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Advertising intensity, investor recognition, and implied cost of capital

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Abstract

The main purpose of this paper is to test Merton’s (J Finance 42(3):483–510, 1987) hypothesis that better investor recognition is correlated with lower expected returns. We measure investor recognition with the firms’ advertising intensity and offer consistent evidence that higher advertising intensity is associated with lower implied cost of capital, as derived from Value Line target prices and dividend forecasts. Investor recognition plays an important role in attracting investors, improving liquidity, and ultimately reducing the cost of capital. The findings shed light on the capital market implications of advertising expenditures and complement the extant research on investor recognition.

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Notes

  1. Specifically, he conjectures that “[the] model provides a rationale for expenditures on advertising about the firm that is targeted for investors and on public relations designed to generate stories about the firm in the financial press” (Merton 1987, p. 501).

  2. In the paper, we use implied cost of (equity) capital and ex ante cost of (equity) capital interchangeably.

  3. In their study, improved liquidity includes the reduced bid-ask spread, smaller price impacts, and greater depth for the common stock of the advertising firm.

  4. The liquidity issue can be more important in emerging market, as evidenced in Hsieh et al. (2008).

  5. However, it should be noted that Chan et al. (2001) seek to investigate the valuation effects of a firm’s intangible assets, such as advertising expenditures, rather than highlighting their role in increasing the firm’s visibility.

  6. Elton (1999) identifies a few market phenomena that cast doubt on the belief that realized return can be averaged to proxy expected return. For example, from 1973 to 1984, realized returns on average were lower than the risk free rate; from 1927 to 1981, risky long-term bonds under-performed the risk free rate; and in the recent past, the US has had stock market returns of higher than 30% per year while Asian markets have had negative returns, although the US market should not have been riskier than the Asian market in this period.

  7. In their examination of the inter-temporal risk-return relationship, Pastor et al. (2008) show that with moderate assumptions, the implied cost of capital can be perfectly correlated with the conditional expected return over time.

  8. Stern Stewart provides information on WACC for the 1,000 best performing—in terms of market value added—companies.

  9. PEG stands for price/earnings/growth.

  10. Implementation of the Ohlson and Juettner-Nauroth (2005) model requires positive and increasing earnings forecasts (in order to impute positive growth rates). For firms where analysts’ long-term growth forecasts are not available, Gebhardt et al. (2001) also require that a firm have positive and increasing earnings forecasts for year t + 1 and t + 2. Easton’s (2004) PEG and MPEG approaches also require that earnings forecasts be both positive and increasing.

  11. We also use different deflators in computing advertising intensity as robustness checks. We find results with book-value- and market-value- adjusted advertising expenditures, but no results with sales-adjusted advertising expenditures.

  12. For example, all VL report in January, February, or March are assigned to the first calendar quarter of the year.

  13. The results are similar if we use the COC estimates in the fourth quarter of the same calendar year or the average value of the quarterly COC estimates within the same calendar year.

  14. They find that, over a 34-year period, the average return on stocks with high sensitivities to liquidity exceeds that for stocks with low sensitivities by 7.5% annually, adjusted for exposures to the market return as well as size, value, and momentum factors.

  15. The specification is as follows:

    $$ {\frac{{{\text{TCA}}_{\text{it}} }}{{{\text{Assets}}_{\text{it}} }}} = \phi_{0} + \phi_{1} {\frac{{{\text{CFO}}_{{{\text{it}} - 1}} }}{{{\text{Assets}}_{\text{it}} }}} + \phi_{2} {\frac{{{\text{CFO}}_{\text{it}} }}{{{\text{Assets}}_{\text{it}} }}} + \phi_{3} {\frac{{{\text{CFO}}_{{{\text{it}} + 1}} }}{{{\text{Assets}}_{\text{it}} }}} + \nu_{\text{it}} $$

    where TCA is total current accruals (=∆CA − ∆CL − ∆Cash + ∆STDEBT). Assets are total assets (#6); CFO is cash flows from operations computed as net income before extraordinary items (#18) minus total accruals (=∆CA − ∆CL − ∆Cash + ∆STDEBT-Dep); ∆CA is change in current assets (item #4) between year t − 1 and year t; ∆CL is change in current liabilities (item #5); ∆Cash is change in cash (item #1); ∆STDEBT is change in debt in current liabilities (item #34); Dep is depreciation and amortization expense (#14).

