and significant (.544, p < .05).Similarly, the impact of unanticipated changes to income, orbottom-line performance, on stock returns is positive andsignificant (2.511, p < .01). The size of the estimate is simi-lar to that reported previously—for example, Kormendi and

Lipe (1987), who report a coefficient of 3.38. These twoeffects are consistent with the extensive accounting andfinance literature (e.g., Kothari 2001) that has documentedthe information content of revenues and earnings measures.When an unanticipated change in firm results (e.g., earn-ings) occurs, investors view it as containing information notonly about changes in current-term results but about future-term prospects as well. This information induces stock mar-ket participants to update their expectations about the firm’sdiscounted future cash flows and revise stock priceaccordingly.

Do Firm Actions Drive Stock Returns?

The key question on the firm actions side is, Do firm actionsurprises affect stock returns?

New-to-the-company innovations. In confirmation ofH1a, in general, new-to-the-company innovations have apositive and significant impact (.546, p < .01). This effect isU shaped (see Figure 2) in the level of innovation, with astrong preference for new market entries/Level 5 innova-tions (.981, p < .01) over minor updates/Level 1 innovations(.546, p < .01), lending support to H1b. As for competitivenew-to-the-company innovations, the main effect of suchinnovations on stock returns is not significant. Thus, itappears that competitive innovations have no incrementalinformation content to investors regarding the focal firm,unless they are new to the market.

New-to-the-market innovations. Pioneering innovationshave a positive and significant impact on stock returns(3.304, p < .01), consistent with H2. Importantly, the adventof pioneering innovations dominates all other explanatoryvariables in the models. As such, pioneering innovationsreflect information that affects financial markets’ expecta-tions about the firm’s future financial performance. Like-wise, pioneering innovations of competitors have a signifi-cant and negative impact on stock returns (–.882, p < .05).

Advertising support. As noted previously, we test boththe main and the interaction effects of marketing support.Advertising has positive and significant effects on stockreturns (.045, p < .05). Advertising support for new-to-the-company innovations and pioneering innovations increasesthe stock market returns of these innovations (.055, p < .05,

Product Innovations,Advertising,and Stock Returns / 37

TABLE 6Intercorrelations Among the Variables

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1. Stock return 1.002. UΔRevenue .04 1.003. UΔEarnings .04 .01 1.004. UΔInnovation .02 –.01 .00 1.005. UΔCompetitive innovation .10 .04 –.01 .00 1.006. Pioneering .03 .00 .00 .00 –.02 1.007. Competitive pioneering –.05 .00 .01 .01 –.05 .00 1.008. UΔAdvertising .07 .11 .00 .01 .11 .00 –.01 1.009. UΔCompetitive advertising .06 .08 .00 .01 .13 .00 .06 .25 1.00

10. UΔPromotions –.05 –.03 .00 –.02 –.02 –.01 –.02 .00 .01 1.0011. UΔCompetitive promotions .01 –.09 .03 –.02 –.03 –.01 .03 –.07 –.06 .01 1.0012. UΔCustomer liking .01 –.01 –.01 .00 .01 .01 –.01 –.05 –.02 .01 .02 1.0013. UΔCompetitive liking –.07 .03 –.07 .01 .06 .00 –.01 .04 .08 .00 –.01 .00 1.0014. UΔPerceived quality .02 –.01 –.01 .02 –.01 .00 –.01 –.01 –.03 .01 .01 .46 .00 1.0015. UΔCompetitive quality .02 .00 –.01 .00 .15 .03 –.02 .00 –.02 .00 .03 –.06 .03 –.03 1.0016. UΔCategory size .00 –.03 –.01 –.03 .03 –.03 –.04 –.10 –.03 .00 .10 .01 .01 –.01 .01 1.0017. UΔCategory growth .00 –.03 .00 –.02 .03 –.03 –.04 –.10 –.02 –.01 .10 –.01 .01 –.03 –.03 .92 1.0018. UΔFirm’s share –.03 –.01 .09 –.02 .01 –.08 –.03 –.04 .00 –.01 –.02 .03 –.02 .03 .00 .04 .03 1.0019. UΔConcentration .02 .02 .05 .02 .00 –.01 –.03 .03 .00 .01 .00 .01 –.07 –.03 .00 .03 .07 .03 1.00

Notes: Correlations are presented as Pearson correlation coefficients and are modest.


