The Composition Matters:

Customer Capital, Talent Turnovers and Stock Returns

Winston Wei Dou Yan Ji David Reibstein Wei Wu∗

August 10, 2017

Abstract

The brand recognition and the key talent’s specialized contribution jointly create and

sustain the firm’s customer capital. The brand-based customer capital is immune to the

firm’s liquidity risk; whereas the talent-based customer capital is fragile to liquidity risk, as

the key talent tends to leave the firm taking away a fraction of the talent-based customer

capital especially when the firm is financially stressed. Using granular proprietary customer

survey data, we decompose the firm-level customer capital and construct the brand-talent

ratio (BTR) to empirically capture the relative contribution of the two components. We

document new joint cross-sectional patterns: the firm with lower BTR has higher expected

return, experiences higher talent turnover rate, and adopts more precautionary financial

policies. To explain these findings, we develop an equilibrium asset pricing model featuring

product market search friction and endogenous liquidity risk caused by the inalienable

talent-based customer capital. The firm with lower BTR is riskier since it is more likely

to lose its talent-based customer capital. Moreover, the firm with lower BTR bears higher

operating leverage due to relatively larger compensation to the key talent. We provide

additional empirical tests to support the mechanisms of our model.

Keywords: Customer base; Marketing and finance; Industrial organization and finance;

Inalienable human capital; Liquidity; Uncertainty shocks.

∗W. Dou is an assistant professor of finance at The Wharton School at University of Pennsylvania(wdou@wharton.upenn.edu). Y. Ji is an assistant professor of finance at HKUST (jiy@ust.hk). D. Reibsteinis a professor of marketing at The Wharton School at University of Pennsylvania (reibstein@wharton.upenn.edu).W. Wu is an assistant professor of finance at TAMU Mays (wwu@mays.tamu.edu). We thank Frederico Belo,Stefano Giglio, Francois Gourio, Shiyang Huang, Niket Jindal, Don Keim, Leonid Kogan, Neil Morgan, ChristianOpp, Adriano Rampini, Alp Simsek, Sheridan Titman, Yu Xu and other seminar participants at University ofHong Kong and AMA MMWS Conference for their comments and kind help. We appreciate Ed Lebar for hiskind support and guidance on the brand value data. We also appreciate John Gerzema, Anna Blender, and DamiRosanvo of BAV consulting for sharing the BAV data. Particularly, we also thank Alina Sorescu for her kindguidance on data processing. Winston Dou is especially grateful for the generous financial support of Rodney LWhite Center for Financial Research.

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1 Introduction

“I believe very deeply the purpose of any business is to create a customer and then try to serve thatcustomer better than anyone else. No customer, no sales, you know, no sales, no profits.”

A.G. Lafley, CEO of Procter & Gamble

Creating and sustaining a brand is essential work and is inextricably linked to a firm’sbusiness, profitability, and thus its valuation.1 Intangible assets representing over 80% ofFortune 500 companies. It has been long recognized that the intangible asset is largely in theform of customer capital sustained by key talent’s contributions and firm’s pure brand value.2

The key talent can contribute to a firm’s customer capital. For example, managers oftenbring in business from clients that they are well connected. The R&D teams can generateproducts with innovative features and thus attract new customers. The part of customer capitalcreated and sustained by key talent’s contributions is referred to as talent-based customercapital. The residual component in customer capital is referred to as the brand-based customercapital. The pure brand recognition also affects customers’ purchase decisions. Firms recognizethe power of pure brand image, and they actively promote brand image by taking measuressuch as increasing advertisement expenses, launching marketing campaigns, maintaining salesinfrastructures and distribution channels, and improving customer services and public relations.

The brand-based customer capital and talent-based customer capital jointly decide the firmvalue. United Airline’s passenger dragging incident is a recent example to illustrate this point.On April 9, 2017, O’Hare International Airport police forcibly removed a passenger from UnitedExpress Flight 3411. Making the situation worse, United CEO Oscar Munoz on April 10th, inan email to employees, claimed that the passenger was “disruptive and belligerent”. As shownby Figure 1A, the stock price of United dropped by more than 3% on April. 11th when thedragging incident and Oscar Munoz’s comments went viral on social media. Indisputably, thedrop of stock prices is a result of customer capital damage.

The brand-based customer capital and talent-based customer capital, however, have dis-tinctive risk and liquidity exposures to uncertainty shocks. Our paper provides a theoreticalframework to analyze how the composition of customer capital affects the joint dynamics offirm financial policies, key talent turnovers, and asset prices. Based on proprietary customersurvey data, we document new robust empirical patterns that strongly support the theoreticalpredictions. Specifically, firms of different brand-to-talent ratios in their customer capital are

1The risk of losing customers were rated as the top one risk of business according to Lloyd’s 2011 risk indexreport and the top two risk according to Llyod’s 2013 risk index report (https://www.lloyds.com).

2Simon and Sullivan (1993) estimates that brand value accounts for over 30% of the firm’s replacement valueand over 50% of the value of intangible asset for firms from a wide range of manufacturing industries. Recentestimates from Gerzema and Lebar (2008) and Vitorino (2014) confirm the important role of brand capital. For theimportance of key talents in asset pricing, see the discussions and citations of Eisfeldt and Papanikolaou (2013).

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affected differently by the liquidity risk and thus have heterogeneous exposures to uncertaintyshocks. Figure 1B shows the cumulative abnormal returns for the long-short portfolio of thebrand-to-talent ratios around the Great Recession featuring heightened uncertainty. We findthat firms with lower brand-talent ratios have lower abnormal returns during the heighteneduncertainty period, while this pattern reverses when the economic uncertainty declines and theeconomy recovers.

In our model, key talent’s contributions and pure brand’s recognition create and sustain thefirm’s customer base, generating consumer demand for the firm’s products. Upon receivingdemand orders, firms employ production capital from a competitive rental market to match.The net income cash flows from demand allows firms to make payout to shareholders. Firms’operations and investments depend on inside liquid funds, and it’s costly for firms to raiseoutside funding. The need for liquidity and imperfect financial market lead to precautionarysaving motive of holding liquidity cushion. As a result, firms manage their liquid funds suchas cash holdings optimally. We assume that cash holding is costly. Then, firms will choose todistribute payout to shareholders, when the cash holding is excessive. When the cash holdingis low, the firm exposes to larger endogenous liquidity risk.

Although the brand-based customer capital and the talent-based customer capital bothcontribute to customer base, they have very different exposure to firms’ liquidity risk. Thebrand-based customer capital is immune to firm’s liquidity risk, whereas the talent-basedcustomer capital is fragile to liquidity risk as key talents tend to leave the firms taking the talent-based customer capital away and causing ex-ante inefficient loss for the firm when the firmis financially stressed. To retain the talent-based customer capital, firms need to compensatethe key talent. The compensation imposes an operating leverage to the firm. The differentialresponses to changes in liquidity risks lead to heterogeneous responses to uncertainty shocks.This is because the uncertainty shock increases firms’ liquidity risk, especially when liquiditycondition is already poor. Effectively, the brand-based customer capital provides a hedge to theheightened uncertainty relative to talent-based customer capital, and therefore requires lowerexpected returns in the cross section.

As a key mechanism, the distinguishing feature of talent-based customer capital is theembedded operating leverage. In our model, key talents have access to an outside option whosevalue determines the division of firm’s surplus between shareholders and key talents. Theoperating leverage embedded in talent-based customer capital implies that firms with lowerbrand-talent ratio are riskier and more exposed to liquidity risks. Specifically, when the firmis liquidity constrained, keeping key talents with the firm is costly for shareholders since themarginal value of cash is high. As a result, the firm has the incentive to ask key talents to leavein order to maintain liquidity and reduce risk exposures, even the turnover is ex-ante inefficient.By contrast, when the firm is in good liquidity condition, shareholders would honor key talents’

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compensation, because it’s profitable to do so for shareholders. In other words, the decision tokeep key talents crucially depends on the firm’s liquidity condition. Furthermore, firms withlower brand-talent ratio are more likely to lose the key talent and incur an efficiency loss.

Our model offers empirically testable joint predictions on stock returns, CEO compensationand turnovers, as well as financial decisions. First, our model implies that firms with lowerbrand-talent ratio face higher costs of capital due to the high marginal value of liquidity. As aresult, portfolios of firms with lower brand-talent ratio have higher expected returns. Second,the model predicts that firms with lower brand-talent ratio tend to have higher exposure touncertainty shocks. An increase in uncertainty increases the firm’s exposure to liquidity risks.Firms with lower brand-talent ratio are hurt more because they are more likely to be financiallyconstrained due to higher operating leverage. Third, firms with lower brand-talent ratio tendto pay higher compensation to key talents and have higher turnover rates of key talents dueto failed renegotiation of labor contracts. Forth, our model implies that firms with lowerbrand-talent ratio are more precautionary in their financial decisions. In particular, firms withlower brand-talent ratio tend to hold more cash, issue more equity, and pay out less.

We test the predictions of our model using a proprietary customer survey database, BrandAsset Valuator (BAV), which is the world’s most comprehensive database on brand perception.The two brand metrics we use in our paper are the brand Stature and brand Strength. BrandStature measures brand loyalty and quality perception as of today. Brand Strength measureshow different and unique are brands compared to their competitors. We construct two measuresfor the brand-talent ratio based on the BAV data. Our main measure for the brand-talent ratiois the the ratio between brand Stature and brand Strength (denoted as BTR). We argue that themaintenance of brand Strength relies more on firms’ key talents compared to brand Stature, asdifferentiating products from competitors require significant inputs of human capitals. Thus forfirms whose brand value is mainly contributed by brand Strength rather than brand Stature,they have larger exposure to key talents’ turnover risks. Consistent with this argument, wefind firms with smaller BTR engaged more in R&D activities (Figure 1D). One main advantageof our BTR measure is that it is constructed solely based on customer survey data. Unlikebrand metrics that are derived from firms’ finance and accounting data, BTR is unlikely to bemechanically correlated with the outcome variables we study in the paper.

To construct our second measure of the brand-talent ratio, we rely on both the BAV dataand the Compustat data. In order to capture the intensity of talent-based customer base, wefirst proxy the talent capital by cumulating the deflated value of the SG&A, which is in thesame spirit of Eisfeldt and Papanikolaou (2013). We then compute the intensity of talent-basedcustomer base by normalizing the talent capital by the sales of the firms. The ratio between thebrand Stature score and the normalized talent capital is thus our second measure of brand-talentratio (denoted as BTR). As shown by Figure 1C, our two measures of the brand-talent ratio are

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highly correlated.We perform a series empirical tests based on the two measures of the brand-talent ratio. The

results provide strong support to our model. We first examine the impact of the brand-talentratio on the cross section of stock returns. We find firms with higher brand-talent ratio areassociated with lower alphas in the Carhart four-factor model (Carhart, 1997) and the Pástor-Stambaugh five-factor Model (Pástor and Stambaugh, 2003), suggesting that firms with higherbrand-talent ratio have lower expect returns after controlling for their risk exposure to typicalrisk factors. We then compare the uncertainty exposure across firms with different levels ofbrand-talent ratio. We find stock returns of firms with higher brand-talent ratio react lessnegatively to aggregate uncertainty shocks measured by the changes of the VXO index (Bloom,2009) and political uncertainty index (Baker, Bloom and Davis, 2016). Next, we examine theimpact of brand-talent ratio on firms’ financial policies. Consistent with the predictions ofour model, we find firms with higher brand-talent ratio are less likely to adopt precautionaryfinancial policies. They hold less cash on the balance sheet and convert smaller fraction of netincome into cash holdings. They also issue less amount of equity and have larger amount ofpayout. We then test the role of brand-talent ratio on executive compensation and CEO turnover.We find that firms with higher brand-talent ratio pay less to their executives and their CEOs areless likely to leave the firms due to reasons other than retirement. These findings are consistentwith our model which implies that firms with lower brand-talent ratio have to pay a premiumto retain key talents in order to compensate their labor income risk as their key talents aremore likely to take outside options in response to the aggregate uncertainty shocks. Finally,we perform heterogeneity analysis to further study the relation between the brand-talent ratioand CEO turnovers. We find that the negative relation between the brand-talent ratio and CEOturnovers are more pronounced in financially constrained firms and also in time periods withheightened uncertainty.

In contrast to brand metrics that derived from firms’ accounting and finance performance,our customer survey-based brand Stature and Strength measures directly reflect the brandperception of customers and they are unlikely to be mechanically related to the outcomevariables we study. The limitation of our measures is that although the BAV sample is theworld’s most comprehensive data on brand perception, it covers a small fraction of publiclytraded US firms.3 To overcome this limitation, we adopt a mimicking portfolio approach andextend our analysis to the entire CRSP-Compustat universe. We first construct a mimickingportfolio for the brand-talent ratio by projecting the returns of the long-short portfolio of thebrand-talent ratio onto the space of excessed returns. We then estimate the mimicking portfoliobeta for all the stocks in the CRSP-Compustat universe. The mimicking portfolio beta is thusthe proxy for the brand-talent ratio in the extended sample. We repeat our empirical analysis

3We identify 545 unique public firms in the time periods from 1993 to 2016.

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based on the mimicking portfolio beta, we find that our empirical findings are robust in theCRSP-Compustat universe.

2 Basic Model

In this section, we develop an industry equilibrium model of heterogenous firms with financialfrictions to understand the role of customer base in determining firms’ stock returns. Firmsoperate in product markets with imperfect competition, and thus the customer base is a valuableintangible asset affecting the demand for the firm’s products. As a particular type of intangibleassets, the scale of customer base shapes the size of firm’s tangible assets. For instance, a firmwith zero customer base has no need to hold any tangible assets for production.4 The value ofthe customer base is often referred to as brand value of a firm.

There are a continuum of firms indexed by i ∈ Ft. Each firm i’s demand is determined byits customer capital as a form of intangible assets embodied in the customer base. Given thedemand for its products, the firm produces using production capital. The production capital istangible such as machines.

2.1 Demand and Customer Capital

The demand for firm i’s products depends on the firm’s customer capital Ci,t. It contains twocomponents: the brand-based customer capital Bi,t and the talent-based customer capital Ti,t attime t. More precisely,

Ci,t = eξi,t (Bi,t + Ti,t) . (2.1)

where ξi,t characterizes the long-term synergy between the firm and the key talent in marketingthe products. The level of ξi,t tends to be higher when the key talent works longer for the firm.More precisely, the synergy level ξi,t takes two possible values: the high value ξH and the lowvalue ξL with 0 < ξL < ξH. The firm-specific Poisson process Ni,t with constant intensity λ

characterizes the jump to the high level ξH.The talent-based customer capital Ti,t depends on the contribution of key talents, such as

the customer relationships sustained by key talents, the management team’s unique businessstrategies, the marketing team’s unique marketing strategies, and the research team’s innova-tions that improve the quality of products. The brand-based customer capital Bi,t is the residualcomponent that is purely attributed to brand recognition, such as customer base created andsustained by advertisement expenses, customers’ loyalty due to habits and switching costs, and

4Except for holdings companies, which are excluded from our analysis.

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product distinctiveness. The market value of the brand-based customer capital is referred to asbrand equity.

The focus of our analysis is the effect of the brand-talent ratio (BTR). The log BTR ratio isdefined as follows:

mi,t ≡ ln(

Bi,t

Ti,t

). (2.2)

We define the talent intensity to be τi,t ≡eξt Ti,tCi,t

, and thus the log BTR is related to the talent

intensity as τ−1i,t = 1 + e−mi,t in the model.

The firm i receives the demand (intensity) order Di,t for its products at the beginning of theperiod [t, t + dt]:

Di,t = eεi,t Ci,t, (2.3)

where the coefficient εi,t characterizes the medium-term business condition of the firm. Thecoefficient εi,t evolves according to a mean-reverting process:

dεi,t = −κεεi,tdt + σεdZi,t, (2.4)

where the shock Zi,t is the firm-specific demand shockUpon the new key talent joins firm i at time t, the synergy level always starts with the low

value (i.e. ξi,t = ξL); after the inception, the synergy level will randomly jump to the high valueξH and stay there until the divorce of the key talent and the firm. The jump occurs when thePoisson process takes the incremental value dNi,t = 1. Since all firms are identical up to theircurrent brand-talent ratios and efficiency levels, we omit the subscript i for the remainder ofthe discussion.

Accumulation of Customer Capital The firm can spend resources to increase the stock ofbrand-based customer capital. The expenses can be advertisement expenses, costs of upgradedcustomer services, and costs of sales channels promotions, among others. Specifically, theincrease of the brand-based customer capital by At incurs the convex adjustment cost:

Φ(At; Bt) ≡ φ (at) Bt, with at =At

Bt. (2.5)

The convex adjustment cost function φ(a) is assumed to be

φ(a) ≡ a + θaη, with θ > 0 and η > 1. (2.6)

The adjustment cost function follows the spirit of convex physical capital adjustment costs inthe asset pricing literature (e.g. Jermann, 1998; Papanikolaou, 2011). Convexity of the function

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captures the idea that brand capital accumulation takes time as increasing its value rapidlyis more costly given the existing stock of organization capital. The coefficient δB > 0 is theaverage depreciation rate of brand-based customer capital. The depreciation can be interpretedas the fluid of customers to competitors as a result of competitors’ efforts (see, e.g. Hoberg,Phillips and Prabhala, 2014).

We assume that the talent-based customer capital Tt is proportional to brand-based customercapital Bt, since it’s natural that the brand has complementarity on the key talent’s contributions.We denote that

Tt = e−mt Bt, (2.7)

where mt is the log brand-talent ratio.The brand-talent ratio evolves randomly and takes a finite number of values µ ≡ µ1 < · · · <

µn ≡ µ.5 Here, µ > 0 is the lowest possible brand-talent ratio, and µ is the highest possiblebrand-talent ratio. Let Mt be a firm-specific Poisson process with intensity q. The brand-talentratio mt jumps if and only if dMt = 1, and it stays at the current level otherwise. Upon receivingan opportunity to jump, the new mt will be chosen randomly from possible levels amongM ≡ {µj ; j = 1, · · · , n} according to a distribution P ≡ {pj > 0 : j = 1, · · · , n; ∑n

j=1 pj = 1}.Intuitively, within each period [t, t + dt], the firm’s brand-talent ratio leaves the current levelmt with probability qdt, and if it jumps, it will switch to the new level mt+ = µj with theconditional probability pj.

Combining the dynamics of ξt, mt and Bt, we can derive the law of evolution of customercapital Ct. It is summarized in Proposition 1.

Proposition 1. The customer capital Ct evolves as

dCt

Ct= (at − δB)dt +

(eξH−ξt − 1

)dNt − τt

[e−(mt+−mt) − 1

]dMt. (2.8)

2.2 Supply and Production Capital

Upon receiving the demand order Dt, the firm can rent production capital optimally withconstant capital rental rate RK from a continuum of perfectly competitive homogeneous capitalgood firms. In the full model, we shall consider time-varying equilibrium rental rate. In thebasic model, we assume that the production capital supply is infinitely elastic, which willalso be relaxed in the full model. The simplification does not change the main results. Therental production capital is a standard modeling technical in macroeconomic literature (see, e.g.Jorgenson, 1963; Buera and Shin, 2013; Moll, 2014) and in corporate theory literature (see, e.g.Rampini and Viswanathan, 2013).

5The number n can be arbitrarily large and will not alter our theoretical results or complicate the numericalsolution method.