  16. The opposite trend in advertising intensity and ex ante cost-of-capital estimate over time will not affect our inferences, because we are testing their cross-sectional relationship.

  17. To account for the serially correlated dividend expectation, we also estimate a pooled regression and assess statistical inference using Newey and West’s (1987) standard errors, which control for unspecified heteroscedasticity and autocorrelation effects. Results from this procedure yield similar inferences.

  18. To see this, assume that a constant growth model holds and that the spread between the cost of equity and the permanent dividend growth rate is no greater than 5%. Denote V 0(R 0) as the firm value (cost-of-equity capital) before the increase in advertising intensity and V 1(R 1) as the firm value (cost-of-equity capital) after the increase in advertising intensity. Assume no changes in expected dividends and a constant dividend growth rate of g. Then V 1/V 0 = (R 0 − g)/(R 1 − g) = 1+(R 0 − R 1)/(R 1 − g) ≥ 1 + 0.36%/5% = 1.072.

  19. In particular, we follow Erickson and Jacobson (1992) in including the following variables as additional instruments: the natural logarithm of total assets, firm age, leverage, the firm-level abnormal return-to-equity, the industry-level abnormal return-to-equity, the Herfindahl index on sales within an industry, and the industry dummies as classified by Fama and French (1997).

  20. Estimation details are provided in the appendix.

  21. The results do not change if we use the median values of the four estimates in the tests.

  22. Hail and Leuz (2006) argue that the estimation of the implied cost-of-capital models makes simplified assumptions about long-term growth rates beyond the explicit forecast horizons of analysts. These assumptions are likely to have measurement errors that could confound the results.

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Acknowledgments

We are grateful to Alon Brav for access to estimates of implied cost-of-equity capital based on Value Line reports. The authors appreciate the comments from Cheng Few Lee (the editor), two anonymous reviewers, John Wei, Chu Zhang, Guochang Zhang and seminar participants at the Hong Kong Polytechnic University. The authors acknowledge the financial support of the Hong Kong Polytechnic University Research Grant (A-PA7J).

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  1. School of Accounting and Finance, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Yuan Huang & Steven X. Wei

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  1. Yuan Huang
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Correspondence to Yuan Huang.

Appendix: Estimation of cost-of-equity capital

Appendix: Estimation of cost-of-equity capital

To ease the discussion, we first define the variables used in the following four models:

P t :

Market price of a firm’s common stock at time t. We use the price at month +4 after the latest fiscal year end

B t :

Book value of equity from the most recent available financial statement at time t

FEPS t+i :

Median forecasted EPS from I/B/E/S or derived EPS forecasts for next ith year at time t

POUT:

Forecasted dividends payout ratio. We use the ratio of the indicated annual dividends from I/B/E/S to FEPSt+1 to measure the forecasted payout ratio. If FEPSt+1 is negative, we assume a return of assets of 6% to calculate earnings. If the indicated annual dividends are missing in I/B/E/S, we use four times the cash dividends paid in the previous fiscal quarter instead. POUT is winsorized between 0 and 1.

1.1 Rgls: Gebhardt et al. (2001)

$$ P_{t}^{*} = B_{t} + \sum\limits_{i = 1}^{T - 1} {{\frac{{\left[ {{\text{FROE}}_{t + i} - R_{\text{GLS}} } \right] \times B_{t + i - 1} }}{{\left( {1 + R_{\text{GLS}} } \right)^{i} }}}} + {\frac{{\left[ {{\text{FROE}}_{t + T} - R_{\text{GLS}} } \right] \times B_{t + T - 1} }}{{\left( {1 + R_{\text{GLS}} } \right)^{T - 1} R_{\text{GLS}} }}} $$
(4)

We explicitly use I/B/E/S analysts’ forecasts to proxy for the market expectation of a firm’s earnings for the next 3 years. Thereafter, the model measures the market earnings expectation by assuming that the future return on equity declines linearly to an equilibrium return on equity from the fourth year to the Tth year. This equilibrium return on equity is measured by a historical, 10-year, industry-specific median return on equity. The return on equity (ROE) is calculated as income available for common shareholders (Compustat data item #237) scaled by the lagged total book value of equity (Compustat data item #60). We classify all firms into 48 industries as defined by Fama and French (1997). Firm-year observations with a negative return on equity are eliminated in the calculation. The future book value of equity is estimated by assuming the clean surplus relation, that is, B t+1 = B t  + EPS t+1 − DPS t+1. The future dividend, DPS t+i , is calculated by multiplying EPS t+i by POUT. We assume that T = 12. We use a numerical approximation program to solve for Rgls that equates the right- and left-hand sides of Eq. (4) within a $0.001 difference.