TABLE 7Drivers of Stock Returns

Pauwels et al.Four-Factor (2004) Variables Focal Model 2:

Model 1 Model 2: Partial Full

Variables Hypothesis Estimate SE Estimate SE Estimate SE

Four FactorsConstant –.010** .035 –.077 .086 .232 .427Rm–Rf .308* .017 .323* .085 .350* .020SMB .041** .019 .048** .024 .059 .039HML .302* .026 .323* .030 .373* .034UMD .051 .036 .038 .038 .046 .041

Firm ResultsUΔRevenue .468** .224 .544** .261UΔEarnings 2.406* .811 2.511* .802UΔRevenue × UΔInnovation 1.115 .678 .118 .077UΔEarnings × UΔInnovation 2.596** 1.133 1.468* .519

Firm ActionsUΔInnovation: Level 1 H1 .473* .140 .546* .146UΔInnovation: Level 2 H1 .231** .113 .432* .137UΔInnovation: Level 3 H1 .202 .125 .375 .272UΔInnovation: Level 4 H1 .157** .078 .335* .103UΔInnovation: Level 5 H1 .654* .223 .981* .420UΔCompetitive innovation .003 .047Pioneering H2 3.304** 1.022Competitive pioneering –.882** .425UΔAdvertising .045** .022UΔCompetitive advertising –.127 .128UΔAdvertising × UΔInnovation H3a .055** .023UΔCompetitive advertising × UΔInnovation .182 .170UΔAdvertising × Pioneering H3b, H3c .812* .329UΔPrice promotions –.002* .000 –.005 .003UΔCompetitive promotions –.004 .004UΔPrice promotions × UΔInnovation H4 –.002* .000 –.002** .000UΔCompetitive promotions × UΔInnovation .017 .111UΔPromotions × Pioneering –.018 .080UΔLiking .001 .001UΔCompetitive liking .019 .016UΔLiking × UΔInnovation H5 .029 .019UΔCompetitive liking × UΔInnovation –.017 .017UΔLiking × Pioneering .191 .190UΔQuality .011 .009UΔCompetitive quality –.014* .004UΔQuality × UΔInnovation H6 .021* .002UΔCompetitive quality × UΔInnovation –.257 .251UΔQuality × Pioneering .158 .111

Category Characteristics UΔSize .018 .212UΔSize × UΔInnovation .220* .052UΔGrowth rate .365 1.181UΔGrowth rate × UΔInnovation .618* .234UΔShare of category –.141 .128UΔShare × UΔInnovation .079 .962UΔConcentration –.432 .420UΔConcentration × UΔ Innovation .129 .121Durbin–Watson statistic for serial correlation 2.072 2.080 2.103

Adjusted R-square .154 .334 .472

*p = .01 (two-sided).**p = .05 (two-sided).Notes: The model also includes the seasonal dummies and the brand- and firm-specific fixed coefficients, which are not displayed in the inter-

est of space. There are 54 brands × 299 weeks in the cross-sectional time-series panel. We obtained the Durbin–Watson test statisticusing EViews 6 (see Johnston and DiNardo 1997, Chap. 6.6.1).


FIGURE 2Stock Returns and New-to-the-Company

Innovations

and .812, p < .01), in support of H3a and H3b. In otherwords, advertising support for new products (through theinteraction effect) has a positive stock return impact beyondgeneral-purpose advertising (i.e., the main advertisingeffect). Because advertising and innovation are at the brand(or vehicle model) level, advertising support will draw con-sumer attention to the brand’s innovation to subsequentlydrive customer traffic and new product sales to the dealer.From a practical perspective, most brand advertising at thetime of a new product launch in the auto industry tends to focus on the innovation itself (e.g., the 1999 HondaOdyssey’s fold-flat rear seat, the Lexus RX’s smooth drive).Overall, our results suggest that the innovation effects areenhanced by advertising support because investors lookbeyond the advertising expense (which reduces immediateprofits) and reward the signal of product support that thebrand provides by advertising. Advertising support for pio-neering innovations appears to be especially effective, inline with H3c. Marketing communication works best whenmanagers have something truly new to offer to and commu-nicate with consumers.

Price promotions. For price promotions, we find that themain effect of these incentives on stock returns is not sig-nificant. Thus, although promotions are known to be reve-nue and profit enhancing in the short run, investors do notreward them. More important, we find a significant, nega-tive interaction effect (–.002, p < .05) for promotions withinnovations, in support of H4. Thus, although advertisingsupport is interpreted as a sign of strength, price promotionsmay be viewed as a signal that an innovation is weak byinvestors judging the innovation’s impact on future cashflows.