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Each firm has the AK technology

Yt = eα+ξt Kt, (2.9)

where α is constant and ξt is the synergy level of the key talent and the firm. Without loss ofgenerality, we normalize the market value of production capital to be one.6 The productioncapital stock depreciates as follows after the production:

dKt

Kt= −δKdt− νtdWt, (2.10)

where νt is the uncertainty level which follows a continuous-time Markov process with finitestates.

2.3 Firm’s Liquidity

We assume that the firm can hold cash and has access to equity market but not corporate debtmarket. The firm has the option to pay out lump-sum dividend Dt or issue equity Ht to financevarious expenses. Equity financing is costly as the firm incurs financing cost Xt which includesa fixed cost χ(Bt + Tt) proportional to firm size and a variable cost γHt proportional to theamount of issued equity, similar to Bolton, Chen and Wang (2011).

The financial frictions in the capital market are captured costly equity financing. Thesefrictions motivate the firm to keep some net income as cash holdings Wt on its balance sheet.The firm also has the incentive to pay out cash because the cash holding is costly. We assumethere is an efficiency deadweight loss ρ. The existence of fixed equity financing cost and cashholding cost implies that the firm will follow a double barrier strategy when making equityfinancing and pay out decisions.

2.4 Compensation to Key Talents

Firm i’s key talents have the option to leave the existing firm and take away talent-basedcustomer capital Tt to a new firm. After leaving the existing firm, the key talent can starts anew talent-only firm, or work together with shareholders to create new brands.

Particularly, the outside option of starting a new talent-only firm characterizes the reservationvalue for the key talents. The key talent needs to raise optimal initial funding W∗0 = w∗0T0 tosustain its operations and investments. Because the key talent operates the self-owned customer

6In the industry equilibrium of the basic model, the production capital rent and its market price are inde-terminated up to the scale. Thus, we can assume the price of production capital to be one without loss ofgenerality.

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capital, the long-term synergy is always at the be high level ξH. The medium-term businesscondition is reset to be ε = 0. The talent-only firm with financial constrained is essentially anextension of the model in Bolton, Chen and Wang (2011). Similarly, the initial value of thetalent-only firm VT(B0, m0, νt). It is the reservation value of the key talent offered by the outsideoption by assuming that the key talent has no bargaining power. When uncertainty νt is higher,the reservation value is lower. This can alleviate the financial fragility of talent-based customercapital in response to uncertainty shocks, but the heterogeneous impact of uncertainty shockson brand-based customer capital and talent-based customer capital of the firm.

Alternatively, the key talent can work together with investors to create new brand-basedcustomer capital. Given T0 = m0B0, the capacity of brand-based customer base can be managedby the key talent is B0. In equilibrium, the new firm always builds new brand-based customercapital up to the capacity. Further, in equilibrium, the representative agent always chooses tocreate new firms since it is more socially efficient.

At any time t, firm i has to offer key talent a continuation value VT(Bt, mt, νt) in order tokeep the key talent. This requires the firm to compensate key talent with cash flows Γt overinterval dt, given by

0 = ΛtΓtdt + Et

[d(

ΛtVT(Bt, mt, νt))]

. (2.11)

If firm i does not honor payment mt∆, key talent will leave the existing firm. As a result,firm i has to hire a new key talent and rebuild the organization capital from a value emBt, andbrand capital’s productivity form ηL. The firm i can successfully fire or ask the the talent toresign probability f dt over the interval dt.

2.5 Pricing Kernel and Equilibrium

There is a representative agent who consumes all outputs and owns all firms. We specify thestate-price density as

dΛt

Λt= −rdt− ∑

ν′ 6=νt

[eη(νt,ν′) − 1

](dN(νt,ν′)

t − λ(νt,ν′)dt) (2.12)

The risk-free rate r and the market price of risks for the uncertainty shocks η(ν, ν′) are constantand exogenously assumed. We assume η(ν, ν′) < 0 if ν′ > ν. It means that heightened uncer-tainty has adverse effect on the economy and raises the state-price density of the representativeagent. Because the representative agent holds the market portfolio, the price kernel is notaffected by any idiosyncratic shocks. The aggregate first-order shock will be added in the fullmodel.

Proposition 2. If eα > RK + δK, the firm always produces to fulfil all demand Dt = Yt (i.e. no

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rationing). It implies the following relations in equilibrium:

(i) The optimal production capital rented by the firm is Kt = eεt−α(Bt + Tt)

(ii) The markup per unit of consumption goods is ω = 1− e−αRK > 0.

(iii) The user’s cost rate of production capital is RK + δK + νtdWt.

(iv) The incremental net income during dt is

dΠt

eεt(Bt + Tt)=(

eξt ω− e−αδK

)dt− e−ανtdWt (2.13)

We consider a Markovian recursive equilibrium. The state space includes the exogenous firm-level state variables ξt and εt, the exogenous aggregate state variable νt, and the endogenousstate variables mt and wt ≡ Wt

Bt+Tt. The firm’s value can be characterized as V(wt, mt, ξt, εt, νt)Bt.

Thus, the value of the brand-based customer base is

VB(wt, mt, ξt, εt, νt)Bt = V(wt, mt, ξt, εt, νt)Bt −VT(mt, νt)Bt. (2.14)

The equilibrium value function V(w, m, ξ, ε, ν) can be characterized by coupled PDE’s aboutvariables w and ε.

2.6 Quantitative Analyses

Parameter Choice We set parameters to standard values to illustrate the model’s mechanism.A full calibration is still in progress.

Numerical Results We first investigate the implication of BTR on firm value, financing,advertising and firing decisions through the lens of the model.

Panel A of Figure 2 plots the firm’s enterprise value (i.e., the firm’s value net of cashholdings) as a function of cash holdings. We normalize both enterprise value and cash holdingsby the value of intangible asset (i.e., B + M) for comparison purposes. It shows that the highBTR firm have significantly higher enterprise value relative to the low BTR firm. Moreover,both the optimal financing amount (w∗h) and the payout boundary (wh) of the high BTR firmare to the left of those of the low BTR firm (w∗l and wl), suggesting that the high BTR firmendogenously holds less cash on its balance sheet.

The difference in cash holdings can be explained by the difference in the marginal value ofcash. As shown in Panel B, the high BTR firm has lower marginal value of cash relative to thelow BTR firm. This is essentially because a larger fraction of the low BTR firm’s intangible asset

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Table 1: Summary of parameters

Parameters Symbol ValueRisk-free rate r 5%Market price of risk for uncertainty η(νL, νH) 0.3Rate of depreciation for B δB 10%Rate of depreciation for K δK 0%Mean reversion parameter of ε κε 2.1Volatility parameter of ε σε 0.15Brand-talent capital ratio [µ, µ] [−0.4, 0.3]Scale adjustment cost of advertisement θ 0.5Convex adjustment cost of advertisement η 2Long-term synergy {ξL, ξH} {1, 1.2}Rate of advancing to ξH λ 0.3Turnover successful rate f 0.1Uncertainty levels {νL, νH} {15%, 30%}Fixed financing cost χ 15%Variable financing cost γ 15%

is talent capital, which is more exposed to liquidity risks due to operating leverage. Althoughfirms with the same value of intangible asset roughly receive same amount of cash flows fromconsumer demand, the low BTR firm has to pay a larger compensation to key talents. Thisimplies that the low BTR firm is more likely to run out of cash and conduct costly equityfinancing after a sequence of negative cash flow shocks. When the firm is in good liquiditycondition, the operating leverage does not increase liquidity risks much because high cashholdings provide cushions against cash flow shocks. As a result, the marginal value of cash forboth firms is equal to one when w > 0.10. However, when cash holdings are low, the low BTRfirm is more exposed to liquidity risks due to higher operating leverage, significantly increasingits marginal value of liquidity.

Panel C of Figure 2 compares the advertisement expenses for the two firms. When cashholdings are low, both firms do not spend resources on advertisement due to the high cost.The high BTR firm spends roughly 2.8% of cash holdings on advertisement compared to 0.4%advertisement expenses of the low BTR firm. The high BTR firm has significantly higheradvertisement expenditure both because of the low cost of internal funds and the high returns.As discussed in the model section, the return from advertisement is proportional to the firm’sbrand capital.

Panel D of Figure 2 plots the firing decisions made by the two firms. It shows that the highBTR firm always chooses to keep key talents while the low BTR firm always chooses to firekey talents when the firing opportunity arrives. A more interesting case is presented for thefirm with BTR=1, whose firing decision crucially depends on the firm’s liquidity condition. In

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particular, the firm fires the key talent whenever cash ratio is below 0.07. This highlights atradeoff between risks and returns. Keeping the key talent is beneficial to the firm because onaverage talent capital generates positive net cash inflows due to productivity synergy. However,when the firm is financially depressed, the increased exposure to liquidity risks outweighs thebenefits from higher average demand, motivating the firm to fire key talents and downsize thescale of production. In this sense, talent capital is riskier relative to brand capital because it ismore likely to be destroyed by liquidity risks.

Figure 3 further illustrates this point by considering how the firing decision varies with cashratio, BTR, and talent productivity. In panel A, we fix talent productivity to be the average value(i.e., ε = 0) to study how the firing decision is influenced by BTR and cash ratio. Consistentwith above discussions, the firing decision is determined by the firm’s liquidity condition.Specifically, firms with low BTR and low cash ratios are more likely to fire key talents. In panelB, we consider a firm with the same value of brand and talent capital to investigate how thefiring decision is influence by talent productivity and cash ratio. It is shown that the firm ismore likely to fire key talents when talent productivity is low because keeping key talentswould generate less net cash inflows.

Figure 4 study the key implication of our model. We consider a negative uncertaintyshock that increases the volatility of depreciation rate from 0.15 to 0.30. We compare thepercentage changes in enterprise value for firms with different BTR. It is shown that all threefirms experience a decrease in enterprise value because larger uncertainty increases liquidityrisks. Importantly, the model implies that low BTR firms are more exposed uncertainty shocks.The exact effect of uncertainty shock also depends on the firm’s liquidity condition. For thefirm with BTR=0.4, the decrease in enterprise value is about 35% when cash ratio is around 2%,and the decrease is about 3% when cash ratio is around 20%. The difference in the change ofenterprise value between the firm with BTR=0.4 and the firm with BTR=2 is as large as 20%when cash ratio is low. Even for firms with good liquidity conditions, the difference is stilleconomically significant, around 2%.

Interestingly, for the low BTR firm, we find that the percentage decrease in enterprise valueis non-monotonic in cash holdings (see the firms with BTR=1 and BTR=0.4). In particular, forthe firm with BTR=0.4, the decrease in enterprise value is about 25% when the cash ratio is closeto zero, and it reaches the minimum 35% when the cash ratio is around 2%. This is becausewhen the cash ratio is close to zero, the firm has a strong tendency to fire key talents. When thefiring opportunity arrives, the low BTR firm is able to successfully downsize its production byshrinking talent capital. As a result, the low BTR firm transforms itself into a high BTR firmbecause brand capital is temporarily unchanged. However, for the firm with highest possibleBTR (i.e., BTR=2), this mechanism is absent because talent capital is already at the minimumvalue MB, as a result, the decrease in enterprise value monotonically increases with the firm’s

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cash ratio.

3 Data

3.1 BAV Data

Our brand value data come from a proprietary brand metrics model, Brand Asset Valuator(BAV), the world’s most comprehensive database on brand perception. This database isdeveloped and managed by BAV consulting, a subsidiary of Young & Rubicam. The sampleconsists of more than 680,000 respondents, and it is constructed to represent the US populationaccording to gender, ethnicity, age, income group, and geographic location. Survey respondentsare asked to complete a 45 minute survey and provide answers to multiple item scales thatyield measures of brand value. Surveys were conducted in 1993, 1997, and 1999 in yearly basisand starting from 2001, the survey has been conducted quarterly. Our BAV sample starts from1993 and ends at 2016.

BAV Consulting uses the brand metrics model to build brand development strategies forclients. This model is well known among both marketing researchers and practitioners.7 TheBAV data have been used by other researchers who have shown that brand value is positivelyrelated to customer lifetime value metrics (Stahl et al., 2012) and firm performance (Mizik andJacobson, 2009), and negatively related to cost of debt (Larkin, 2013) and CEO compensation(Tavassoli, Sorescu and Chandy, 2014). In contrast to brand value measures that are constructedfrom accounting and finance data, the customer survey-based brand metrics are unlikely to bemechanically driven by finance outcome variables such as stock returns and firms’ financialpolicies.

The two main brand metrics we use in our paper are the brand Stature and the brandStrength. Brand Stature measures brand loyalty and quality perception. Stature is a product ofKnowledge (how well consumers know the brand) and Esteem (how much regard and loyaltyconsumers have towards the brand). Brand Strength measures how relevant is the brandto customers and whether this brand is different and unique compared to its competitorsaccording to the perception of consumers. Strength is a product of Relevance (how relevant abrand is to consumers) and Di f f erentiation (how different and unique is a brand compared toits competitors). Brand Stature and brand Strength both capture positive customer perceptionof the brands. However, they have different implications regarding the value of the brands.Brand Stature predicts whether a brand has large customer base as of today. On the otherhand, brand Strength measures whether a brand can grow its customer base in the f uture by

7The BAV model has been incorporated into major marketing textbooks (see, for example, Aaker, 1991; Keller,2008).

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differentiating itself from its competitors.

3.2 Merging BAV Data with Compustat

The BAV data are constructed at the brand level. We manually create a link table to merge themwith the Compustat-CRSP data. Specifically, for each brand-quarter observation in the BAVdata, we identify the company that owns the brand at the given quarter. We then average thebrand Stature and brand Strength scores across all the brands in a given year and assign theaverage scores to the firm. We are able to identify 545 unique firms in the Compustat-CRSPmerged data as the owners of the brands in the BAV sample. These firms own 3245 uniquebrands that are covered by the BAV survey. In a given year, on average 58% of firms own onebrand in the BAV sample; 16% of firms own two brands; 7% of firms own three brands; 4%firms own four brands; and 15% of firms own five brands or more in the BAV sample.

We examine the distribution of the BAV sample by Fama-French 12 industry. AppendixTable A2 summarizes the results. Compared to Compustat-CRSP data, BAV sample containsmore observations from the consumer non-durables and retail sectors. This pattern is notsurprising since most of the firms in these sectors are business-to-consumer firms. Financialservices and utilities are underrepresented in the BAV sample. However, this sampling biasdoes not affect us as we follow the literature and omit financial firms (SIC classification between6000 and 6999) and utility firms (SIC classification between 4000 and 4999) in our empiricalanalysis. The distribution of the remaining segments in the BAV data is comparable to theCompustat-CRSP universe. In summary, although the coverage of the BAV sample is somewhatbiased, the data covers a wide range of industries and represents all the major sectors of theeconomy.

3.3 Construction of the Brand-Talent Ratio

Our main measure of the brand-talent ratio (denoted as BTR) comes from the BAV data. We usethe ratio between the brand Stature score and the brand Strength score to capture the relativecontribution between the pure brand-based customer capital and the key talent-based customercapital in firms’ customer base.

BTRit =Statureit

Strengthit. (3.1)

We argue that the maintenance of brand Strength relies more on the firms’ key talentscompared to brand Stature. In order to build up the brand Strength, firms need to differentiatetheir brands from competitors, which relies critically on the human capital of key talents. Forfirms whose brand value is mainly contributed by brand Strength, their risk exposure to key

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talent turnovers is large. For example, a young tech firm that is current developing an innovativeproduct has large exposure to the turnover risks of their R&D team. On the other hand, forfirms whose brand value is mainly contributed by brand Stature, they do not rely as muchon the human capital of key talents because these firms have established leadership positionsin the product market and no longer actively engage in innovation activities. Therefore, theratio between brand Stature and brand Strength (i.e., BTR) captures the relative contributionbetween pure brand based customer capital and key talent-based customer capital. Consistentwith this argument, we find firms with lower BTR value are firms who have engaged in moreR&D activities and hence rely more on their key talents. In Figure 1D, we plot the naturallog of the BTR (lnBTR) against the lagged R&D expenses (normalized by asset). In order tovisualize the data, we create 100 bins for lnBTR and then compute the average value of thelagged R&D within each bin. We plot the binned data in Figure 1D. We also show the fittedlinear line based on the binned data and the 95% confidence interval for the fitted value. Forthe plot, we can see that lnBTR is negatively correlated with R&D activities. We emphasize thatthe BTR measure is constructed solely based on BAV’s customer survey data. Therefore it isunlikely to be mechanically related to the outcome variables we study in the paper.

We also construct an alternative brand-talent ratio (denoted as BTR) by measuring thetalent capital using the Compustat data. We measure the stock of talent capital (Tt) using theperpetual inventory method.8 Specifically, we compute the stock of talent capital by cumulatingthe deflated value of the adjusted SG&A. In Compustat, SG&A includes the following mainitems: 1) advertising and promotion expenses, 2) administrative labor expenses (includingsalary, pension, retirement, profit sharing, provision for bonus and stock options, employeeinsurance, and other employee benefits when reported below a gross profit figure),9 3) R&Dexpenses and 4) foreign currency adjustments.10 Some but not all of these items are related tokey talents. Administrative labor expenses are compensation to key talents, and they should becounted towards talent capital. Moreover, R&D expenditures lead to technology innovationsand their value largely depends on the key talents. Thus R&D expenses increase human capitalof key talents and should be also counted towards talent capital. We take out advertisementexpenditure from the SG&A because advertisement expenditure increases customer base mainlythrough brand recognition. We also take out the foreign exchange income (loss) ( f ca) in theSG&A as it is unrelated to talent capital. Thus, the amount of talent capital is computed asfollowing:

8The perpetual inventory method is in the same spirit as the method used by Eisfeldt and Papanikolaou (2013)to compute organization capital.

9Production labor expenses are included in the costs of goods sold.10For financial firms, SG&A in Compustat also includes commissions. Non-financial firms, on the other hand,

do not pay commission expenses. Since our sample excludes financial firms, we do not adjust for the commissions.

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Tt = (1− δ)Tt−1 +(xsgat − xadt − f cat)

cpit. (3.2)

Here cpit denotes the consumer price index, and δ is the depreciation rate of the talentcapital. The initial stock of the talent capital is:

T0 =(xsga1 − xad1 − f ca1)

g + δ. (3.3)

We use a depreciation rate of 25% in our analysis.11 We choose g to match the average realgrowth rate of firm-level SG&A expenditures, which in our sample equals 10%. We set missingvalues in the advertisement expenditure and the foreign exchange income (loss) as zero. Insome rare cases, the adjusted SG&A is negative. We set the adjusted SG&A to zero in thissituation. We normalize talent capital by sales. Our second measure for the brand-talent ratio(BTR) is the ratio between the brand Stature and the normalized talent capital.

BTRit =Statureit

(Tit/Salesit)=

Statureit ∗ Salesit

Tit. (3.4)

Since the distribution of BTR and BTR shows right heavy tail, we take natural log of theratios (denoted as lnBTR and lnBTR). lnBTR and lnBTR are highly correlated with each other.The correlation coefficient between lnBTR and lnBTR is 0.51. In order to visualize the data,we create 100 bins for lnBTR and then compute the average value of the lnBTR within eachbin. We plot the binned data in Figure 1C. We also show the fitted linear line based on thebinned data and the 95% confidence interval for the fitted value. For the plot, we can see thetwo measures are positively correlated with each other.