1.2 Rct: Claus and Thomas (2001)

This model is similar to the one introduced by Gebhardt et al. (2001), except that it assumes that the abnormal earnings after 5 years grow at a rate of (1 + g lt) in perpetuity, where g lt is analysts’ long-term growth forecast. Under this assumption, we have

$$ P_{t}^{*} = B_{t} + \sum\limits_{i = 1}^{5} {{\frac{{\left[ {{\text{FEPS}}_{t + i} - R_{\text{CT}} \times B_{t + i - 1} } \right]}}{{\left( {1 + R_{\text{CT}} } \right)^{i} }}}} + {\frac{{\left[ {{\text{FEPS}}_{t + 5} - R_{\text{CT}} \times B_{t + 4} } \right] \times \left( {1 + g_{\text{lt}} } \right)}}{{\left( {R_{\text{CT}} - g_{\text{lt}} } \right)\left( {1 + R_{\text{CT}} } \right)^{5} }}} $$
(5)

To implement the model, we explicitly use the I/B/E/S earnings forecasts to derive the abnormal earnings for the next 5 years. Earnings forecasts for the future fourth and fifth years are derived from earnings forecasts for the future third year and the long-term earnings growth rate. If the latter is missing in I/B/E/S, an implied earnings growth rate from EPS t+2 and EPS t+3 is used instead. The long-term abnormal earnings growth rate is calculated using the contemporaneous risk-free rate (the yield on 10-year treasury bonds) minus 3%. The future book value of equity is also estimated by assuming the clean surplus relation, that is, B t+1 = B t  + EPS t+1 − DPS t+1. The future dividend, DPSt+i, is calculated by multiplying EPSt+i by the payout ratio, POUT. We use a numerical approximation program to solve for Rct that equates the right- and left-hand sides of Eq. (5) within a $0.001 difference.

1.3 Roj: Ohlson and Juettner-Nauroth (2005) implemented by Gode and Mohanram (2003)

$$ P_{t}^{*} = {\frac{{E_{t} \left( {{\text{EPS}}_{t + 1} } \right)}}{{R_{\text{OJ}} }}} + {\frac{{E_{t} \left( {{\text{EPS}}_{t + 1} } \right)E_{t} \left[ {g_{\text{st}} - R_{\text{OJ}} \times \left( {1 - {\text{POUT}}} \right)} \right]}}{{R_{\text{OJ}} \left( {R_{\text{OJ}} - g_{\text{lt}} } \right)}}} $$
(6)

where \( g_{\text{st}} = {\frac{{{\text{EPS}}_{t + 2} - {\text{EPS}}_{t + 1} }}{{{\text{EPS}}_{t + 1} }}} \). The implementation requires EPS t+1 > 0 and EPS t+2 > 0.

The long-term asymptotic growth rate, g lt , is calculated using the contemporaneous risk-free rate (the yield on 10-year treasury bonds) minus 3%. We use a numerical approximation program to solve for Roj that equates the right- and left-hand sides of Eq. (6) within a $0.001 difference.

1.4 Rmpeg: Modified PEG ratio model by Easton (2004)

$$ P_{t}^{*} = {\frac{{E_{t} \left( {{\text{EPS}}_{t + 1} } \right)}}{{R_{\text{MPEG}} }}} + {\frac{{E_{t} \left( {{\text{EPS}}_{t + 1} } \right)E_{t} \left[ {g_{\text{st}} - R_{\text{MPEG}} \times \left( {1 - {\text{POUT}}} \right)} \right]}}{{R_{\text{MPEG}}^{2} }}} $$
(7)

We use a numerical approximation program to solve for Rmpeg that equates the right- and left-hand sides of Eq. (7) within a $0.001 difference. This model requires E t (EPSt+2) ≥ E t (EPSt+1) ≥ 0.

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Huang, Y., Wei, S.X. Advertising intensity, investor recognition, and implied cost of capital. Rev Quant Finan Acc 38, 275–298 (2012). https://doi.org/10.1007/s11156-011-0228-1

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