Customer liking and perceived quality. With respect tothe brand’s customer liking and perceived quality, main

12We also examined the robustness of our results with anexpanded model that incorporates the effects of other brandsowned by the same firm. Thus, this model includes two types ofcompetitive variables. First, we control for competition frombrands of the same firm within the same category to account forcannibalization. Second, we consider competition from brands ofcompeting firms in the same category to account for cross-effects.We observe no substantial differences in the results between theexpanded model and the focal model, indicating that our statisticalinference is robust to this issue.

effects are not significant. This is not surprising, becausewhen there is no new product or change in the existingproduct, we would not expect any change in the brand’s lik-ing and quality and, thus, in stock market returns. The effectof new product introductions that enjoy more positivescores on customer liking is in the expected direction (H5);however, it does not reach traditional significance levels.New product introductions that enjoy more positive con-sumer perceptions of quality have systematically higherstock returns (.021, p < .01), in support of H6. Our resultssuggest that improvements in consumer appraisal in termsof perceived quality, particularly for new products, translateinto better investor appraisal of firm performance.

Do Category Characteristics Drive StockReturns?

Category size and category growth rate have significantinteraction effects with product innovations. First, newproduct introductions have a larger stock return impact inlarge than small categories (.220, p < .01). Second, the cate-gory growth rate has a significant, positive influence (.618,p < .01) on stock returns from new product introductions.This finding is consistent with the forward-looking natureof investment behavior; that is, investors reward firms thattarget high-growth-rate categories with new product intro-ductions because they offer the potential of higher sales andfinancial performance. Moreover, the returns from innovat-ing grow as the category grows, and such growth tends to berewarded all the more by investors.12

Robustness Test of Endogeneity

Our central hypothesis is that marketing-mix activity, suchas product innovation and advertising, improves the outlookon cash flows and thus improves stock returns beyond theknown impact of other important variables, such as thefirm’s net operating income. However, an argument couldalso be constructed in favor of the reverse effect; for exam-ple, firms’ innovations and advertising levels are based, inpart, on their observed stock returns. Specifically, marketersmight want to incorporate investor behavior in their actionsbecause there may be a “reverse causality” between market-ing and stock returns (Markovitch, Steckel, and Yeung2005).

Under the reverse-causation scenario, firm actions (e.g.,innovations, advertising levels) are endogenously deter-mined. Therefore, we tested for the presence of endogeneityusing the Hausman–Wu test (Davidson and MacKinnon1993; Gielens and Dekimpe 2001). The procedure is imple-mented as follows for each potentially endogenous variable:


TABLE 8Impact of Firm Actions on Stock Returns

Effect on Stock Returns

New Product IntroductionsLevel 1 .47% .55%Level 2 .23% .43%Level 3 .20% .38%Level 4 .16% .34%Level 5 .65% .98%Pioneering innovations 4.28%Advertising support for new

product introductions .10%Advertising support for

pioneering innovations .91%Promotional support for new

product introductions –.20%Improvement in customer

liking for new product introductions n.s.

Improvement in perceived quality for new product introductions 2.10%

Notes: n.s. = the estimate is not significant. The simulation for theimpact of a pioneering innovation is based on the assump-tion that when a pioneering innovation is introduced, the levelof innovation increases from Level 0 to Level 5.

Focal Model

(Table 7,Column 7)

Pauwels etal. (2004)Variables(Table 7,

Column 5)Impact of …

FIGURE 3Marketing Impact on Stock Returns

Notes: NPI = new product introduction.

In the test equation, we include both the variable and itsinstruments, which are derived as the forecasts from an aux-iliary regression linking the variable to the other controlvariables. A chi-square test on the significance of theseinstruments then constitutes the exogeneity test. None ofthese tests revealed any violation of the assumed exogeneityof the right-hand-side variables (using a significance levelof p < .05), indicating that the model specification is robustto this issue.

Managerial ImplicationsTo better appreciate the managerial meaning of theseresults, we juxtapose the consequences of the variableslargely under managerial control: new product introduc-tions, the pioneering status of the new product introduction,advertising support, promotion support for new productintroductions, and improvements in customer liking andperceived quality of new product introductions. The firsttwo variables are related to innovation characteristics (i.e.,value creation), the next two involve marketing support(i.e., value communication), and the last two involve bothvalue creation and value communication. Therefore, a com-parison of these effects may provide valuable input forresource allocation decisions in the new product process.Specifically, we calculate the stock return impact of (1)introducing a new product by itself, (2) introducing a pio-neering innovation, (3) increasing advertising support for anew product introduction or for a pioneering innovation by$1 million, (4) increasing promotional incentives for a newproduct introduction by $1,000, (5) increasing customer lik-ing for a new product introduction, and (6) increasing theperceived quality for a new product introduction. Of theseeffects, only the first and fourth have been addressed byPauwels and colleagues (2004). Table 8 reports the effectsizes and also highlights the new managerial insights (overPauwels and colleagues’) we obtained by comparingColumns 2 and 3 (for a graphical comparison summary ofmarketing variables’ impact on stock returns, see Figure 3).