The summary statistics of the natural log of the brand-talent ratio (lnBTR and lnBTR) andother variables used in our paper are shown in Table 2. The definition of all the variables islisted in Table A1.

4 Empirical Results

4.1 Portfolio Sorting Analysis on the Brand-Talent Ratio

Our model predicts that firms with low brand-talent ratio have higher costs of capital. Wetest this prediction by examining portfolio returns sorted on the brand-talent ratio. In June ofyear t, we sort firms into five quintiles based on firms’ brand-talent ratio in year t-1. Once theportfolios are formed, their monthly returns are tracked from July of year t to June of year t+1.

11Our results are not sensitive to the choice of the depreciation rate. Other choices of the depreciation rate, suchas 15% and 40%, yield qualitatively similar results.

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We compute the value-weighted portfolio returns and estimate their alphas and betas usingboth the Carhart four-factor model (Carhart, 1997) and the Pástor-Stambaugh five-factor model(Pástor and Stambaugh, 2003). The sample period is July 1994 to December 2016, since the BAVsample starts from 1993.

We measure the brand-talent ratio by two measures (BTR and BTR). Panel A and Panel Bof Table 3 present the sorting results for the two measures respectively. As shown by PanelA, Quintile 5 (high) BTR portfolio is associated with 9.06% annualized excess return, which islower than the excess return of the Quintile 1 (low) BTR portfolio. Since high BTR firms mayhave different levels of risk exposure to the market returns, SMB, HML, MOM, and the marketliquidity factor, we use the Carhart four-factor model and the Pástor-Stambaugh five-factormodel to estimate the alphas of the long-short (long Q5/short Q1) portfolio. We find thatthe long-short portfolios have significantly negative alphas in both models. The annualizedalphas are around -7.5% in both models. High BTR portfolio has significantly larger HML betacompared to low BTR portfolio, suggesting that value firms on average have higher BTR ratio.The portfolio return analysis using our second measure of brand-talent ratio, BTR, deliversvery similar results. The abnormal returns of the long-short BTR portfolio are significantlynegative in both the Carhart four-factor model and the Pástor-Stambaugh five-factor model.In summary, the sorting results show that firms with smaller brand-talent ratio are associatedwith higher costs of capital after controlling for the exposure to the conventional risk factors,which is consistent with the prediction of our model.

4.2 Exposure to Aggregate Uncertainty Shocks

Why do low brand-talent ratio firms have higher costs of capital? In our model, one keyexplanation is that the key talents in the low brand-talent ratio firms are more likely to demandhigher compensation when aggregate uncertainty shocks hit. In order to retain the key talents,firms with low brand-talent ratio have to costly refinance, which increases their business risks.In other words, our model predicts that low brand-talent ratio firms have a higher level ofuncertainty exposure compared to high brand-talent ratio firms. To test this prediction, we firstestimate the uncertainty exposure of individual firms using the following regression:

retit = ait + bit∆Uncertaintyt + εit, where t ∈ [t− 36, t− 1]. (4.1)

Here, bit is the estimated uncertainty exposure for firm i at time t, and ∆Uncertainty is themonthly change of the uncertainty measure. We use three different uncertainty measures inour analysis. The first measure is the CBOE S&P 100 volatility index. VXO is a commonlyused proxy for macroeconomic uncertainty (Bloom, 2009). VXO data come from CBOE and itis available from 1986 onward. The second and third measures are the two economic policy

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uncertainty indexes (baseline overall index and news based policy uncertainty index) developedby Scott Baker, Nick Bloom, and Steven Davis (Baker, Bloom and Davis, 2016). We use vxobeta,epubetab, epubetan to denote the uncertainty exposure for the three uncertainty measures. InPanel A of Table 4, we show the summary statistics of the uncertainty exposure for firms sortedby BTR. In each calendar month, firms are sorted into quintiles based on BTR in year t− 1 andthe sorting is performed within SIC-2 industries. The mean and median of the uncertaintyexposure are negative for all firm quintiles, which is not surprising as stock returns on averagereact negatively to the increases of macroeconomic and political uncertainty. We find that thelow brand-talent ratio firms have more negative uncertainty exposure, suggesting that thesefirms are more adversely affected by the aggregate uncertainty shocks. The differences in theuncertainty exposure between Q1 and Q5 firms are statistically significant for all three measures(p<0.001 in all three cases).

To formally test the impact of BTR on uncertainty exposure, we run the following panelregressions:

bit = αind + αt + β ln(BTRit) + εit. (4.2)

Here, ln(BTRit) is the natural log of BTR. We include both SIC-2 industry fixed effect andcalendar month fixed effect in the regressions. Notice that we do not include firm fixed effect inthe regressions because the impact of BTR on uncertainty exposure is driven by cross-sectionalvariation rather than within-firm time-series variation in our model. Standard errors areclustered by firm and calendar month. Panel B of Table 4 presents the regression results. Thecoefficients of ln(BTRit) are significantly positive, which means stock returns of high BTRfirms are less likely to react negatively to aggregate uncertainty shocks. The results are alsoeconomically significant, one standard deviation increase in ln(BTRit) leads to an increase ofvxobeta by 0.123 (0.156 * 0.79 ≈ 0.123), which roughly corresponds to 1/5 standard deviation ofvxobeta. Similarly, one standard deviation increase in ln(BTRit) leads to an increase of epubetab

by 0.013 (0.016 * 0.79 ≈ 0.013), which roughly corresponds to 1/7 standard deviation of epubetab.Thus, the results from the panel regressions show that firms with larger BTR suffer less fromaggregate uncertainty shocks, which is the key channel to explain the asset pricing implicationsof the brand-talent ratio in our model.

We repeat our analysis on the relation between the brand-talent ratio and the uncertaintyexposure using the BTR measure. We tabulate the summary statistics of the uncertaintyexposure for BTR quintiles in Panel C, and we show the regression results in Panel D. Both setsof results are very similar to those obtained with the BTR measure.

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4.3 Firms’ Financial Policies

Since firms with larger brand-talent ratio suffer less from aggregate uncertainty shocks, wepredict that these firms are less likely to adopt precautionary financial policies. To test theimpact of the brand-talent ratio on firms’ financial policies, we run the following regressions:

yit = αind + αt + β ln(BTRit) + γ′Controlsit + εit. (4.3)

Here, the outcome variables yit are the amount of cash holding normalized by laggedasset, the change of cash holdings normalized by contemporaneous net income, the amountof equity issuance normalized by lagged asset, the amount of total payout normalized bylagged asset, the amount of dividend issuance normalized by lagged asset, and the amountof share repurchases normalized by lagged asset. The outcome variables are winsorized atthe 1st and 99th percentiles of their empirical distributions to mitigate the effect of outliers.The main independent variable is the natural log of the lagged brand-talent ratio. Controlvariables include lagged firm characteristics such as the natural log of the organization capitalto asset ratio ln(OC/Asset), the natural log of firm market capitalization (lnsize), the naturelog of the book-to-market ratio (lnBEME) and the natural log of the debt to equity ratio (lnlev).We include SIC-2 industry fixed effects in the regressions and fiscal year fixed effects in theregressions. Standard errors are clustered by firm and fiscal year.

Panel A of Table 5 shows the regression results. We find high brand-talent ratio firms holdless cash on their balance sheet. They also convert less fraction of net income to cash. Onestandard deviation increase in ln(BTRit) leads to a 3.063 (3.782 * 0.81 ≈ 3.063) percentagepoints decrease (roughly 1/6 standard deviation) in normalized cash holdings, and a 12.847(15.861 * 0.81 ≈ 12.847) percentage points decrease (roughly 1/14 standard deviation) in thecash saving rate (∆Cash/NI). High BTR firms also issue less equity and they pay out more.One standard deviation increase in ln(BTRit) leads to a 0.611 (0.754 * 0.81 ≈ 0.611) percentagepoints decrease (roughly 1/9 standard deviation) in equity issuance and a 1.055 (1.303 * 0.81 ≈0.611) percentage points increase (roughly 1/7 standard deviation) in total payout. We also testthe relation between the brand-talent ratio and financial policies using the BTR measure. Wepresent the regression results in Panel B of Table 5. The results are consistent with the findingsobtain by the BTR measure. Taken together, we find that firms with larger brand-talent ratioare less likely to adopt precautionary financial policies, which is consistent with the predictionsof our model.

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4.4 Executive Compensation and CEO Turnovers

Our model predicts that firms with higher brand-talent ratio pay less to key talents becausethe labor income of their key talents are less risky since these firms suffer less from aggregateuncertainty shocks. Our model also predicts that firms with higher brand-talent ratio haveless key talent turnovers due to failed renegotiation of labor contracts because key talents inthese firms are less likely to enter into the states in which the value of the outside optiondominates the continuation value within the firms. To test this prediction, we use Execucompdata and examine the impact of the brand-talent ratio on executive compensation and CEOturnovers. We need to point out that the prediction of our model is about key talents of thefirms, which include important firm employees out of the management team (such as thealgorithm developers in a software company). We focus on executive compensation and CEOturnovers only because of the availability of the compensation and turnover data.

4.4.1 Executive Compensation

In the compensation analysis, we run the following regressions:

lnTDC1it = αind + αt + β ln(BTRit) + γ′Controlsit + εit. (4.4)

The outcome variables are the natural log of the total pay (Execucomp variable tdc1). Themain independent variable is the natural log of the lagged brand-talent ratio. Control variablesinclude lagged firm characteristics such as the natural log of the organization capital to assetratio ln(OC/Asset), the natural log of firm market capitalization (lnsize), the nature log of thebook-to-market ratio (lnBEME), the natural log of the debt to equity ratio (lnlev), the 12-monthstock returns prior to the fiscal year ends (StockRet), the age of the executives (Age), and adummy variable for the gender of the executives (Female). We include fiscal year fixed effects inthe regressions to control for time-series changes of executive compensation that are commonacross executives. We run regression both with and without SIC-2 industry fixed effects tomake sure our findings are robust to the industry controls. Standard errors are clustered byfirm and fiscal year.

Panel A of Table 6 presents the regression results. We find high BTR firms pay lowercompensation to their CEO and other top executives. The results are not statically significantfor the CEO sample, but they are significant for the non-CEO sample and also in the samplethat contains both CEOs and non-CEOs. Since the predictions of our model is about key talentsof the firms, the combined sample is perhaps more relevant for our analysis as both CEOsand non-CEOs top executives are key talents of the firms. The negative correlation betweenBTR and compensation is also economically significant for the combined sample. According to

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the regression results with industry fixed effects, one standard deviation increase in ln(BTRit)

leads to a decrease of lnTDC1 by 0.053 (0.065 * 0.81 ≈ 0.053) in the combined sample, whichcorresponds to a 5.41% decrease in total pay for the top executives. We repeat our analysisusing the other measure of brand-talent ratio, BTR. We tabulate the results in Panel B of Table6. Again, we find firms with larger brand-talent ratio pay less to their top executives. Theresults are in fact statistically and economically significant in both the CEO sample and thenon-CEO sample.

4.4.2 CEO Turnovers

Next, we test the relation between the brand-talent ratio and key talent turnovers. Our theorypredicts that the key talents in the high BTR firms are less likely to find outside optionsthat dominate the continuation value within the firms upon the aggregate liquidity shocks.Therefore, firms with higher brand-to-talent ratio are associated with lower turnovers ratesof the key talents. To test this prediction, we study the CEO turnovers using the Execucompdata. We focus on CEOs due to data availability. Notice that the key talent turnovers in ourmodel are due to failure of contract renegotiation. Key talents may leave the firms voluntarily ifthey find better outside options, or they may be forced out by the firms if the firms find betterreplacements. Unlike many studies, the distinction between forced turnovers and voluntaryturnovers is not important for us because both types of turnovers can be due to the failedrenegotiation of labor contracts between firms and their key talents. To focus on the turnoversdue to failure of contract renegotiation, we remove retirement and death from our analysis.Death events are obviously unrelated to contract renegotiation. Retirement is largely drivenby key talents’ age and choices of lifestyle. It is usually planned and is unlikely driven by thefailed renegotiation of labor contracts upon liquidity shocks. We run the following regressionto study the relation between BTR and CEO turnover:

D(Non− retirement)it ∗ 100 = αind + αt + β ln(BTRit) + γ′Controlsit + εit. (4.5)

We use two methods to define non-retirement turnovers. The first approach is solelybased on the age of the CEOs. We follow the literature (see, for example, Parrino, 1997;Jenter and Kanaan, 2015) and use age 60 as the cutoff for retirement age.12 We define aCEO turnover as non-retirement turnover if the CEO leaves the firm at age 59 or youngerdue to reasons other than death. The dummy variable for the non-retirement turnover isdenoted as D(Non − retirement1). The second approach uses additional information fromthe Execucomp data. Execucomp classifies the reasons for CEO turnovers into three groups:retirement, unknown, and resignation. We define a CEO turnover as non-retirement turnover if

12Our results are robust to other age cutoffs such as 59 and 61.

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the CEO leaves the firm at age 59 or younger, or if the CEO leaves the firm through a resignationaccording to the Execucomp data. The dummy variable for the non-retirement turnover isdenoted as D(Non− retirement2). The main independent variable is the natural log of thelagged brand-talent ratio (lnBTR in Panel A and lnBTR in Panel B). The control variables andfixed effects are the same as the ones in the compensation regressions, except that we do notinclude age as a control variable since age is used to define the outcome variables. Standarderrors are clustered by both firm and fiscal year.

Column (1) to Column (4) in Panel A of Table 7 shows the regression results for the BTRmeasure. Consistent with the predictions of our model, we find that CEOs in high BTR firmsare less likely to leave firms due to non-retirement turnover. This result is robust to the twodefinitions of the non-retirement turnover, and it is also robust to the inclusion of SIC-2 industryfixed effects. The negative relation between BTR and CEO turnover is economically significant.According to the specification with both SIC-2 industry fixed effect and fiscal year fixed effect,one standard deviation increase in ln(BTRit) leads to a decrease of turnover probability (D(Non-retirement1)*100) by 0.994 percentage point (1.166 * 0.81 ≈ 0.994), which is roughly 1/5 of theaverage non-retirement turnover rate in the data.

As a comparison, we test the relation between BTR and the retirement turnover. We againuse two approaches to define retirement turnovers. In the first approach,we define a CEOturnover as a retirement turnover if the CEO leaves the firm at age 60 or elder (denoted asD(Retirement1)). In the second approach, we define a CEO turnover as a retirement turnoverif the CEO leaves the firm at age 60 or elder, or if the CEO leaves the firm due to retirementaccording to the Execucomp data (denoted as D(Retirement2)). Different from non-retirementCEO turnovers, we find that high BTR firms actually show higher retirement-related CEOturnovers, which may be because high BTR firms tend to be mature firms and their CEOs arelikely older and thus have higher probability of retirement. We repeat our analysis using theBTR measure. We again find CEOs in the firms with higher brand-talent ratio are less likely toleave the firms for non-retirement reasons.

4.4.3 Heterogeneity Analysis for CEO turnovers

Our model predicts that the relation between the brand-talent ratio and key talent turnoversdepends critically on firms’ financial conditions. For firms that are financially constrained,if their customer base is mainly contributed by key talents, they will likely suffer from theaggregate uncertainty shocks as their balance sheets are too weak to keep firms’ key talentsby matching the outside options. On the other hand, when firms have enough cash on theirbalance sheets, they can afford to keep their key talents and hence avoid damage to firms’customer capital. To test this hypothesis, we classify firms into financially constrained firms

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and non-financially constrained firms at yearly basis. Following the literature, we use threeproxies for the financial constraint: HP index (Hadlock and Pierce, 2010), WW index (Whitedand Wu, 2006; Hennessy and Whited, 2007), and Altman Z-score (Altman, 1968). The financiallyconstrained firms are the firms with HP index larger than median values, with WW indexlarger than the median values, or with Altman Z-score lower than the median values.

We perform a split sample analysis and regress the non-retirement CEO turnover on thebrand-talent ratio in both the financially constrained firms and non-financially constrained firms.The regression results are tabulated in Table 8. Consistent with the prediction of our model,we find the coefficients of the brand-talent ratio are significantly negative in the financiallyconstrained firms and not in the non-financially constraint firms. This pattern is robust acrossthe three proxies for financial constraints and across both measures of the brand-talent ratio.

Besides the heterogeneous impact of the brand-talent ratio in the cross section, our modelalso predicts heterogeneity in the time series. Specifically, the model predicts that the associationbetween key talent turnovers and the brand-talent ratio should be stronger in time periodswith heightened economic uncertainty, in which the liquidity constraints are more likely tobe binding. We define 2000, 2001, 2007, and 2008 as the time period with high economicuncertainty and the rest of the years as the time period with low economic uncertainty. We runthe turnover regressions separately in these two different time periods. As shown by Table 9,the coefficients of the brand-talent ratio is both statistically and economically significant in thetime period with high economic uncertainty, while the magnitude of the coefficients becomesmuch smaller and are no longer statistically significant in time periods with low economicuncertainty. These findings are robust across both measures of the brand-talent ratio.

In summary, the empirical evidence suggests that the negative association between thebrand-talent ratio and the CEO turnovers are stronger for firms with financial constraints andin time periods with heighten uncertainty. These findings provide strong support for the keymechanism of our model.

4.4.4 Aggregate CEO Turnover

According to our model, when aggregate uncertainty shocks take place, the aggregate turnoverrate increases. At the same time, firms with lower brand-to-talent ratio underperform in therealized stock returns compared to firms with high brand-to-talent ratio. Thus, we predict apositive correlation between the aggregate CEO turnover rates and the realized returns to thelong-short BTR portfolio. To test this hypothesis, we run the following regressions:

AggTOit = α0 + β0RetLS_BTRit + β1RetLS_BTR

i,t−1 + γ0RetMktit + γ1RetMkt

i,t−1 + AggTOi,t−1 + εit. (4.6)

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The dependent variable is the aggregate non-retirement CEO turnover rate. The mainindependent variables are the contemporaneous and lagged returns of the long-short BTRportfolio. We also control for the contemporaneous and lagged market returns and the laggedaggregate turnover rate. The regression results are tabulated in Table 10. We find the coefficientsβ0 are not statistically different from zero. However, the sum of the two coefficients β0 and β1

is always positive, which is consistent with the prediction of our model. In most cases, the sumis also statistically different from zero, as shown by the F-test results. The finding is robustacross the two measures for the brand-talent ratio and it is also robust to controlling for thecontemporaneous and lagged market returns.

4.5 Mimicking Portfolio Analysis

We rely on BAV customer survey data to construct the brand-talent ratio. Instead of deriving thebrand value from firms’ performance metrics, we measure the brand value from the perspectiveof customers. The advantage of this approach is that it provides a direct measure of brandvalue outside of the CRSP/Compustat data and hence avoids potential mechanical link betweenthe brand-talent ratio and the outcome variables. The drawback of this approach is BAV datacover a limited number of firms (545 unique firms) and they are only available after 1993. Toovercome this drawback, we apply mimicking portfolio technique and construct a mimickingportfolio for the brand-talent ratio. We then estimate the mimicking portfolio beta for all thefirms in the CRSP and Compustat universe. The mimicking portfolio approach extends ouranalysis both in the cross section and in time series, and therefore allows us to examine the roleof brand-talent ratio in a much larger sample.

To estimate the mimicking portfolio beta, we first construct the mimicking portfolio for thebrand-talent ratio by projecting the long-short portfolio returns for the brand-talent ratio (longQuintile 5, short Quintile 1) on the space of excess returns. Specifically, we run the followingregression:

LSt = a + b′[BL, BM, BH, SL, SM, SH, Mom]t + εt. (4.7)

Here, LSt is the equal-weighted long-short portfolio for the brand-talent ratio.13 [BL, BM,BH, SL, SM, SH, Mom] are the excess returns of the six Fama-French benchmark portfolios onsize (Small and Big) and book-to-market (Low, Medium, and High) in excess of the risk-freerate and Mom is the momentum factor. The BTR mimicking portfolio return is given by:

13Since the brand-talent ratio plays a more important role in financially constraint firms in shaping theirexpected returns, we use equal-weighted method rather then the value-weighted method to construct the long-short portfolio because value-weighed method puts less weights to the small firms, which is more likely to befinancially constrained.

25

MPt = b′[BL, BM, BH, SL, SM, SH, Mom]t + εt. (4.8)

We next estimate the mimicking portfolio beta for firm i at time t using the followingregression:

retit = αit + βitMPt + εit, where t ∈ [t− 36, t− 1]. (4.9)

The estimated coefficient βit is the mimicking portfolio beta for firm i at time t. Mimickingportfolio beta is thus our proxy for the brand-talent ratio in the expanded sample. We use twomeasures for the brand-talent ratio in our analysis: BTR and BTR. Thus, we also obtain twosets of corresponding mimicking portfolio beta and they are denoted as βmp and βmp. Table11 shows the summary statistics for the samples with the mimicking portfolio beta. For stockreturn analysis, the expanded data cover all firms in the CRSP data starting from 1929 (werequire three-year data to estimate the mimicking portfolio beta). The expanded sample size isroughly 45 times of the BAV sample. For the analysis of firm policies, the expanded data coverall firms in the Compustat data starting from 1950. The expanded sample size is roughly 30times of the BAV sample. For the analysis of managerial compensation and CEO turnover, theexpanded data cover all managers and CEOs in the Execucomp data starting from 1992. Theexpanded sample size is roughly 6.5 times of the BAV sample.14

We first examine the relation between the mimicking portfolio beta and stock returns. InTable 12, we show the portfolio returns sorted on both βmp and βmp. Similar to the sortingresults based on the brand-talent ratio, firms with higher mimicking portfolio beta (Q5 firms)have lower expected returns compared to firms with low mimicking portfolio beta (Q1 firms).The differences in stock returns are statistically significant after we control the conventionalrisk factors in the Carhart four-factor model and the Pástor-Stambaugh five-factor model.

Next, we study the relation between mimicking portfolio beta and the uncertainty exposure.As shown by Table 13, firms with higher mimicking portfolio beta have significantly less negativeuncertainty beta (measured by vxobeta, epubetab, and epubetan), suggesting that these firmssuffer less from aggregate uncertainty shocks. This pattern is robust in the panel regressions aswell, where we regress uncertainty beta on the quintiles of the mimicking portfolio beta.15

We then examine the relation between mimicking portfolio beta and firms’ financial policies.The regression results are tabulated in Table 14. We find firms with higher mimicking portfolio

14The main expansion comes from the cross section as the time series data are limited by the availability ofExecucomp.

15We use the quintiles of the mimicking portfolio beta as the independent variables in the regressions to mitigatethe errors-in-variable (EIV) problem. The sorting of mimicking portfolio beta is performed at yearly basis based onthe average mimicking portfolio beta of the firms within a given year. We denote the quintiles of the mimickingportfolios as βQ

mp and βQmp.

26

beta are less likely to adopt precautionary financial policies. They hold less cash, issue lessequity, and payout more.

Finally, we investigate the relation between the mimicking portfolio beta and the executivecompensations and CEO turnovers. We present the regression results in Table 15 and 16. Wefind firms with higher mimicking portfolio beta pay less to top executives and their CEOsare less likely to leave the firms due to non-retirement turnovers. We define non-retirementturnovers based on age of the CEOs and also the turnover classification in the Execucomp data.Eisfeldt and Kuhnen (2013) examine the media coverage around CEO turnovers and carefullyclassify the turnovers from 1992 to 2006 into forced-out turnovers, unclassified departures, andplanned retirements.16 We further study the relation between the mimicking portfolio beta andCEO turnovers based on the intersection of our mimicking portfolio sample and their CEOturnover dataset.17 The regression results are tabulated in Table 17. We find that CEOs in firmswith larger mimicking portfolio beta are less likely to leave the firms due to reasons other thanplanned retirements, a result that is consistent with our turnover analysis that relies on age andExecucomp classification to define non-retirement turnovers.

In summary, the role of the mimicking portfolio beta in the extended sample is very similarto the role of the brand-talent ratio in the BAV sample, which provides strong support for ourmodel in the extended sample.

5 Conclusion

In this paper, we provide the first elements of a conceptual framework to theoretically analyzeand empirically test an economic mechanism by which the composition of brand and talentcapital influences firm valuation and asset prices. We argue that firms of different brand-to-talent ratio have distinctive risk and liquidity exposures to uncertainty shocks. As a result, thevariation in firm-level brand-to-talent ratio is informative about the cross-sectional stock returns.Based on proprietary customer survey data, we find empirical evidence strongly supportingwith the predictions of the model.

16Their approach is built on the precedure proposed by Parrino (Parrino, 1997). Jenter and Kanaan (2015) alsouse a similar approach to classify CEO turnovers.

17We have 5149 CEO-year observations for the intersection of our mimicking portfolio sample and the CEOturnover data in Eisfeldt and Kuhnen (2013). Without using the mimicking portfolio approach, we have only 672CEO-year observations for the intersection of the BAV sample and the CEO turnover data in Eisfeldt and Kuhnen(2013). Although the coefficients of the brand-talent ratio have the same signs as the coefficients of the mimickingportfolio beta in Table 17, the coefficients are not statistically significant due to small sample size.

27

Figure 1: Brand-talent Ratio

Note: Panel A shows the stock price reaction around United Airline’s passenger-dragging incident. On April 9, 2017, O’Hare InternationalAirport police forcibly removed passenger David Dao from United Express Flight 3411 after he refused to depart the airplane upon thedemand of management. Video of the incident recorded by passengers went viral on social media, resulting in outrage over the violentincident. United CEO Oscar Munoz stated on April 10: "This is an upsetting event to all of us here at United. I apologize for having tore-accommodate these customers. Our team is moving with a sense of urgency to work with the authorities and conduct our own detailedreview of what happened. We are also reaching out to this passenger to talk directly to him and further address and resolve this situation."Munoz’s use of the word "re-accommodate" received particular attention and ridicule from social media and commentators. Later on April10, in an e-mail to employees, Munoz praised and defended the crew’s actions, while claiming the passenger was "disruptive and belligerent".He stated that this was not a mistake, but "Our employees followed established procedures for dealing with situations like this." This ledto an online petition calling for his resignation. Politicians expressed concern and called for official investigation. U.S. President DonaldTrump criticized United Airlines, calling treatment of their customer "horrible". Investor Warren Buffett, a major investor in airline stocks,said that United made a "terrible mistake," and that public perceptions were influenced by the CEO’s initial reaction. The detailed coverageof this incident can by found on Wikipedia: https://en.wikipedia.org/wiki/United_Express_Flight_3411_incident. Panel B plots thecumulative abnormal returns (CAR) for the long-short portfolio (long Quintile 5 and short Quintile 1) of the brand talent ratios around theGreat Recession. We measure the brand-talent ratio using Stature

Strength (denoted as BTR) and Stature∗SalesTalent Capital (denoted as BTR). The grey shaded area

denotes the Great Recession with heightened economic uncertainty. We follow NBER and define the time period of the Great Recession asfrom Dec. 2007 to Jun. 2009. We compute the abnormal returns using an event study approach with both the Carhart four-factor model andthe Pástor-Stambaugh five-factor model. We estimate the model parameters using monthly returns of the long-short portfolio from Dec. 2003to Nov. 2006. We then compute the cumulative abnormal returns for the time period that starts from Dec. 2006 and ends at July 2010. PanelC plots lnBTR against lnBTR. In order to visualize the data, we create 100 bins for lnBTR and then compute the average value of the lnBTRwithin each bin. We also show the fitted linear line based on the binned data and the 95% confidence interval for the fitted value. PanelC plots lnBTR against lagged R&D (normalized by asset). In order to visualize the data, we create 100 bins for lnBTR and then computethe average value of the lagged R&D within each bin. We also show the fitted linear line based on the binned data and the 95% confidenceinterval for the fitted value.

DateApr. 06 Apr. 07 Apr. 10 Apr. 11 Apr. 12

Sto

ck R

etur

ns (

%)

-4

-2

0

2

4

6

8

10

A. Stock Prices around the UA Passenger-Dragging Incident

United Airline (UA)Delta Airline (DAL)Southwest Airline (LUV)American Airline (AAL)

Year2007 2008 2009 2010

Diff

eren

ce in

CA

R (

%)

-40

-20

0

20

40

60

80

B. CAR around the Great Recession

The Great Recession with Heightened Uncertainty

CAR(High BTR) - CAR(Low BTR), Carhart Four-Factor Model

CAR(High BTR) - CAR(Low BTR), Pastor-Stambaugh Five-Factor Model

CAR(High BTR) - CAR(Low BTR), Carhart Four-Factor Model

CAR(High BTR) - CAR(Low BTR), Pastor-Stambaugh Five-Factor Model

lnBTR-3 -2 -1 0 1 2

lnBTR

-3

-2

-1

0

1

2

3

4

C. Relation between the Two BTR Measures

binned datafitted line95% CI

lnBTR-3 -2 -1 0 1 2

Lagg

ed R

&D

/Ass

ets

(%)

0

2

4

6

8

D. Relation betweeen Lagged R&D Expenses and BTR

binned datafitted line95% CI

28

0 0.04 0.08 0.12 0.16 0.2

cash ratio: w=W/(B+M)

0

0.2

0.4

0.6

0.8

1

U(w

)-w

A. Enterprise value: U(w)-w

BTR=2BTR=0.4

0 0.04 0.08 0.12 0.16 0.2

cash ratio: w=W/(B+M)

0

2

4

6

8

10

U'(w

)

B. Marginal value of cash: U'(w)

BTR=2BTR=0.4

0 0.04 0.08 0.12 0.16 0.2

cash ratio: w=W/(B+M)

0

0.01

0.02

0.03

0.04

A(w

)

C. Advertisement expense: A(w)

BTR=2BTR=0.4

0 0.04 0.08 0.12 0.16 0.2

cash ratio: w=W/(B+M)

0

1

R(w

)

D. Firing decision: R(w)

BTR=2BTR=1BTR=0.4

Note: This figure plots the firm’s enterprise value, marginal value of cash, investment, and firing decisions for firms with BTR=2 (blue solidline) and BTR=0.4 (black dashed line). Both firms’ key talents have the average productivity (i.e., ε = 0) and both firms have high productivityof intangible asset (i.e., η = ηH). In panel A, the dash-dotted lines plot the optimal financing amounts w∗ and payout boundaries w. In panelD, the red dash-dotted line plots the firing decision for a firm with BTR=1.

Figure 2: The firm’s enterprise value, marginal value of cash, investment, and firing decisions(ε = 0).

29

Note: This figure considers how the firm’s firing decision is determined by cash ratio, BTR, and talent productivity. In panel A, we fix thetalent productivity to be the average value (i.e., ε = 0) and plot the firing decision as a function of the firm’s cash ratio and BTR. In panel B,we set BTR=1 and plot the firing decision as a function of the firm’s cash ratio and talent productivity. In the blue areas, the firm optimallyfires key talents when the firing opportunity arrives.

Figure 3: The firm’s firing decision for different cash holdings and organization capital (ε = 0).

0 0.04 0.08 0.12 0.16 0.2

cash ratio: w=W/(B+M)

-40

-30

-20

-10

0

decr

ease

in e

nter

pris

e va

lue

(%)

BTR=2BTR=1BTR=0.4

I consider an uncertainty shock that increases σ from 0.15 to 0.30. The figure plots the percentage change in firm’s enterprise value for a lowBTT firm (BTT=0.4) and a high BTT firm (BTT=2) for ε = 0.

Figure 4: Uncertainty shocks.

30

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32

Table 2: Summary Statistics for the BAV Sample

Panel A: Summary statistics for the sample used in the return analysis. The data sample is the intersectionbetween the BAV brand survey data and the CRSP data (1994 -2016, monthly data). We exclude financialfirms and utility firms from the sample. We explain the definition of the variables in Appendix Table A1.

Variables Mean Median 10% 90% S.D. # of obs.

lnBTR 0.30 0.42 -0.76 1.20 0.79 58,892lnBTR 1.11 1.02 -0.67 2.57 1.85 56,449vxobeta -0.82 -0.70 -1.59 -0.19 0.68 56,529epubetab -0.08 -0.06 -0.47 0.05 0.14 56,550epubetan -0.05 -0.03 -0.14 0.03 0.08 56,550

Panel B: Summary statistics for the sample used in the analysis of firms’ financial policies. The datasample is the intersection between the BAV brand survey data and Compustat Data (1994 -2016, yearlydata). We exclude financial firms and utility firms from the sample. We explain the definition of thevariables in Appendix Table A1.

Variables Mean Median 10% 90% S.D. # of obs.

lnBTR 0.30 0.42 -0.80 1.21 0.81 5,139lnBTR 1.09 1.00 -0.73 2.56 1.85 4,917Cash/Lagged Asset (%) 15.71 10.00 1.53 35.74 17.67 5,012∆Cash/Net Income (%) 15.03 5.43 -71.40 113.67 175.38 4,343∆Equity/Lagged Asset (%) 1.56 0.53 0 2.92 5.74 5,012Payout/Lagged Asset (%) 6.38 4.05 0 17.16 6.93 5,012Dividend/Lagged Asset (%) 1.99 1.09 0 5.52 2.56 5,012Repurchases/Lagged Asset (%) 4.13 1.65 0 13.38 5.42 5,012ln(OC/Asset) -0.55 -0.21 -1.65 0.63 1.64 4,821lnsize 8.61 8.65 6.09 11.14 1.93 5,012lnBEME -1.02 -1.00 -2.08 0.06 0.92 4,809lnlev 0.23 0.19 -0.93 1.38 0.98 4,809

Panel C: Summary statistics for the sample used in the analysis of managerial compensation and turnover.The data sample is the intersection between the BAV brand survey data and the Execucomp data (1994-2016, year-manager data). We exclude financial firms and utility firms from the sample. We explain thedefinition of the variables in Appendix Table A1.

Variables Mean Median 10% 90% S.D. # of obs.

lnTDC1 (CEOs) 8.71 8.90 7.42 9.92 1.48 4,269lnTDC1 (Non-CEOs) 7.73 7.74 6.54 8.92 1.04 19,364lnTDC1 (Both CEOs and Non-CEOs) 7.91 7.92 6.62 9.28 1.19 23,633D(Non-retirement1)*100 (CEOs) 4.99 0 0 0 21.77 4,290D(Non-retirement2)*100 (CEOs) 5.41 0 0 0 22.62 4,290D(Retirement1)*100 (CEOs) 6.06 0 0 0 23.86 4,290D(Retirement2)*100 (CEOs) 6.99 0 0 0 25.51 4,290

33

Table 3: Excess Portfolio Returns Sorted on the Brand-Talent Ratio

This table shows the asset pricing tests for portfolios sorted on the brand-talent ratio. Wemeasure the brand-talent ratio using Stature

Strength (denoted as BTR) in Panel A and using Stature∗SalesTalent Capital

(denoted as BTR) in Panel B. In June of year t, we sort firms into five quintiles based on firms’brand-talent ratio in year t − 1. Once the portfolios are formed, their monthly returns aretracked from July of year t to June of year t + 1. We compute the value-weighted portfolioreturns and report the excess returns of the individual portfolios and the long/short portfolio.We also report the portfolio alphas and betas estimated by the Carhart four-factor model andthe Pástor-Stambaugh five-factor model. Data on SMB, HML, and MOM are from KennethFrench’s website. The liquidity factor is from L’uboš Pástor’s website. The sample period isJuly 1994 to December 2016, since the BAV sample starts from 1993. We exclude financial firmsand utility firms from the sample. We include t-statistics in parentheses. We annualize theexcess returns and the alphas by multiplying by 12. *, **, and *** indicate statistical significanceat the 10%, 5%, and 1% levels.