First, the stock return impact is U shaped with the inno-vation level, with a preference for new market entries(.98%) over minor updates (.55%). In comparison, Chaney,Devinney, and Winer (1991) find a stock market impact fornew product announcements of approximately .75%. Theseresults support the interpretation that investors look beyondcurrent financial returns and consider spillover innovationbenefits, which may include increased revenues from open-ing up whole new markets and reduced costs from applyingthe innovation technology to different vehicles in the manu-facturer’s fleet (Sherman and Hoffer 1971). While a newproduct introduction generates only modest stock returngains, the gain generated by a pioneering new product ismuch higher at 4.28%. Thus, the impact of introducing apioneering innovation on stock returns is approximatelyseven times higher than that of introducing a minor update.

Second, an incremental outlay of $1 million in advertis-ing support of an innovation generates up to .10% in stockreturns but up to .91% gains for advertising support of apioneering innovation. Note that these gains occur in addi-tion to the direct sales and profit impact of such advertising

support. The reverse is true for promotional support for newproduct introductions and pioneering innovations becausethese are negative (–.20%) in terms of stock return impact.Finally, improvements in perceived quality score by 100points or a 45% improvement relative to the sample averagescore of 221 (see Table 5) results in a stock return impact of


2.10%. In contrast, financial markets do not seem to incre-mentally value improvements in customer liking scores fornew product introductions. The reason for this differencemay reside in the sampling of only current car owners, whologically like the car features (thus the low informationalvalue of customer liking, beyond the informational value ofmeasures such as sales and earnings, to future companycash flows) but may or may not have experienced problems(thus the high informational value of perceived quality tofuture company cash flows, which likely suffer from nega-tive word of mouth and poor retention in the case of nega-tive perceived quality).

ConclusionsThis article investigates the impact of new product introduc-tions and the associated marketing investments on stockreturns. We postulated several hypotheses in this regard thatare centered on the role of the marketing mix in enhancing,accelerating, and stabilizing cash flows for the firm and/orincreasing its residual value. We tested these hypothesesusing stock return response modeling on six years ofweekly automotive data.

First, we conclude that new product introductions havepositive postlaunch effects on stock returns. These effectsare stronger in larger, high-growth categories. In addition,the stock return benefits of pioneering (new-to-the-market)innovations are seven times larger than those of innovationsthat are merely new to the company. This finding contrastswith the reality that managers favor the rollout of frequentincremental innovations over that of fewer, more fundamen-tal innovations. Such incremental innovations are less costlyand risky, which is important in light of the multibillion dol-lar cost of new car platforms (White 2001). Our researchcontrols for these costs empirically by including firm reve-nue and earnings performance as drivers of stock returns.However, we do not claim to have fully captured the finan-cial and time investment of innovation development. Com-panies should compare our reported findings with their

internal data on project costs to help determine the extent towhich they should aim for pioneering innovations.

Second, the marketing of these innovations plays anequally important role. We find that the stock return impactof new product introductions is greater when they arebacked by substantial advertising investments. In otherwords, communicating the differentiated added value toconsumers yields higher firm value effects of innovations,especially for pioneering innovations. In contrast, promo-tional incentives do not increase firm value effects of newproduct introductions, because they may signal an antici-pated weakness in demand for the new product. Third, thestock return impact of new product introductions is higherfor innovations with higher levels of perceived quality.

This study has several limitations that provide worth-while avenues for further research. First, we analyzed onlyone industry, albeit an important one in which product inno-vation, advertising, and consumer incentives are a majorpart of the marketing mix. Therefore, we emphasize that ourfindings on stock drivers pertain to the automobile industry,and a validation of the results in this study to other indus-tries is an important area for further research. Second, wedid not consider specific launch strategy or innovationprocess measures, both of which have been researchedextensively in prior literature. Third, the focus in this studywas on postlaunch effects of innovations, including pioneer-ing innovations on stock market returns. As such, wefocused on product innovations that made it to market butcensored out of the data those that did not make it to mar-ket. Further research using data on the development costs ofinnovations, including those that do not make it to market,would enable a direct assessment of the stock return impactof prelaunch effects of innovation. Fourth, we did not havedata on advertising copy, and thus we leave the issue ofadvertising copy and effectiveness of new product advertis-ing to further research. Finally, we leave the issues of inves-tigating the presence or absence of threshold effects andreciprocal causation of advertising on stock market perfor-mance for further research.

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