Panel A: Portfolios sorted on BTR

BTR Portfolios 1 (Low) 2 3 4 5 (High) 5-1

Excess Returns

E[R]-r f (%) 15.05*** 13.59*** 10.13*** 10.43*** 9.06*** -6.00*[3.78] [3.74] [2.99] [3.58] [3.02] [-1.97]

Carhart Four-Factor Model

α (%) 9.22*** 7.01*** 3.54** 4.33*** 1.72 -7.50***[4.32] [3.94] [2.57] [3.51] [1.32] [-2.83]

βmkt 1.00*** 0.99*** 0.96*** 0.84*** 0.87*** -0.13**[23.05] [27.37] [34.43] [33.80] [33.09] [-2.34]

βsmb -0.09 -0.16*** -0.10*** -0.17*** -0.15*** -0.06[-1.58] [-3.56] [-2.75] [-5.46] [-4.49] [-0.93]

βhml -0.32*** -0.01 0.02 0.09*** 0.35*** 0.66***[-5.23] [-0.24] [0.42] [2.70] [9.46] [8.84]

βmom -0.12*** -0.11*** -0.11*** -0.05** -0.00 0.12***[-3.36] [-3.61] [-4.57] [-2.30] [-0.05] [2.68]

R2 0.73 0.78 0.85 0.83 0.83 0.30

Pástor-Stambaugh Five-Factor Model

α (%) 9.22*** 6.50*** 3.56** 4.18*** 1.85 -7.37***[4.28] [3.66] [2.57] [3.37] [1.42] [-2.75]

βmkt 1.00*** 0.97*** 0.96*** 0.84*** 0.88*** -0.12**[22.66] [26.79] [33.87] [33.12] [32.73] [-2.22]

βsmb -0.09 -0.17*** -0.10*** -0.18*** -0.15*** -0.06[-1.58] [-3.72] [-2.74] [-5.50] [-4.44] [-0.90]

βhml -0.32*** -0.01 0.02 0.10*** 0.35*** 0.66***[-5.21] [-0.12] [0.41] [2.75] [9.40] [8.80]

βmom -0.12*** -0.12*** -0.11*** -0.05** 0.00 0.12***[-3.35] [-3.83] [-4.53] [-2.38] [0.02] [2.70]

βps 0.03 10.42** -0.38 2.95 -2.65 -2.68[0.01] [2.55] [-0.12] [1.03] [-0.88] [-0.44]

R2 0.73 0.78 0.85 0.83 0.83 0.30

34

Table 3: Excess Portfolio Returns Sorted on the Brand-Talent Ratio (Continued)

Panel B: Portfolios sorted on BTR

BTR Portfolios 1 (Low) 2 3 4 5 (High) 5-1

Excess Returns

E[R]-r f (%) 12.94*** 10.18*** 12.61*** 10.81*** 9.57*** -3.37[3.55] [3.13] [3.79] [3.40] [2.81] [-1.23]

Carhart Four-Factor Model

α (%) 7.46*** 3.56* 6.43*** 4.80*** 1.91 -5.55**[3.59] [1.91] [4.41] [3.57] [1.43] [-2.14]

βmkt 0.92*** 0.88*** 0.95*** 0.88*** 0.98*** 0.06[21.84] [23.32] [32.05] [32.37] [36.27] [1.14]

βsmb -0.17*** -0.23*** -0.21*** -0.02 -0.10*** 0.06[-3.10] [-4.77] [-5.66] [-0.68] [-2.96] [0.96]

βhml -0.24*** 0.15*** -0.09** -0.02 0.29*** 0.53***[-4.06] [2.80] [-2.08] [-0.62] [7.75] [7.25]

βmom -0.09** -0.01 -0.06** -0.11*** -0.09*** 0.01[-2.59] [-0.27] [-2.58] [-4.59] [-3.74] [0.15]

R2 0.70 0.69 0.82 0.83 0.86 0.17

Pástor-Stambaugh Five-Factor Model

α (%) 7.96*** 3.52* 6.38*** 4.54*** 1.63 -6.33**[3.83] [1.88] [4.35] [3.37] [1.22] [-2.46]

βmkt 0.93*** 0.88*** 0.94*** 0.87*** 0.97*** 0.04[22.02] [22.89] [31.47] [31.70] [35.56] [0.67]

βsmb -0.16*** -0.23*** -0.21*** -0.03 -0.11*** 0.06[-3.01] [-4.76] [-5.66] [-0.76] [-3.06] [0.85]

βhml -0.25*** 0.15*** -0.09** -0.02 0.30*** 0.54***[-4.19] [2.80] [-2.06] [-0.54] [7.87] [7.46]

βmom -0.09** -0.01 -0.07** -0.11*** -0.09*** -0.00[-2.43] [-0.28] [-2.59] [-4.72] [-3.89] [-0.05]

βps -10.19** 0.74 0.92 5.21* 5.70* 15.89***[-2.12] [0.17] [0.27] [1.68] [1.85] [2.67]

R2 0.70 0.69 0.82 0.84 0.86 0.19

35

Table 4: Brand-Talent Ratio and the Uncertainty Exposure of Stock Returns

This table shows the relation between the brand-talent ratio and the uncertainty exposure ofstock returns. We measure the brand-talent ratio using Stature

Strength (denoted as BTR) in Panel A & B

and using Stature∗SalesTalent Capital (denoted as BTR) in Panel C & D. We estimate the uncertainty exposure

(bit) for firm i at time t from the following regression: retit = ait + bit∆Uncertaintyt + εit, wheret ∈ [t− 36, t− 1]. We use three uncertainty measures in our analysis. The first measure is themonthly CBOE S&P 100 volatility index (VXO). The second and third measures are the twoeconomic policy uncertainty indexes (baseline overall index and news based policy uncertaintyindex) developed by Scott Baker, Nick Bloom, and Steven Davis (Baker, Bloom and Davis,2016). bit for the three uncertainty measures are denoted as vxobeta, epubetab, and epubetan,respectively. In Panel A & C, we show the summary statistics of the uncertainty exposure forfirms sorted by the brand-talent ratio. In each calendar month, firms are sorted into quintilesbased on firms’ brand-talent ratio in year t-1. The sorting is performed within SIC-2 industries.In Panel B & D, we show the relation between uncertainty exposure and the brand-talentratio using panel regressions. The dependent variables are the monthly uncertain exposure ofindividual stocks. The independent variables are the natural log of the brand-talent ratio. Weinclude SIC-2 industry fixed effects and fiscal year fixed effects in the regressions. Standarderrors are clustered by firm and calendar month. The sample period for the analysis of thistable is from 1994 to 2016, since the BAV sample starts from 1993. We exclude financial firmsand utility firms from the sample. We include t-statistics in parentheses. *, **, and *** indicatestatistical significance at the 10%, 5%, and 1% levels.

Panel A: Summary statistics of the uncertainty exposure for firm quintiles sorted on BTR

vxobeta epubetab epubetan

BTR Quintiles Mean Median S.D. # obs Mean Median S.D. # obs Mean Median S.D. # obs

Q1 (Low) -0.893 -0.751 0.763 13,183 -0.093 -0.072 0.190 13,183 -0.055 -0.042 0.102 13,183Q2 -0.869 -0.736 0.713 8,936 -0.078 -0.062 0.129 8,936 -0.047 -0.037 0.078 8,936Q3 -0.868 -0.755 0.702 9,329 -0.081 -0.062 0.119 9,329 -0.046 -0.036 0.072 9,329Q4 -0.760 -0.652 0.647 8,904 -0.072 -0.049 0.126 8,904 -0.042 -0.027 0.076 8,904Q5 (High) -0.722 -0.634 0.579 14,156 -0.067 -0.051 0.118 14,156 -0.039 -0.029 0.069 14,156

Panel B: Panel regression results, brand-talent ratio measured by BTR

(1) (2) (3)

vxobeta epubetab epubetan

lnBTR 0.156*** 0.016*** 0.011***[5.034] [3.309] [3.951]

Industry FE Yes Yes YesCalendar Month FE Yes Yes YesObservations 56529 56550 56550R-squared 0.240 0.193 0.221

36

Table 4: Brand-talent Ratio and the Uncertainty Exposure of Stock Returns (Continued)

Panel C: Summary statistics of the uncertainty exposure for firm quintiles sorted on BTR

vxobeta epubetab epubetan

BTR Portfolios Mean Median S.D. # obs Mean Median S.D. # obs Mean Median S.D. # obs

Q1 (Low) -0.940 -0.772 0.786 12,917 -0.096 -0.072 0.149 12,917 -0.057 -0.043 0.088 12,917Q2 -0.891 -0.758 0.759 8,539 -0.080 -0.064 0.132 8,539 -0.047 -0.037 0.079 8,539Q3 -0.810 -0.708 0.672 9,024 -0.072 -0.051 0.131 9,024 -0.042 -0.029 0.079 9,024Q4 -0.736 -0.632 0.608 8,549 -0.072 -0.050 0.119 8,549 -0.042 -0.029 0.071 8,549Q5 (High) -0.734 -0.660 0.567 13,846 -0.072 -0.056 0.115 13,846 -0.042 -0.032 0.068 13,846

Panel D: Panel regression results, brand-talent ratio measured by BTR

(1) (2) (3)

vxobeta epubetab epubetan

lnBTR 0.060*** 0.006*** 0.004***[4.636] [3.137] [3.672]

Industry FE Yes Yes YesCalendar Month FE Yes Yes YesObservations 54907 54907 54907R-squared 0.242 0.233 0.245

37

Table 5: Brand-talent Ratio and Firms’ Financial Policies

This table shows the relation between brand-talent Ratio and firms’ financial policies.brand-talent ratio using Stature

Strength (denoted as BTR) in Panel A and using Stature∗SalesTalent Capital (denoted

as BTR) in Panel B. The dependent variables are the amount of cash holdings (% of laggedasset), the change of cash holdings (% of contemporaneous net income), the amount ofequity issuance (% of lagged asset), the amount of total payout (% of lagged asset), theamount of dividend issuance (% of lagged asset), and the amount of share repurchases(% of lagged asset). The outcome variables are winsorized at the 1st and 99th percentilesof their empirical distributions to mitigate the effect of outliers. The main independentvariable is the natural log of the lagged brand-talent ratio (lnBTR in Panel A and lnBTR inPanel B). Control variables include lagged firm characteristics such as the natural log of theorganization capital to asset ratio ln(OC/Asset), the natural log of firm market capitalization(lnsize), the nature log of the book-to-market ratio (lnBEME), and the natural log of thedebt to equity ratio (lnlev). We include SIC-2 industry fixed effects and fiscal year fixedeffects in the regressions. The sample period for the analysis of this table is from 1994to 2016, since the BAV sample starts from 1993. We exclude financial firms and utilityfirms from the sample. We include t-statistics in parentheses. Standard errors are clusteredby firm and fiscal year. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels.

Panel A: Brand-talent ratio measured by BTR

(1) (2) (3) (4) (5) (6)Casht

Assett−1(%) ∆Casht

NIt(%) ∆Equityt

Assett−1(%) Payoutt

Assett−1(%) Dividendt

Assett−1(%) Repurchasest

Assett−1(%)

lnBTR -3.782*** -15.861*** -0.754** 1.303*** 0.369** 0.893***[-4.469] [-3.870] [-2.313] [4.524] [2.634] [4.189]

ln(OC/Asset) 1.274*** 1.323 0.136** 0.334** 0.118** 0.201**[3.994] [0.633] [2.334] [2.791] [2.213] [2.692]

lnsize -1.396*** 0.601 -0.349*** 0.451*** 0.209** 0.332***[-2.965] [0.312] [-3.135] [3.056] [2.683] [3.265]

lnBEME -7.420*** -1.177 -1.201*** -2.948*** -0.664*** -1.984***[-8.049] [-0.195] [-5.445] [-8.683] [-4.978] [-8.212]

lnlev -5.605*** 5.015 -0.529** -1.812*** -0.207* -1.345***[-8.663] [1.453] [-2.285] [-7.026] [-1.962] [-7.410]

Industry FE Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes YesObservations 4702 4014 4702 4702 4702 4702R-squared 0.411 0.040 0.075 0.342 0.382 0.290

38

Table 5: Brand-talent Ratio and Firms’ Financial Policies (Continued)

Panel B: Brand-talent ratio measured by BTR

(1) (2) (3) (4) (5) (6)Casht

Assett−1(%) ∆Casht

NIt(%) ∆Equityt

Assett−1(%) Payoutt

Assett−1(%) Dividendt

Assett−1(%) Repurchasest

Assett−1(%)

lnBTR -1.530*** -6.702* -0.248 0.821*** 0.138 0.647***[-2.850] [-2.046] [-1.427] [4.170] [1.550] [4.337]

ln(OC/Asset) -0.135 -4.919 -0.102 1.048*** 0.246** 0.756***[-0.242] [-1.675] [-0.708] [5.174] [2.738] [5.072]

lnsize -1.847*** -1.050 -0.451*** 0.571*** 0.255*** 0.403***[-4.142] [-0.583] [-3.680] [4.149] [3.412] [4.244]

lnBEME -8.270*** -4.691 -1.373*** -2.679*** -0.585*** -1.802***[-8.972] [-0.765] [-6.197] [-8.592] [-4.683] [-8.172]

lnlev -6.325*** 2.390 -0.683*** -1.646*** -0.137 -1.251***[-9.805] [0.666] [-3.258] [-6.839] [-1.350] [-7.353]

Industry FE Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes YesObservations 4691 4008 4691 4691 4691 4691R-squared 0.401 0.039 0.070 0.341 0.377 0.293

39

Table 6: Brand-talent Ratio and Executive Compensation

This table shows the relation between the brand-talent ratio and executive compensation.We measure the brand-talent ratio using Stature

Strength (denoted as BTR) in Panel A and usingStature∗SalesTalent Capital (denoted as BTR) in Panel B. The dependent variables are the natural log ofthe total pay (Execucomp variable tdc1). The main independent variable is the naturallog of the lagged brand-talent ratio (lnBTR in Panel A and lnBTR in Panel B). Controlvariables include lagged firm characteristics such as the natural log of the organizationcapital to asset ratio ln(OC/Asset), the natural log of firm market capitalization (lnsize),the nature log of the book-to-market ratio (lnBEME), the natural log of the debt to equityratio (lnlev), the 12-month stock returns prior to the fiscal year ends (StockRet), the ageof the executives (age), and a dummy variable for the gender of the executives (Female).SIC-2 industry fixed effects and fiscal year fixed effects are included in the regressionsas indicated in the table. The sample period for the analysis of this table is from 1994to 2016, since the BAV sample starts from 1993. We exclude financial firms and utilityfirms from the sample. We include t-statistics in parentheses. Standard errors are clusteredby firm and fiscal year. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels.

Panel A: Brand-talent ratio measured by BTR

(1) (2) (3) (4) (5) (6)lnTDC1

Sample CEO CEO Non-CEO Non-CEO Both Both

lnBTR -0.018 -0.020 -0.115*** -0.087** -0.090*** -0.065*[-0.396] [-0.358] [-3.798] [-2.648] [-2.921] [-1.918]

ln(OC/Asset) 0.031 0.021 0.032* 0.047** 0.031* 0.037**[1.589] [1.063] [1.769] [2.699] [1.762] [2.334]

lnsize 0.319*** 0.342*** 0.380*** 0.389*** 0.364*** 0.377***[8.292] [9.626] [21.830] [19.158] [19.698] [17.322]

lnBEME 0.169*** 0.194*** 0.199*** 0.210*** 0.191*** 0.207***[3.929] [3.550] [6.409] [6.219] [6.255] [6.112]

lnlev 0.248*** 0.240*** 0.179*** 0.194*** 0.203*** 0.214***[3.934] [4.004] [4.893] [4.960] [5.190] [5.254]

StockRet 0.031 0.051 0.131*** 0.128*** 0.107*** 0.108***[0.213] [0.378] [4.517] [4.281] [2.838] [3.049]

Age 0.013 0.008 0.007 0.008* 0.018*** 0.019***[1.672] [1.218] [1.489] [1.811] [3.566] [3.918]

Female 0.350*** 0.351** 0.010 0.009 -0.016 -0.017[3.112] [2.741] [0.204] [0.200] [-0.263] [-0.290]

Industry FE No Yes No Yes No YesYear FE Yes Yes Yes Yes Yes YesObservations 3868 3868 12392 12392 16260 16260R-squared 0.160 0.192 0.357 0.373 0.269 0.282

40

Table 6: Brand-talent Ratio and Executive Compensation (Continued)

Panel B: Brand-talent ratio measured by BTR

(1) (2) (3) (4) (5) (6)lnTDC1

Sample CEO CEO Non-CEO Non-CEO Both Both

lnBTR -0.051* -0.090** -0.051*** -0.055** -0.054*** -0.064***[-2.051] [-2.183] [-2.963] [-2.576] [-3.088] [-2.926]

ln(OC/Asset) -0.012 -0.048 -0.017 -0.003 -0.019 -0.016[-0.501] [-1.311] [-0.771] [-0.128] [-0.933] [-0.793]

lnsize 0.313*** 0.351*** 0.366*** 0.382*** 0.352*** 0.375***[8.280] [10.571] [22.440] [20.846] [19.703] [18.636]

lnBEME 0.159*** 0.186*** 0.172*** 0.190*** 0.169*** 0.190***[3.523] [3.375] [5.648] [5.837] [5.557] [5.879]

lnlev 0.246*** 0.239*** 0.157*** 0.178*** 0.186*** 0.203***[3.833] [4.078] [4.336] [4.601] [4.813] [5.065]

StockRet 0.038 0.059 0.142*** 0.136*** 0.117*** 0.116***[0.259] [0.442] [4.505] [4.319] [3.046] [3.220]

Age 0.013* 0.008 0.007 0.008* 0.018*** 0.019***[1.730] [1.326] [1.394] [1.723] [3.549] [3.931]

Female 0.347*** 0.353** 0.018 0.011 -0.009 -0.017[3.120] [2.812] [0.370] [0.252] [-0.146] [-0.281]

Industry FE No Yes No Yes No YesYear FE Yes Yes Yes Yes Yes YesObservations 3839 3839 12299 12299 16138 16138R-squared 0.161 0.194 0.353 0.371 0.269 0.282

41

Table 7: Brand-talent Ratio and CEO Turnovers

This table shows the relation between the brand-talent ratio and CEO turnovers. We measurethe brand-talent ratio using Stature

Strength (denoted as BTR) in Panel A and using Stature∗SalesTalent Capital (denoted

as BTR) in Panel B. In Column (1) and (2), the dependent variable is 100 for a given CEO-yearobservation if the CEO leaves the firm at age 59 or younger due to reasons other than death,and it is 0 otherwise. In Column (3) and (4), the dependent variable is 100 for a given CEO-yearobservation if the CEO leaves the firm at age 59 or younger due to reasons other than death, orif the CEO resigns according to the Execucomp data, and it is 0 otherwise. In Column (5) and(6), the dependent variable is 100 for a given CEO-year observation if the CEO leaves the firmat age 60 or elder due to reasons other than death, and it is 0 otherwise. In Column (7) and (8),the dependent variable is 100 for a given CEO-year observation if the CEO leaves the firm atage 60 or elder due to reasons other than death, or if the CEO leaves the firm due to retirementaccording to the Execucomp data, and it is 0 otherwise. The main independent variable is thenatural log of the lagged brand-talent ratio (lnBTR in Panel A and lnBTR in Panel B). Controlvariables include lagged firm characteristics such as the natural log of the organization capitalto asset ratio ln(OC/Asset), the natural log of firm market capitalization (lnsize), the nature logof the book-to-market ratio (lnBEME), the natural log of the debt to equity ratio (lnlev), the12-month stock returns prior to the fiscal year ends (StockRet), and a dummy variable for thegender of the executives (Female). We do not control for the age of the executivies because ageis used to construct the dependent variables. We include SIC-2 industry fixed effects and fiscalyear fixed effects in the regressions. The sample period for the analysis of this table is from1994 to 2016, since the BAV sample starts from 1993. We exclude financial firms and utilityfirms from the sample. We include t-statistics in parentheses. Standard errors are clusteredby firm and fiscal year. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels.

Panel A: Brand-talent ratio measured by BTR

(1) (2) (3) (4) (5) (6) (7) (8)D(Non-retirement1)*100 D(Non-retirement2)*100 D(Retirement1)*100 D(Retirement2)*100

lnBTR -0.982** -1.166*** -0.936** -1.177*** 0.287 0.437 0.670 0.823[-2.698] [-3.381] [-2.134] [-2.938] [0.673] [0.964] [1.270] [1.413]

ln(OC/Asset) 0.147 0.204 0.185 0.187 0.114 -0.210 0.097 -0.321[0.518] [0.709] [0.742] [0.693] [0.542] [-0.754] [0.408] [-1.068]

lnsize -0.185 0.083 -0.125 0.200 0.318 0.484* 0.418 0.607*[-0.790] [0.252] [-0.559] [0.611] [1.477] [1.794] [1.547] [1.958]

lnBEME 0.563 1.241* 0.824 1.573** 1.013** 1.564*** 1.094** 1.692***[1.089] [1.902] [1.536] [2.426] [2.409] [3.288] [2.804] [3.775]

lnlev 0.290 0.404 0.337 0.480 0.751 0.733 0.929** 0.971*[0.792] [0.786] [1.023] [0.936] [1.652] [1.468] [2.160] [2.039]

StockRet -4.234*** -4.335*** -3.949*** -4.008** -0.818 -0.554 -1.214 -0.931[-3.036] [-2.857] [-3.011] [-2.811] [-0.734] [-0.520] [-0.925] [-0.725]

Female 0.250 -0.780 0.563 -0.545 -3.998*** -4.648*** -4.541*** -5.281***[0.417] [-1.140] [0.717] [-0.707] [-3.032] [-3.747] [-3.293] [-4.248]

Industry FE No Yes No Yes No Yes No YesYear FE Yes Yes Yes Yes Yes Yes Yes YesObservations 3999 3999 3999 3999 3999 3999 3999 3999R-squared 0.013 0.030 0.013 0.033 0.009 0.018 0.013 0.021

42

Table 7: Brand-talent Ratio and CEO Turnovers (Continued)

Panel B: Brand-talent ratio measured by BTR

(1) (2) (3) (4) (5) (6) (7) (8)D(Non-retirement1)*100 D(Non-retirement2)*100 D(Retirement1)*100 D(Retirement2)*100

lnBTR -0.477** -0.464 -0.461* -0.448 -0.023 -0.094 0.309 0.155[-2.078] [-1.605] [-1.784] [-1.455] [-0.102] [-0.251] [0.947] [0.325]

ln(OC/Asset) -0.267 -0.346 -0.216 -0.353 0.129 -0.237 0.387 -0.133[-0.654] [-0.843] [-0.546] [-0.910] [0.553] [-0.749] [1.260] [-0.356]

lnsize -0.285 -0.062 -0.221 0.052 0.359 0.572** 0.491* 0.718**[-1.205] [-0.183] [-1.014] [0.155] [1.715] [2.139] [1.868] [2.387]

lnBEME 0.336 0.959 0.609 1.293* 1.081** 1.695*** 1.250*** 1.900***[0.616] [1.378] [1.065] [1.872] [2.619] [3.790] [3.094] [4.296]

lnlev 0.155 0.209 0.211 0.279 0.951** 1.002** 1.166*** 1.295***[0.404] [0.378] [0.571] [0.501] [2.566] [2.478] [3.457] [3.612]

StockRet -4.106*** -4.183*** -3.822*** -3.855** -0.899 -0.633 -1.328 -1.049[-3.020] [-2.841] [-2.977] [-2.775] [-0.798] [-0.596] [-1.001] [-0.812]

Female 0.258 -0.833 0.567 -0.607 -4.031*** -4.656*** -4.583*** -5.305***[0.426] [-1.109] [0.712] [-0.726] [-3.084] [-3.746] [-3.335] [-4.227]

Industry FE No Yes No Yes No Yes No YesYear FE Yes Yes Yes Yes Yes Yes Yes YesObservations 3968 3968 3968 3968 3968 3968 3968 3968R-squared 0.013 0.031 0.013 0.034 0.009 0.018 0.014 0.021

43

Table 8: Brand-talent Ratio and CEO Turnovers: Role of Financial Constraints

This table shows the relation between the brand-talent ratio and CEO turnovers in firmswith and without financial constraints. We measure the brand-talent ratio using Stature

Strength

(denoted as BTR) in Panel A and using Stature∗SalesTalent Capital (denoted as BTR) in Panel B. We classify

firms into financially constrained firms and non-financially constrained firms based on threefinancial constraint measures: HP index (Hadlock and Pierce, 2010), WW index (Whitedand Wu, 2006; Hennessy and Whited, 2007), and Altman Z-score (Altman, 1968). Theclassification is performed at yearly basis. The financially constrained firms are the firmswith HP index larger than median values, with WW index larger than the median values,or with Altman Z-score lower than the median values. The dependent variable is 100 for agiven CEO-year observation if the CEO leaves the firm at age 59 or younger due to reasonsother than death, and it is 0 otherwise. The main independent variable is the natural log ofthe lagged brand-talent ratio (lnBTR in Panel A and lnBTR in Panel B). Control variablesinclude lagged firm characteristics such as the natural log of the organization capital toasset ratio ln(OC/Asset), the natural log of firm market capitalization (lnsize), the naturelog of the book-to-market ratio (lnBEME), the natural log of the debt to equity ratio (lnlev),the 12-month stock returns prior to the fiscal year ends (StockRet), and a dummy variablefor the gender of the executives (Female). We do not control for the age of the executiviesbecause age is used to construct the dependent variables. We include fiscal year fixedeffects in the regressions. The sample period for the analysis of this table is from 1994to 2016, since the BAV sample starts from 1993. We exclude financial firms and utilityfirms from the sample. We include t-statistics in parentheses. Standard errors are clusteredby firm and fiscal year. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels.

Panel A: Brand-talent ratio measured by BTR

(1) (2) (3) (4) (5) (6)D(Non-retirement1)*100

Constrained Non-constrained Constrained Non-constrained Constrained Non-constrainedSample (High HP) (Low HP) (High WW) (Low WW) (Low Z-score) (High Z-score)

lnBTR -0.992** -0.793 -1.366** -0.512 -1.328** -0.376[-2.643] [-1.147] [-2.608] [-0.959] [-2.254] [-0.744]

ln(OC/Asset) -0.213 0.335* 0.325 0.078 0.163 0.395[-0.419] [1.787] [1.020] [0.241] [0.576] [1.647]

lnsize 0.029 -0.283 0.605 -1.179** -0.475 -0.039[0.080] [-1.083] [1.355] [-2.734] [-1.512] [-0.157]

lnBEME -0.029 1.277* 1.457* -0.463 1.091* -1.492[-0.028] [1.977] [1.739] [-0.832] [1.856] [-1.634]

lnlev 0.160 0.619 0.418 -0.331 0.141 -0.823[0.322] [1.152] [1.115] [-0.634] [0.321] [-1.192]

StockRet -5.402*** -2.344 -3.148 -6.268*** -4.008* -6.058***[-3.012] [-1.396] [-1.713] [-3.733] [-2.044] [-3.777]

Female -1.082 1.683 -1.816 2.279 2.973* -2.959**[-0.845] [1.129] [-0.910] [1.390] [1.724] [-2.177]

Year FE Yes Yes Yes Yes Yes YesObservations 1936 2063 1929 2070 1921 2059R-squared 0.023 0.011 0.022 0.020 0.017 0.025

44

Table 8: Brand-talent Ratio and CEO Turnovers: Role of Financial Constraints (Continued)

Panel B: Brand-talent ratio measured by BTR

(1) (2) (3) (4) (5) (6)D(Non-retirement1)*100

Constrained Non-constrained Constrained Non-constrained Constrained Non-constrainedSample (High HP) (Low HP) (High WW) (Low WW) (Low Z-score) (High Z-score)

lnBTR -0.950*** 0.148 -0.888** -0.127 -0.776* 0.130[-3.723] [0.370] [-2.421] [-0.376] [-2.066] [0.440]

ln(OC/Asset) -0.939 0.406 -0.466 -0.037 -0.503 0.431[-1.520] [0.980] [-0.954] [-0.073] [-0.962] [1.291]

lnsize -0.070 -0.329 0.525 -1.197*** -0.612* -0.075[-0.203] [-1.246] [1.264] [-3.007] [-1.920] [-0.320]

lnBEME -0.375 1.160* 1.209 -0.587 0.872 -1.535[-0.359] [1.817] [1.523] [-1.022] [1.484] [-1.671]

lnlev 0.121 0.497 0.256 -0.391 0.054 -0.846[0.244] [0.905] [0.663] [-0.788] [0.140] [-1.219]

StockRet -5.283*** -2.374 -2.942 -6.374*** -3.978* -5.769***[-3.039] [-1.405] [-1.635] [-3.920] [-1.992] [-3.640]

Female -1.258 1.730 -1.705 2.260 2.913 -2.887**[-1.053] [1.151] [-0.837] [1.376] [1.627] [-2.163]

Year FE Yes Yes Yes Yes Yes YesObservations 1911 2057 1907 2061 1904 2045R-squared 0.025 0.011 0.021 0.020 0.018 0.023

45

Table 9: Brand-talent Ratio and CEO Turnovers: Role of Uncertainty

This table shows the relation between the brand-talent ratio and CEO turnovers in time periodswith high levels of uncertainty and low levels of uncertainty. We measure the brand-talentratio using Stature

Strength (denoted as BTR) in Panel A and using Stature∗SalesTalent Capital (denoted as BTR)

in Panel B. We define 2000, 2001, 2007, and 2008 as the time period with high levels ofuncertainty and the rest years as the time period with low levels of uncertainty. In Column(1) and (2), the dependent variable is 100 for a given CEO-year observation if the CEO leavesthe firm at age 59 or younger due to reasons other than death, and it is 0 otherwise. InColumn (3) and (4), the dependent variable is 100 for a given CEO-year observation if theCEO leaves the firm at age 59 or younger due to reasons other than death, or if the CEOresigns according to the Execucomp data, and it is 0 otherwise. The main independentvariable is the natural log of the lagged brand-talent ratio (lnBTR in Panel A and lnBTR inPanel B). Control variables include lagged firm characteristics such as the natural log of theorganization capital to asset ratio ln(OC/Asset), the natural log of firm market capitalization(lnsize), the nature log of the book-to-market ratio (lnBEME), the natural log of the debt toequity ratio (lnlev), the 12-month stock returns prior to the fiscal year ends (StockRet), and adummy variable for the gender of the executives (Female). We do not control for the age ofthe executivies because age is used to construct the dependent variables. We include fiscalyear fixed effects in the regressions. The sample period for the analysis of this table is from1994 to 2016, since the BAV sample starts from 1993. We exclude financial firms and utilityfirms from the sample. We include t-statistics in parentheses. Standard errors are clusteredby firm and fiscal year. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels.

Panel A: Brand-talent ratio measured by BTR

(1) (2) (3) (4)D(Non-retirement1)*100 D(Non-retirement2)*100

Sample High Uncertainty Low Uncertainty High Uncertainty Low Uncertainty

lnBTR -1.613** -0.341 -1.418* -0.327[-3.188] [-1.216] [-2.118] [-0.993]

ln(OC/Asset) -1.657 -0.123 -1.288 -0.099[-1.864] [-0.268] [-1.240] [-0.204]

lnsize -0.267 -0.247 -0.035 -0.226[-0.596] [-1.077] [-0.082] [-0.860]

lnBEME -1.440 0.908* -0.447 1.024*[-0.703] [2.004] [-0.198] [1.994]

lnlev -0.957 0.466 -0.771 0.475[-2.119] [0.997] [-1.625] [1.055]

StockRet -6.049 -3.673** -4.137 -3.905**[-2.152] [-2.690] [-1.730] [-2.772]

Female 1.128 0.164 0.576 0.602[0.178] [0.756] [0.088] [1.332]

Year FE Yes Yes Yes YesObservations 750 3218 750 3218R-squared 0.020 0.012 0.013 0.013

46

Table 9: Brand-talent Ratio and CEO Turnovers: Role of Uncertainty (Continued)

Panel B: Brand-talent ratio measured by BTR

(1) (2) (3) (4)D(Non-retirement1)*100 D(Non-retirement2)*100

Sample High Uncertainty Low Uncertainty High Uncertainty Low Uncertainty

lnBTR -1.603** -0.245 -1.328** -0.274[-2.777] [-1.150] [-2.650] [-1.012]

ln(OC/Asset) -1.504 -0.020 -1.085 -0.029[-1.930] [-0.046] [-1.352] [-0.067]

lnsize -0.436 -0.287 -0.183 -0.263[-0.977] [-1.171] [-0.405] [-0.968]

lnBEME -1.642 0.846* -0.620 0.963*[-0.798] [1.958] [-0.271] [1.968]

lnlev -0.915 0.443 -0.754 0.460[-1.788] [1.029] [-1.492] [1.110]

StockRet -5.984 -3.682** -4.072 -3.912**[-2.143] [-2.684] [-1.701] [-2.767]

Female 1.229 0.179 0.681 0.614[0.194] [0.773] [0.103] [1.314]

Year FE Yes Yes Yes YesObservations 750 3218 750 3218R-squared 0.022 0.012 0.013 0.013

47

Table 10: Long-Short Brand-Talent Ratio Portfolio Returns and Aggregate CEO Turnover Rate

This table shows the relation between long-short brand-talent ratio portfolio returns andthe aggregte CEO turnover rate. The dependent variables are the aggregate non-retirementturnover rates. In Column (1) and (2), we classify a CEO turnover as a non-retirement turnoverif the CEO leaves the firm at age 59 or younger due to reasons other than death, and it is 0otherwise. In Column (3) and (4), we classify a CEO turnover as a non-retirement turnoverif the CEO leaves the firm at age 59 or younger due to reasons other than death, or if theCEO resigns according to the Execucomp data, and it is 0 otherwise. The main independentvariables are the contemporaneous and lagged value-weighted long-short brand-talent ratioportfolio returns. We measure the brand-talent ratio using Stature

Strength (denoted as BTR) in Panel A

and using Stature∗SalesTalent Capital (denoted as BTR) in Panel B. Control variables include lagged aggregate

non-retirement CEO turnover rates, the contemporaneous and lagged market returns. Thesample period for the analysis of this table is from 1992 to 2016. We exclude financial firmsand utility firms from the sample. We include t-statistics in parentheses. Standard errors arecomputed using the Newey-West estimator allowing for two lags of serial correlation. *, **, and*** indicate statistical significance at the 10%, 5%, and 1% levels. We present the results of anF-test of whether the sum of the coefficients on the contemporaneous and lagged long-shortBTR portfolio returns are jointly equal to zero. We also present the results of an F-test ofwhether the sum of the coefficients on the contemporaneous and lagged market returns arejointly equal to zero.

Panel A: Brand-talent ratio measured by BTR

(1) (2) (3) (4)AggTO1t (%) AggTO2t (%)

RetLS_BTRt 0.001 -0.004 0.002 -0.001

[0.137] [-1.167] [0.403] [-0.160]RetLS_BTR

t−1 0.009* 0.009* 0.010** 0.008*[2.025] [1.801] [2.305] [1.948]

AggTO1t−1 0.652*** 0.746***[3.760] [6.168]

AggTO2t−1 0.428** 0.527***[2.193] [4.223]

RetMktt 0.004* 0.003

[1.891] [1.533]RetMkt

t−1 0.000 0.002[0.160] [1.054]

Constant 0.309* 0.194 0.555** 0.416***[2.025] [1.686] [2.835] [3.083]

R-squared 0.558 0.622 0.407 0.519p(F) (RetLS_BTR

t + RetLS_BTRt−1 ) = 0 0.188 0.280 0.095 0.063

p(F) (RetMktt + RetMkt

t−1 ) = 0 0.106 0.069

48

Table 10: Long-Short Brand-Talent Ratio Portfolio Returns and Aggregate CEO Turnover Rate(Continued)

Panel B: Brand-talent ratio measured by BTR

(1) (2) (3) (4)AggTO1t (%) AggTO2t (%)

RetLS_BTRt 0.004 -0.008 0.005 -0.003

[0.791] [-1.499] [1.223] [-0.533]

RetLS_BTRt−1 0.010*** 0.017** 0.011*** 0.013**

[3.080] [2.836] [4.320] [2.419]AggTO1t−1 0.628*** 0.754***

[4.132] [7.652]AggTO2t−1 0.403** 0.540***

[2.375] [4.297]RetMkt

t 0.006*** 0.005**[3.612] [2.483]

RetMktt−1 -0.003 -0.001

[-1.031] [-0.207]Constant 0.309** 0.186* 0.558*** 0.401***

[2.195] [1.892] [3.096] [3.038]

R-squared 0.608 0.714 0.495 0.602

p(F) ( RetLS_BTRt + RetLS_BTR

t−1 ) = 0 0.006 0.005 0.001 0.004p(F) (RetMkt

t + RetMktt−1 ) = 0 0.042 0.053

49

Table 11: Summary Statistics for the Mimicking Portfolio Samples

Panel A: Summary statistics for the sample in the return analysis using mimicking portfolio approach.The data sample is the CRSP monthly data from Jan. 1929 (we use three year data to estimate mimickingportfolio beta) to Dec. 2016. We exclude financial firms and utility firms from the sample. We explainthe definition of the variables in Appendix Table A1.

Variables Mean Median 10% 90% S.D. # of obs.

βmp -1.84 -1.64 -4.72 0.57 2.43 2,665,876βmp -2.42 -2.15 -5.40 0.17 2.47 2,665,876vxobeta -0.59 -0.48 -1.49 0.16 0.84 2,665,876epubetab -0.09 -0.07 -0.33 0.11 0.22 1,472,229epubetan -0.06 -0.05 -0.21 0.06 0.13 1,472,229

Panel B: Summary statistics for the sample in the analysis of firms’ financial policies using mimickingportfolio approach. The data sample is the Compustat yearly data from 1950 to 2016. We excludefinancial firms and utility firms from the sample. We explain the definition of the variables in AppendixTable A1.

Variables Mean Median 10% 90% S.D. # of obs.

βmp -2.26 -2.03 -4.97 0.11 2.24 151,156βmp -2.79 -2.54 -5.67 -0.22 2.38 151,156Cash/Lagged Asset (%) 16.77 8.20 1.09 43.78 22.45 148,873∆Cash/Net Income (%) 10.75 0 -60.22 102.16 199.03 108,851∆Equity/Lagged Asset (%) 4.80 0.14 0 7.35 17.52 148,875Payout/Lagged Asset (%) 2.14 0.22 0 6.05 4.18 148,875Dividend/Lagged Asset (%) 0.95 0 0 3.03 1.82 148,875Repurchases/Lagged Asset (%) 1.10 0 0 3.32 3.10 148,875ln(OC/Asset) -0.42 -0.22 -1.66 0.71 1.23 139,903lnsize 4.61 4.45 1.79 7.64 2.27 148,299lnBEME -0.52 -0.44 -1.73 0.64 1.02 139,097lnlev -0.13 -0.13 -1.52 1.13 1.19 139,662

Panel C: Summary statistics for the sample in the analysis of managerial compensation and turnoverusing mimicking portfolio approach. The data sample is the Execucomp data from 1992 to 2016. Weexclude financial firms and utility firms from the sample. We explain the definition of the variables inAppendix Table A1.

Variables Mean Median 10% 90% S.D. # of obs.

lnTDC1 (CEOs) 7.93 7.96 6.54 9.32 1.20 27,614lnTDC1 (Non-CEOs) 6.95 6.91 5.75 8.23 1.00 125,901lnTDC1 (Both CEOs and Non-CEOs) 7.13 7.07 5.82 8.55 1.11 153,515D(Non-retirement1)*100 (CEOs) 4.16 0 0 0 19.97 27,810D(Non-retirement2)*100 (CEOs) 4.78 0 0 0 21.33 27,810D(Retirement1)*100 (CEOs) 4.91 0 0 0 21.60 27,810D(Retirement2)*100 (CEOs) 5.63 0 0 0 23.05 27,810

50

Table 12: Excess Portfolio Returns Sorted on Mimicking Portfolio Beta

This table shows the asset pricing tests for portfolios sorted on mimicking portfolio beta. InPanel A, our analysis is performed based on the mimicking portfolio beta for the brand-talentratio measured by Stature

Strength (denoted as βmp), while in Panel B, our analysis is performed based

on the mimicking portfolio beta for the brand-talent ratio measured by Stature∗SalesTalent Capital (denoted as

βmp). In June of year t, we sort firms into five quintiles based on firms’ mimicking portfoliobeta in year t − 1. Once the portfolios are formed, their monthly returns are tracked fromJuly of year t to June of year t + 1. We compute the value-weighted portfolio returns andreport the excess returns of the individual portfolios and the long/short portfolio. We alsoreport the portfolio alphas and betas estimated by the Carhart four-factor model and thePástor-Stambaugh five-factor model. Data on SMB, HML, and MOM are from Kenneth French’swebsite. The liquidity factor is from L’uboš Pástor’s website. The sample of this table is theCRSP monthly data from Jan. 1926 to Dec. 2016. For the Pástor-Stambaugh five-factor model,our analysis starts from 1968 as the liquidity factor is available from 1968 onward. We excludefinancial firms and utility firms from the sample. We annualize the excess returns and thealphas by multiplying by 12. *, **, and *** indicate statistical significance at the 10%, 5%, and1% levels.

Panel A: Mimicking portfolio beta for BTR

βmp Portfolios 1 (Low) 2 3 4 5 (High) 5-1

Excess Returns

E[R]-r f (%) 12.49*** 11.06*** 10.13*** 12.06*** 12.17*** -0.32[3.95] [4.39] [4.50] [5.86] [5.22] [-0.13]

Carhart Four-Factor Model

α (%) 6.09*** 4.98*** 2.62*** 3.73*** 1.99** -4.10**[4.64] [6.41] [4.23] [5.89] [2.13] [-2.14]

βmkt 1.29*** 1.14*** 1.06*** 0.97*** 0.99*** -0.31***[58.93] [87.92] [102.24] [91.83] [63.02] [-9.54]

βsmb 0.57*** 0.26*** 0.09*** -0.01 -0.05** -0.62***[16.63] [12.86] [5.31] [-0.44] [-2.02] [-12.37]

βhml -0.58*** -0.36*** -0.01 0.17*** 0.51*** 1.09***[-17.58] [-18.31] [-0.89] [10.95] [21.65] [22.60]

βmom -0.21*** -0.15*** -0.05*** 0.05*** 0.07*** 0.29***[-8.48] [-9.94] [-4.18] [4.14] [3.96] [7.74]

R2 0.84 0.91 0.93 0.91 0.85 0.43

Pástor-Stambaugh Five-Factor Model

α (%) 7.96*** 5.85*** 4.11*** 4.68*** 3.40*** -4.56**[4.87] [6.01] [5.87] [6.40] [3.74] [-2.10]

βmkt 1.32*** 1.16*** 1.04*** 0.96*** 0.90*** -0.43***[43.13] [63.58] [78.78] [69.85] [52.43] [-10.53]

βsmb 0.83*** 0.36*** 0.08*** -0.07*** -0.15*** -0.98***[19.35] [14.13] [4.17] [-3.64] [-6.29] [-17.23]

βhml -0.64*** -0.38*** -0.07*** 0.15*** 0.27*** 0.91***[-13.34] [-13.52] [-3.41] [7.04] [10.17] [14.33]

βmom -0.18*** -0.19*** -0.10*** -0.03** 0.02 0.21***[-6.02] [-10.63] [-7.29] [-2.06] [1.19] [5.04]

βps -0.00 -0.00 0.00 0.00 0.00 0.00[-0.09] [-1.10] [0.18] [1.25] [0.92] [0.45]

R2 0.86 0.91 0.93 0.89 0.82 0.62

51

Table 12: Excess Portfolio Returns Sorted on Mimicking Portfolio Beta (Continued)

Panel B: Mimicking portfolio beta for BTR

βmp Portfolios 1 (Low) 2 3 4 5 (High) 5-1

Excess Returns

E[R]-r f (%) 14.69*** 12.68*** 11.31*** 10.12*** 10.75*** -3.94[4.27] [4.71] [5.00] [4.98] [5.34] [-1.61]

Carhart Four-Factor Model

α (%) 6.06*** 5.55*** 4.53*** 2.84*** 2.35*** -3.71**[4.68] [6.29] [7.42] [4.94] [3.13] [-2.12]

βmkt 1.37*** 1.19*** 1.05*** 0.97*** 0.92*** -0.45***[63.60] [81.11] [103.10] [100.68] [73.53] [-15.51]

βsmb 0.80*** 0.36*** 0.15*** -0.02 -0.12*** -0.92***[23.59] [15.39] [9.21] [-1.32] [-6.20] [-20.11]

βhml -0.41*** -0.32*** -0.11*** 0.08*** 0.30*** 0.71***[-12.49] [-14.32] [-7.44] [5.52] [15.74] [15.99]

βmom -0.20*** -0.12*** -0.09*** -0.02 0.07*** 0.26***[-7.83] [-7.32] [-7.94] [-1.41] [4.53] [7.73]

R2 0.87 0.90 0.93 0.93 0.87 0.53

Pástor-Stambaugh Five-Factor Model

α (%) 8.33*** 6.53*** 4.40*** 3.78*** 3.61*** -4.72**[4.96] [6.06] [6.00] [5.31] [4.57] [-2.21]

βmkt 1.36*** 1.21*** 1.07*** 0.98*** 0.87*** -0.49***[43.08] [59.95] [77.90] [73.30] [58.89] [-12.12]

βsmb 1.00*** 0.49*** 0.19*** -0.02 -0.22*** -1.22***[22.71] [17.17] [9.90] [-1.30] [-10.45] [-21.77]

βhml -0.56*** -0.34*** -0.06*** 0.10*** 0.19*** 0.75***[-11.35] [-10.86] [-2.83] [4.93] [8.24] [12.00]

βmom -0.17*** -0.18*** -0.11*** -0.06*** 0.02 0.20***[-5.51] [-8.83] [-8.16] [-4.35] [1.46] [4.88]

βps 0.00 -0.00 -0.00 0.00 0.00 0.00[0.32] [-0.36] [-0.87] [1.31] [0.82] [0.05]

R2 0.86 0.91 0.93 0.91 0.85 0.66

52

Table 13: Mimicking Portfolio Beta and Uncertainty Exposure

This table shows the relation between mimicking portfolio beta and the uncertainty exposure ofstock returns. In Panel A, our analysis is performed based on the mimicking portfolio beta forthe brand-talent ratio measured by Stature

Strength (denoted as βmp), while in Panel B, our analysisis performed based on the mimicking portfolio beta for the brand-talent ratio measured byStature∗SalesTalent Capital (denoted as βmp). We estimate the uncertainty exposure (bit) for firm i at timet from the following regression: retit = ait + bit∆Uncertaintyt + εit, where t ∈ [t− 36, t− 1].∆Uncertainty is the monthly change of the uncertainty measures. We use three uncertaintymeasures. The first measure is the monthly CBOE S&P 100 volatility index (VXO), which isavailable from 1986 onward. We follow Bloom (2009) and extend the VXO times series back to1926. Prior to 1986, VXO is calculated as the monthly standard deviation of the daily S&P 500index normalized to the same mean and variance as the VXO index when they overlap from1986 onward. The second and third measures are the two economic policy uncertainty indexes(baseline overall index and news based policy uncertainty index) developed by Scott Baker,Nick Bloom, and Steven Davis (Baker, Bloom and Davis, 2016). These two economic policyuncertainty indexes are available from 1985 onward. bit for the three uncertainty measuresare denoted as vxobeta, epubetab, and epubetan, respectively. Since we require three year’sdata to estimate bit, vxobeta spans 1929 and 2016, while epubetab and epubetan span 1988 and2016. In Panel A & C, we show the summary statistics of the uncertainty exposure for firmssorted on the mimicking portfolio beta. In each calendar month, firms are sorted into quintilesbased on the mimicking portfolio beta and the sorting is performed within SIC-2 industries. InPanel B & D, we show the relationship between uncertainty exposure and mimicking portfoliobeta using panel regressions. The dependent variables are the monthly uncertain exposure ofindividual stocks. The independent variables are the quintiles of the mimicking portfolio beta(βQ

mp in Panel B and βQmp in Panel D). The sorting of MPB is performed at yearly basis based

on the average mimicking portfolio beta of the firms within a given year. We include SIC-2industry fixed effects and fiscal year fixed effects in the regressions. Standard errors from panelregressions are clustered by firm and calendar month. We exclude financial firms and utilityfirms from the sample. We include t-statistics in parentheses. *, **, and *** indicate statisticalsignificance at the 10%, 5%, and 1% levels.

Panel A: Summary statistics of the uncertainty exposure for firm quintiles sorted on βmp

vxobeta epubetab epubetan

βmp Quntiles Mean Median S.D. # obs Mean Median S.D. # obs Mean Median S.D. # obs

Q1 (Low) -0.934 -0.753 1.230 438,031 -0.164 -0.143 0.332 229,732 -0.107 -0.091 0.199 229,732Q2 -0.731 -0.633 0.845 413,471 -0.125 -0.108 0.219 220,811 -0.080 -0.068 0.133 220,811Q3 -0.606 -0.540 0.716 414,992 -0.104 -0.088 0.194 221,054 -0.066 -0.055 0.118 221,054Q4 -0.504 -0.456 0.637 413,471 -0.084 -0.072 0.176 220,811 -0.054 -0.045 0.106 220,811Q5 (High) -0.360 -0.329 0.745 447,516 -0.059 -0.052 0.214 233,519 -0.038 -0.033 0.130 233,519

53

Table 13: Mimicking Portfolio Beta and Uncertainty Exposure (Continued)

Panel B: Panel regression results, mimicking portfolio beta for BTR

(1) (2) (3)

vxobeta epubetab epubetan

βQmp 0.152*** 0.028*** 0.019***

[21.437] [15.977] [16.764]Industry FE Yes Yes YesCalendar Month FE Yes Yes YesObservations 2130477 1075994 1075994R-squared 0.259 0.130 0.155

Panel C: Summary statistics of the uncertainty exposure for firm quintiles sorted on βmp

vxobeta epubetab epubetan

βmp Quntiles Mean Median S.D. # obs Mean Median S.D. # obs Mean Median S.D. # obs

Q1 (Low) -0.940 -0.772 0.786 438,031 -0.175 -0.154 0.332 229,730 -0.114 -0.098 0.199 229,730Q2 -0.891 -0.758 0.759 413,471 -0.127 -0.112 0.221 220,813 -0.081 -0.070 0.134 220,813Q3 -0.810 -0.708 0.672 414,993 -0.103 -0.090 0.193 221,055 -0.066 -0.056 0.117 221,055Q4 -0.736 -0.632 0.608 413,470 -0.081 -0.071 0.175 220,810 -0.052 -0.044 0.106 220,810Q5 (High) -0.734 -0.660 0.567 447,516 -0.050 -0.046 0.208 233,519 -0.032 -0.029 0.125 233,519

Panel D: Panel regression results, mimicking portfolio beta for BTR

(1) (2) (3)

vxobeta epubetab epubetan

βQmp 0.178*** 0.032*** 0.021***

[24.731] [19.021] [19.859]Industry FE Yes Yes YesCalendar Month FE Yes Yes YesObservations 2130477 1075994 1075994R-squared 0.279 0.139 0.165

54

Table 14: Mimicking Portfolio Beta and Firms’ Financial Policies

This table shows the relation between mimicking portfolio beta and firms’ financial policies. InPanel A, our analysis is performed based on the mimicking portfolio beta for the brand-talentratio measured by Stature

Strength (denoted as βmp), while in Panel B, our analysis is performed

based on the mimicking portfolio beta for the brand-talent ratio measured by Stature∗SalesTalent Capital

(denoted as βmp). The dependent variables are the amount of cash holdings (% of laggedasset), the change of cash holdings (% of contemporaneous net income), the amount of equityissuance (% of lagged asset), the amount of total payout (% of lagged asset), the amount ofdividend issuance (% of lagged asset), and the amount of share repurchases (% of laggedasset). The outcome variables are winsorized at the 1st and 99th percentiles of their empiricaldistributions to mitigate the effect of outliers. The main independent variable is the quintile ofthe mimicking portfolio beta (βQ

mp in Panel A and βQmp in Panel B). The sorting of mimicking

portfolio beta is performed at yearly basis based on the average mimicking portfolio betaof the firms within a given year. Control variables include lagged firm characteristics suchas the natural log of the organization capital to asset ratio ln(OC/Asset), the natural logof firm market capitalization (lnsize), the nature log of the book-to-market ratio (lnBEME)and the natural log of the debt to equity ratio (lnlev). We include SIC-2 industry fixedeffects and fiscal year fixed effects in the regressions. The sample for the analysis of thistable is the Compustat yearly data from 1950 to 2016. We exclude financial firms and utilityfirms from the sample. We include t-statistics in parentheses. Standard errors are clusteredby firm and fiscal year. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels.

Panel A: Mimicking portfolio beta for BTR

(1) (2) (3) (4) (5) (6)Casht

Assett−1(%) ∆Casht

NIt(%) ∆Equityt

Assett−1(%) Payoutt

Assett−1(%) Dividendt

Assett−1(%) Repurchasest

Assett−1(%)

βQmp -1.234*** -2.754*** -0.644*** 0.324*** 0.201*** 0.109***

[-6.417] [-3.418] [-5.555] [13.964] [15.512] [6.112]ln(OC/Asset) -0.228 0.902 0.141 0.279*** 0.107*** 0.164***

[-1.415] [1.474] [1.600] [8.807] [6.452] [7.409]lnsize -1.557*** -2.175*** -1.501*** 0.598*** 0.280*** 0.315***

[-15.680] [-4.570] [-11.772] [15.860] [14.622] [7.817]lnBEME -8.319*** -8.285*** -6.327*** -0.484*** -0.179*** -0.232***

[-19.685] [-4.977] [-12.793] [-8.117] [-5.198] [-6.878]lnlev -7.115*** 0.182 -1.319*** -0.507*** -0.245*** -0.211***

[-31.882] [0.179] [-7.977] [-13.673] [-7.303] [-8.705]Industry FE Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes YesObservations 132688 96470 132689 132689 132689 132689R-squared 0.321 0.009 0.167 0.183 0.248 0.119

55

Table 14: Mimicking Portfolio Beta and Firms’ Financial Policies (Continued)

Panel B: Mimicking portfolio beta for BTR

(1) (2) (3) (4) (5) (6)Casht

Assett−1(%) ∆Casht

NIt(%) ∆Equityt

Assett−1(%) Payoutt

Assett−1(%) Dividendt

Assett−1(%) Repurchasest

Assett−1(%)

βQmp -0.964*** -2.622*** -0.576*** 0.210*** 0.119*** 0.081***

[-7.350] [-4.080] [-5.962] [14.755] [13.818] [7.848]ln(OC/Asset) -0.242 1.068* 0.126 0.280*** 0.108*** 0.164***

[-1.508] [1.751] [1.457] [8.784] [6.400] [7.442]lnsize -1.557*** -2.143*** -1.497*** 0.599*** 0.281*** 0.315***

[-15.770] [-4.488] [-11.836] [15.983] [14.861] [7.816]lnBEME -8.280*** -8.070*** -6.279*** -0.480*** -0.174*** -0.234***

[-19.539] [-4.897] [-12.854] [-8.070] [-4.994] [-6.981]lnlev -7.097*** 0.367 -1.301*** -0.508*** -0.245*** -0.212***

[-31.652] [0.360] [-7.950] [-13.678] [-7.167] [-8.846]Industry FE Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes YesObservations 132719 96492 132720 132720 132720 132720R-squared 0.323 0.009 0.169 0.182 0.243 0.120

56

Table 15: Mimicking Portfolio Beta and Executive Compensation

This table shows the relation between mimicking portfolio beta and executive compensation.In Panel A, our analysis is performed based on the mimicking portfolio beta for thebrand-talent ratio measured by Stature

Strength (denoted as βmp), while in Panel B, our analysis isperformed based on the mimicking portfolio beta for the brand-talent ratio measured byStature∗SalesTalent Capital (denoted as βmp). The dependent variables are the natural log of the total pay(Execucomp variable tdc1). The main independent variable is the quintile of the mimickingportfolio beta (βQ

mp in Panel A and βQmp in Panel B). The sorting of mimicking portfolio

beta is performed at yearly basis based on the average mimicking portfolio beta of thefirms within a given year. Control variables include lagged firm characteristics such asthe natural log of the organization capital to asset ratio ln(OC/Asset), the natural log offirm market capitalization (lnsize), the nature log of the book-to-market ratio (lnBEME),the natural log of the debt to equity ratio (lnlev), the 12-month stock returns prior to thefiscal year ends (StockRet), the age of the executives (age), and a dummy variable for thegender of the executives (Female). SIC-2 industry fixed effects and fiscal year fixed effectsare included in the regressions as indicated by the table. The sample for the analysis of thistable is the Execucomp yearly data from 1992 to 2016. We exclude financial firms and utilityfirms from the sample. We include t-statistics in parentheses. Standard errors are clusteredby firm and fiscal year. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels.

Panel A: Mimicking portfolio beta for BTR

(1) (2) (3) (4) (5) (6)lnTDC1

Sample CEO CEO Non-CEO Non-CEO Both Both

βQmp -0.028*** -0.030*** -0.028*** -0.024*** -0.029*** -0.026***

[-3.835] [-5.134] [-4.946] [-5.083] [-5.023] [-5.446]ln(OC/Asset) 0.012 0.009 0.021** 0.020*** 0.021*** 0.020***

[1.447] [1.023] [2.790] [2.959] [2.939] [3.099]lnsize 0.438*** 0.437*** 0.410*** 0.409*** 0.415*** 0.415***

[27.331] [26.511] [50.184] [49.757] [40.628] [40.015]lnBEME 0.171*** 0.179*** 0.157*** 0.170*** 0.155*** 0.169***

[9.552] [9.780] [13.287] [14.191] [12.978] [13.818]lnlev 0.191*** 0.202*** 0.137*** 0.148*** 0.153*** 0.166***

[12.225] [12.548] [15.569] [15.772] [15.595] [16.115]StockRet 0.213*** 0.211*** 0.159*** 0.158*** 0.176*** 0.175***

[7.226] [7.395] [11.178] [10.742] [9.699] [9.720]Age -0.003 -0.002 -0.002 -0.000 0.008*** 0.009***

[-1.242] [-1.124] [-1.383] [-0.475] [6.205] [7.082]Female 0.165** 0.186*** -0.056*** -0.067*** -0.127*** -0.134***

[2.218] [2.822] [-3.004] [-3.790] [-5.641] [-6.015]Industry FE No Yes No Yes No YesYear FE Yes Yes Yes Yes Yes YesObservations 24886 24886 70110 70110 94996 94996R-squared 0.413 0.423 0.476 0.487 0.398 0.407

57

Table 15: Mimicking Portfolio Beta and Executive Compensation (Continued)

Panel B: Mimicking portfolio beta for BTR

(1) (2) (3) (4) (5) (6)lnTDC1

Sample CEO CEO Non-CEO Non-CEO Both Both

βQmp -0.030*** -0.032*** -0.026*** -0.025*** -0.028*** -0.026***

[-3.702] [-4.615] [-4.354] [-4.888] [-4.405] [-5.028]ln(OC/Asset) 0.012 0.009 0.021*** 0.020*** 0.021*** 0.020***

[1.477] [1.024] [2.825] [2.960] [2.983] [3.104]lnsize 0.440*** 0.440*** 0.411*** 0.411*** 0.417*** 0.417***

[27.409] [26.894] [49.426] [49.739] [40.313] [40.235]lnBEME 0.169*** 0.179*** 0.154*** 0.169*** 0.153*** 0.168***

[9.550] [9.814] [13.000] [14.157] [12.721] [13.775]lnlev 0.190*** 0.201*** 0.134*** 0.147*** 0.151*** 0.165***

[12.176] [12.548] [15.228] [15.553] [15.226] [15.859]StockRet 0.213*** 0.210*** 0.159*** 0.158*** 0.176*** 0.174***

[7.195] [7.340] [11.317] [10.810] [9.762] [9.734]Age -0.003 -0.002 -0.002 -0.000 0.008*** 0.009***

[-1.229] [-1.105] [-1.425] [-0.467] [6.167] [7.081]Female 0.165** 0.186*** -0.056*** -0.067*** -0.127*** -0.134***

[2.217] [2.806] [-2.996] [-3.776] [-5.630] [-6.003]Industry FE No Yes No Yes No YesYear FE Yes Yes Yes Yes Yes YesObservations 24886 24886 70110 70110 94996 94996R-squared 0.413 0.423 0.476 0.487 0.398 0.407

58

Table 16: Mimicking Portfolio Beta and CEO Turnovers

This table shows the relation between mimicking portfolio beta and CEO turnovers. In PanelA, our analysis is performed based on the mimicking portfolio beta for the brand-talent ratiomeasured by Stature

Strength (denoted as βmp), while in Panel B, our analysis is performed based on

the mimicking portfolio beta for the brand-talent ratio measured by Stature∗SalesTalent Capital (denoted as

βmp). In Column (1) and (2), the dependent variable is 100 for a given CEO-year observationif the CEO leaves the firm at age 59 or younger due to reasons other than death, and it is0 otherwise. In Column (3) and (4), the dependent variable is 100 for a given CEO-yearobservation if the CEO leaves the firm at age 59 or younger due to reasons other than death,or if the CEO resigns according to the Execucomp data, and it is 0 otherwise. In Column (5)and (6), the dependent variable is 100 for a given CEO-year observation if the CEO leavesthe firm at age 60 or elder due to reasons other than death, and it is 0 otherwise. In Column(7) and (8), the dependent variable is 100 for a given CEO-year observation if the CEOleaves the firm at age 60 or elder due to reasons other than death, or if the CEO leaves thefirm due to retirement according to the Execucomp data, and it is 0 otherwise. The mainindependent variable is the quintile of the mimicking portfolio beta (βQ

mp in Panel A andβQ

mp in Panel B). The sorting of mimicking portfolio beta is performed at yearly basis basedon the average mimicking portfolio beta of the firms within a given year. Control variablesinclude lagged firm characteristics such as the natural log of the organization capital to assetratio ln(OC/Asset), the natural log of firm market capitalization (lnsize), the nature log of thebook-to-market ratio (lnBEME), the natural log of the debt to equity ratio (lnlev), the 12-monthstock returns prior to the fiscal year ends (StockRet), and a dummy variable for the gender ofthe executives (Female). We do not control for the age of the executivies because age is usedto construct the dependent variables. SIC-2 industry fixed effects and fiscal year fixed effectsare included in the regressions as indicated by the table. The sample for the analysis of thistable is the Execucomp yearly data from 1992 to 2016. We exclude financial firms and utilityfirms from the sample. We include t-statistics in parentheses. Standard errors are clusteredby firm and fiscal year. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels.

59

Table 16: Mimicking Portfolio Beta and CEO Turnovers (Continued)

Panel A: Mimicking portfolio beta for BTR

(1) (2) (3) (4) (5) (6) (7) (8)D(Non-retirement1)*100 D(Non-retirement1)*100 D(Retirement2)*100 D(Retirement2)*100

βQmp -0.502*** -0.413*** -0.626*** -0.527*** 0.298*** 0.258** 0.367*** 0.306***

[-6.189] [-4.776] [-6.777] [-5.716] [3.130] [2.558] [3.953] [3.224]ln(OC/Asset) 0.323*** 0.360*** 0.378*** 0.405*** 0.228** 0.150* 0.243*** 0.195**

[3.577] [3.631] [4.183] [3.836] [2.669] [1.935] [3.099] [2.720]lnsize -0.055 -0.016 -0.089 -0.047 0.696*** 0.695*** 0.857*** 0.853***

[-0.559] [-0.152] [-0.906] [-0.465] [9.173] [8.298] [9.479] [9.740]lnBEME -0.104 0.072 0.016 0.219 1.184*** 1.205*** 1.282*** 1.296***

[-0.509] [0.325] [0.073] [0.919] [7.580] [6.787] [9.196] [8.018]lnlev 0.050 0.080 -0.005 0.047 0.535*** 0.493*** 0.694*** 0.633***

[0.448] [0.652] [-0.038] [0.318] [5.056] [5.309] [5.863] [5.804]StockRet -2.325*** -2.299*** -3.019*** -2.987*** -0.662** -0.637** -0.945*** -0.917**

[-5.876] [-5.582] [-6.837] [-6.566] [-2.285] [-2.108] [-2.797] [-2.624]Female 0.568 0.070 0.454 -0.070 -3.182*** -3.089*** -3.327*** -3.296***

[0.843] [0.098] [0.627] [-0.092] [-5.378] [-4.759] [-5.329] [-4.731]Industry FE No Yes No Yes No Yes No YesYear FE Yes Yes Yes Yes Yes Yes Yes YesObservations 26002 26002 26002 26002 26002 26002 26002 26002R-squared 0.007 0.011 0.008 0.012 0.008 0.011 0.009 0.012

Panel B: Mimicking portfolio beta for BTR

(1) (2) (3) (4) (5) (6) (7) (8)D(Non-retirement1)*100 D(Non-retirement2)*100 D(Retirement1)*100 D(Retirement2)*100

βQmp -0.493*** -0.425*** -0.620*** -0.547*** 0.297*** 0.263** 0.362*** 0.306***

[-5.651] [-4.627] [-6.457] [-5.733] [3.254] [2.700] [4.093] [3.346]ln(OC/Asset) 0.328*** 0.358*** 0.384*** 0.403*** 0.225** 0.151* 0.240*** 0.197**

[3.630] [3.650] [4.242] [3.864] [2.654] [1.957] [3.094] [2.744]lnsize -0.025 0.015 -0.050 -0.008 0.677*** 0.676*** 0.834*** 0.833***

[-0.250] [0.143] [-0.510] [-0.074] [8.629] [7.812] [9.110] [9.366]lnBEME -0.139 0.058 -0.027 0.202 1.204*** 1.214*** 1.308*** 1.308***

[-0.692] [0.264] [-0.125] [0.857] [7.925] [6.931] [9.841] [8.297]lnlev 0.025 0.065 -0.035 0.028 0.549*** 0.503*** 0.712*** 0.645***

[0.223] [0.523] [-0.265] [0.188] [5.180] [5.369] [5.984] [5.870]StockRet -2.328*** -2.304*** -3.024*** -2.994*** -0.660** -0.635** -0.943** -0.915**

[-5.968] [-5.678] [-6.943] [-6.693] [-2.253] [-2.081] [-2.762] [-2.596]Female 0.565 0.064 0.452 -0.078 -3.181*** -3.085*** -3.325*** -3.289***

[0.842] [0.090] [0.628] [-0.102] [-5.412] [-4.782] [-5.366] [-4.755]Industry FE No Yes No Yes No Yes No YesYear FE Yes Yes Yes Yes Yes Yes Yes YesObservations 26002 26002 26002 26002 26002 26002 26002 26002R-squared 0.007 0.011 0.007 0.012 0.008 0.011 0.009 0.012

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Table 17: Mimicking Portfolio Beta and CEO Turnovers, Eisfeldt and Kuhnen Sample

This table shows the relation between mimicking portfolio beta and CEO turnovers in theEisfeldt and Kuhnen CEO turnover sample (Eisfeldt and Kuhnen, 2013). Building on theprecedure proposed by Parrino (Parrino, 1997), Eisfeldt and Kuhnen classified CEO turnoversinto planned retirements, forced-out turnovers and unclassified departures based on thenews coverage about the reason why a CEO left. Jenter and Kanaan (2015) also use a similarapproach to classify CEO turnovers. In Column (1) and (2) the dependent variable is 100 for agiven CEO-year observation if the CEO leaves the firm in that year due to forced-out turnoversand unclassified departures, and it is 0 otherwise. In Column (3) and (4), the dependentvariable is 100 for a given CEO-year observation if the CEO leaves the firm in that year due toplanned retirement, and it is 0 otherwise. The turnover classification data are downloaded fromAndrea Eisfeldt and Camelia Kuhnen’s websites. The sample spans 1992 and 2006. The mainindependent variable is the quintile of the mimicking portfolio beta (βQ

mp in Panel A and βQmp in

Panel B). The sorting of mimicking portfolio beta is performed at yearly basis based on theaverage mimicking portfolio beta of the firms within a given year. The sorting of mimickingportfolio beta is performed at yearly basis based on the average mimicking portfolio beta of thefirms within a given year. Control variables include lagged firm characteristics such as thenatural log of the organization capital to asset ratio ln(OC/Asset), the natural log of firm marketcapitalization (lnsize), the nature log of the book-to-market ratio (lnBEME), the natural log of thedebt to equity ratio (lnlev), the 12-month stock returns prior to the fiscal year ends (StockRet),the age of the executives (age), and a dummy variable for the gender of the executives(Female). Notice that we control for executive age in this analysis because age is not used toconstruct the dependent variables. SIC-2 industry fixed effects and fiscal year fixed effects areincluded in the regressions as indicated by the table. We exclude financial firms and utilityfirms from the sample. We include t-statistics in parentheses. Standard errors are clusteredby firm and fiscal year. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels.

Panel A: Mimicking portfolio beta for BTR

(1) (2) (3) (4)D(Non-retirement_EK)*100 D(Retirement_EK)*100

βQmp -0.621** -0.635** 0.185 0.185

[-2.428] [-2.089] [0.824] [0.824]ln(OC/Asset) -0.213 0.099 0.459** 0.459**

[-0.883] [0.400] [2.733] [2.733]lnsize 0.095 0.191 0.056 0.056

[0.358] [0.686] [0.284] [0.284]lnBEME 0.773 0.994* -0.302 -0.302

[1.305] [1.870] [-0.634] [-0.634]lnlev -0.344 -0.535 0.744** 0.744**

[-0.784] [-1.114] [2.356] [2.356]StockRet -2.610* -2.383 -0.474 -0.474

[-1.786] [-1.534] [-0.968] [-0.968]Age -0.017 0.016 0.310*** 0.310***

[-0.352] [0.333] [4.830] [4.830]Female -3.936 -6.322 1.679 1.679

[-0.703] [-1.087] [0.586] [0.586]Industry FE No Yes No YesYear FE Yes Yes Yes YesObservations 5149 5149 5149 5149R-squared 0.035 0.045 0.046 0.046

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Table 17: Mimicking Portfolio Beta and CEO Turnovers, Eisfeldt and Kuhnen Sample (Contin-ued)

Panel B: Mimicking portfolio beta for BTR

(1) (2) (3) (4)D(Non-retirement_EK)*100 D(Retirement_EK)*100

βQmp -0.603** -0.576* 0.123 -0.049

[-2.215] [-1.771] [0.573] [-0.278]ln(OC/Asset) -0.209 0.101 0.458** 0.536**

[-0.873] [0.410] [2.725] [2.233]lnsize 0.126 0.214 0.061 0.069

[0.482] [0.815] [0.309] [0.316]lnBEME 0.751 0.970* -0.276 -0.405

[1.254] [1.811] [-0.589] [-0.730]lnlev -0.352 -0.555 0.763** 0.800***

[-0.797] [-1.165] [2.367] [2.959]StockRet -2.601* -2.369 -0.488 -0.473

[-1.780] [-1.523] [-0.974] [-1.038]Age -0.017 0.015 0.311*** 0.318***

[-0.348] [0.307] [4.839] [4.538]Female -3.877 -6.249 1.625 1.735

[-0.692] [-1.072] [0.571] [0.567]Industry FE No Yes No YesYear FE Yes Yes Yes YesObservations 5149 5149 5149 5149R-squared 0.035 0.044 0.046 0.052

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Table A1: Definition of Variables

This table shows the definition and data sources of the variables.

Variables Definition Sources

lnBTR The natural log of the brand-talent ratio measured by StatureStrength

(denoted as BTR). Stature and Strength come from the BAVconsulting firm’s customer survey. Stature measures how fa-miliar customers are with the brand and whether they have apositive regard towards it. Stature is a product of Knowledge(how well consumers know the brand) and Esteem (howmuch regard and loyalty consumers have towards the brand).Strength measures how relevant is the brand to customersand whether this brand is different and unique comparedto its competitors according to the perception of consumers.Strength is a product of Relevance (how relevant a brand is toconsumers) and Di f f erentiation (how different and unique isa brand compared to its competitors).

BAV

lnBTR The natural log of the brand-talent ratio measured byStature∗SalesTalent Capital (denoted as BTR). Sales is the Compustat itemrevt. We construct the talent capital measure from the adjustedSG&A expenditures (xsga in Compustat) using the perpetualinventory method (25% depreciation rate). We take out ad-vertising expenses (xad in Compustat) and foreign exchangeincome (loss) ( f ca in Compustat) from SG&A to measure thetalent capital.

BAV; Compustat

βQmp Quitiles of the mimicking portfolio beta (βmp) for BTR. To

estimate βmp, we first compute the mimicking portfolio forBTR by projecting the BTR long-short portfolio returns (longQuintile 5, short Quintile 1, equal-weighted) on the spaceof excess returns. Specifically, we run the following re-gression: LSt = a + b′[BL, BM, BH, SL, SM, SH, Mom]t + εt,where [BL, BM, BH, SL, SM, SH, Mom] are the excess re-turns of the six Fama-French benchmark portfolios on size(Small and Big) and book-to-market (Low, Medium, andHigh) in excess of the risk-free rate and Mom is the mo-mentum factor. The BTR mimicking portfolio return is givenby MPt = b′[BL, BM, BH, SL, SM, SH, Mom]t + εt. We nextestimate the mimicking portfolio beta for firm i at time t us-ing the following regression: retit = αit + βitMPt + εit, wheret ∈ [t − 36, t − 1]. The estimated coefficient βit is the mim-icking portfolio beta (βmp). We further sort the mimickingportfolio beta into quintiles. The sorting is performed at theyearly basis based on the average mimicking portfolio beta ofthe firms within a given year.

BAV; CRSP

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Table A1: Definition of Variables (Continued)

Variables Definition Sources

βQmp Quitiles of the mimicking portfolio beta (βmp) for BTR.

The computation method of βmp is the same as the onefor computing βmp, except that we project the long-shortBTR portfolio returns to find the mimicking portfolio.

BAV; CRSP

vxobeta The uncertainty exposure for firm i at time t. It is mea-sured by the coefficient bit estimated from the regres-sion: retit = ait + bit∆VXOt + εit, where t ∈ [t− 36, t− 1].∆VXO is the change of the monthly CBOE S&P 100 volatil-ity index. VXO data come from CBOE and it is availablefrom 1986 onward. We follow Bloom (2009) and extendthe VXO times series. Prior to 1986, VXO is calculatedas the monthly standard deviation of the daily S&P 500index normalized to the same mean and variance as theVXO index when they overlap from 1986 onward.

CRSP; CBOE; Bloom (2009)

epubetab The political uncertainty exposure for firm i at time t. It ismeasured by the coefficient bit estimated from the regres-sion: retit = ait + bit∆EPUt + εit, where t ∈ [t− 36, t− 1].∆EPU is the change of the monthly economic policy un-certainty indexes (baseline overall index) developed byScott Baker, Nick Bloom, and Steven Davis (Baker, Bloomand Davis, 2016). EPU is available from 1985 onward.

Baker, Bloom and Davis (2016)

epubetan Same as epubeta1 except that it is estimated based onthe monthly change of the news based policy uncertaintyindex.

Baker, Bloom and Davis (2016)

CashtAssett−1

The amount of cash holding (che) normalize by the laggedtotal asset (at).

Compustat

∆CashtNIt

The change of cash holding (chech) normalize by thecontemporaneous net income (ni).

Compustat

∆EquitytAssett−1

The amount of equity issuance (sstk) normalize by thelagged total asset (at).

Compustat

PayouttAssett−1

The amount of total payout (dv + prstkc) normalize bythe lagged total asset (at).

Compustat

DividendtAssett−1

The amount of dividend issuance (dv) normalize by thelagged total asset (at).

Compustat

RepurchasestAssett−1

The amount of share repurchases (prstkc) normalize bythe lagged total asset (at).

Compustat

lnTDC1 The natural log of the total compensation (tdc1, in thou-sand dollars).

Execucomp

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Table A1: Definition of Variables (Continued)

Variables Definition Sources

D(Non-retirement1) A dummy variable that equals one if the CEO leaves thefirm at age 59 or younger due to reasons other than death,and it is 0 otherwise.

Execucomp

D(Non-retirement2) A dummy variable that equals one if the CEO leaves thefirm at age 59 or younger due to reasons other than death,or if the CEO resigns according to the Execucomp data,and it is 0 otherwise.

Execucomp

D(Retirement1) A dummy variable that equals one if the CEO leaves thefirm at age 60 or elder due to reasons other than death,and it is 0 otherwise.

Execucomp

D(Retirement2) A dummy variable that equals one if the CEO leaves thefirm at age 60 or elder due to reasons other than death,or if the CEO leaves the firm due to retirement accordingto the Execucomp data, and it is 0 otherwise.

Execucomp

D(Non-retirement_EK) A dummy variable that equals one if the CEO leavesthe firm due to forced-out turnovers or unclassified de-partures in the Eisfeldt and Kuhnen data (Eisfeldt andKuhnen, 2013), and it is 0 otherwise. Building on theprecedure proposed by Parrino (Parrino, 1997), Eisfeldtand Kuhnen classified CEO turnovers into planned retire-ments, forced-out turnovers and unclassified departuresbased on the news coverage about the reason why a CEOleft. Jenter and Kanaan (2015) also use a similar approachto classify CEO turnovers. The Eisfeldt and Kuhnen datacover CEO turnovers from 1992 to 2006.

Eisfeldt and Kuhnen (2013)

D(Retirement_EK) A dummy variable that equals one if the CEO leavesthe firm due to planned retirement in the Eisfeldt andKuhnen data (citation needed), and it is 0 otherwise.

Eisfeldt and Kuhnen (2013)

ln(OC/Asset) The nature log of the organization capital normalized byasset. Following Eisfeldt and Papanikolaou (2013), weconstruct the organization capital from SG&A expendi-tures using the perpetual inventory method.

Compustat

lnsize The natural log of the market cap (in million dollars). CRSP

lnBEME The natural log of the book-to-market ratio. CRSP; Compustat

lnlev The natural log of the debt-to-equity ratio. Compustat

StockRet The 12-month stock returns prior to the fiscal year ends. CRSP

Age The age of the executives Execucomp

Female A dummy variable that equals one if the executive is afemale.

Execucomp

65

Table A2: Industry Distribution of the BAV Sample

This table presents the distribution of the BAV sample and CRSP-Compustat universe byindustry for the period 1993 − 2016. Industries are defined according to the Fama-French12-industry classification. We report the total number of firm-year observations and theproportion (in percentage) of the number of observations in each industry in both the BAVsample and the Compustat-CRSP universe.

FF12 Industry Name # Firm-Year Obs. % Firm-Year Obs.

BAV Compustat-CRSP BAV Compustat-CRSP

Consumer NonDurables 1,220 6,487 19.91 4.31Consumer Durables 151 3,110 2.46 2.07Manufacturing 515 12,321 8.4 8.19Energy 125 5,671 2.04 3.77Chemicals 280 2,849 4.57 1.89Business Equipment 708 24,908 11.55 16.55Telecommunications 364 4,604 5.94 3.06Utilities 2 3,464 0.03 2.3Shops 1,281 11,797 20.9 7.84Healthcare 197 14,634 3.21 9.73Money 782 42,368 12.76 28.16Other 504 18,265 8.22 12.14

66