Abstract
For the valuation of fast growing innovative firms Schwartz and Moon (Financ Anal J 56:62–75, 2000), (Financ Rev 36:7–26, 2001) develop a fundamental valuation model where key parameters follow stochastic processes. While prior research shows promising potential for this model, it has never been tested on a large scale dataset. Thus, guided by economic theory, this paper is the first to design a large-scale applicable implementation on around 30,000 technology firm quarter observations from 1992 to 2009 for the US to assess this model. Evaluating the feasibility and performance of the Schwartz-Moon model reveals that it is comparably accurate to the traditional sales multiple with key advantages in valuing small and non-listed firms. Most importantly, however, the model is able to indicate severe market over- or undervaluation from a fundamental perspective. We demonstrate that a trading strategy based on our implementation has significant investment value. Consequently, the model seems suitable for detecting misvaluations as the dot-com bubble.
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Notes
Wall Street Journal (05/17/12): Facebook Prices IPO at Record Value.
Reuters (11/04/11): Groupon's IPO biggest by U.S. Web company since Google. Wall Street Journal (01/17/12): Zynga Chief Talks IPO, Lessons Learned. Wall Street Journal (06/11/11): Pandora Raises IPO's Size.
There are five more recent working papers on the Schwartz-Moon model which demonstrate the interest in the model. Dubreuille et al. (2011) and Baek et al. (2009) look at the valuations of IT firms, however, they use small samples of 76 and 6 observations, respectively. Moreover, they only cover one single year, i.e. 2003 and 2009, respectively. Ehrhardt and Merlaud (2004) and Baek et al. (2004) have even smaller samples with three and one firms only. Baule and Tallau (2009) have a different focus as they investigate the use of the Schwartz-Moon model in the context of option markets. They also have a very small sample of three firms and cover only the years 2003–2006. Consequently, none of these studies offers a test of the original model on a large cross section of firms and over a longer time period.
Requiring basic analyst data as one-year-, two-year-ahead sales and gross margin forecasts for our sample firms would reduce our sample by over 60%.
Wall Street Journal (12/27/99): Analyst Discovers the Order in Internet Stocks Valuation.
Assuming exponential decay, the half-life can be derived by solving the following equation for \( t_{h} :e^{{ - \kappa t_{h} }} = \frac{1}{2} \).
These parameters are the long term variable costs, the long term volatility of variable costs, the capital expenditure rate and the depreciation rate.
We start with the first quarter 1992 since we need eight quarters of accounting information from 1990; since then data availability is reasonably complete for all required items. Moreover, it sufficiently covers the inception of the industry as well as the peak and burst of the dot-com bubble as described in Bhattacharya et al. (2010).
Additionally, we considered market capitalization two and three-months following the date the financial statements refers to as well as mean values over six months following this date. Our results are not influenced by this decision.
We allow stocks to enter the portfolio even if they are already invested in. Restricting the multiple inclusion reduces the reported abnormal returns only slightly.
We thank an anonymous referee for this suggestion.
We also re-estimated all specifications employing linear feasible general least squares estimators and results (unreported, but available up on request) are qualitatively the same.
We thank an anonymous referee for this suggestion.
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Acknowledgments
We are very grateful to Georg Keienburg for his insightful suggestions and valuable comments. Moreover, we thank Thomas Hartmann-Wendels, Dieter Hess and Georg Keienburg for their work on an early draft of this study. This paper has also benefited from the comments of Jeff Abarbanell, John Hand, Dieter Hess, Thomas Hartmann-Wendels and seminar participants at the 2012 Midwest Finance Association Meeting, the 2012 European Accounting Association Annual Congress and the 2012 German Academic Association for Business Research Meeting.
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Appendices
Appendix 1: Variable definitions
No. | Label | Description | Measurement (abbreviations are Compustat mnemonics) |
---|---|---|---|
Critical parameters | |||
1 | \( \mu_{0} \) | = initial growth rate of revenues | \( = \frac{1}{7}\sum\limits_{t = 0}^{ - 6} {\ln (saleq_{t} /saleq_{t - 1} )} \) |
2 | \( \eta_{0} \) | = initial volatility of the sales growth rate | = \( \sqrt {\frac{1}{n - 1}\sum\nolimits_{j = 0}^{t} {(\widehat{{\varepsilon_{t - j} }} - \bar{\varepsilon })^{2} } } \), where \( \widehat{{\varepsilon_{j} }} \) are the estimated residuals of the AR(1) process: \( \mu_{t} = \alpha + \beta \mu_{t - 1} + \varepsilon_{t} \) |
3 | \( \varphi_{0} \) | = initial volatility of variable costs | = \( \sqrt {\frac{1}{n - 1}\sum\nolimits_{j = 0}^{t} {(\widehat{{\varepsilon_{t - j} }} - \bar{\varepsilon })^{2} } } \), where \( \widehat{{\varepsilon_{j} }} \) are the estimated residuals of an AR(1) process on the cost rate c = (cogsq + xsgaq)/saleq: \( c_{t} = \alpha + \beta c_{t - 1} + \varepsilon_{t} \) |
4 | \( \gamma_{0} \) | = initial variable cost | = \( \frac{1}{8}\sum\nolimits_{t = 0}^{ - 7} {\frac{{cogsq_{t} + xsgaq_{t} }}{{saleq_{t} }}} \) |
5 | \( \bar{\mu } \) | = long term sales growth rate | = 0.0075 |
6 | \( \bar{\gamma } \) | = industry median long term variable cost | = \( median_{sic3} \sum\nolimits_{t = 1970}^{T} {\frac{{cogs_{t} + xsga_{t} }}{{sale_{t} }}} , \, for \,T = 1992,\, \ldots ,\, 2009 \) |
7 | κ | = speed of adjustment | = \( median_{sic2} \left( { - \frac{1}{4}\ln \left( {\sum\nolimits_{t - 5}^{t - 8} {\frac{{saleq_{t} - saleq_{t - 1} }}{{saleq_{t - 1} }}} /\sum\nolimits_{t - 1}^{t - 4} {\frac{{saleq_{t} - saleq_{t - 1} }}{{saleq_{t - 1} }}} } \right)} \right) \) |
Uncritical parameters | |||
8 | R | = revenues | = saleq |
9 | X | = cash and cash equivalents | = cheq + rectq + acoq + tstkq − apq |
10 | L | = loss carry forward | = tlcf |
11 | P | = property, plant and equipment | = ppent + aoq |
12 | \( \sigma_{0} \) | = initial sales volatility | = \( \sqrt {\frac{1}{7}\sum\nolimits_{t = 0}^{ - 7} {\left( {\frac{{saleq_{t} - saleq_{t - 1} }}{{saleq_{t - 1} }} - \mu_{0} } \right)^{2} } } \) |
13 | \( \bar{\sigma } \) | = long term volatility | = 0.05 |
14 | \( \bar{\varphi } \) | = industry median long term volatility of variable costs | = \( median_{sic3} \left( {std_{t = 1970}^{T} \left( {\frac{{cogs_{t} + xsga_{t} }}{{sale_{t} }}} \right)} \right), \, for \,T = 1992,\, \ldots ,\, 2009 \) |
15 | F | = fix costs | = 0 |
16 | cr | = industry median capital expenditure rate | = \( median_{sic3\,t^{T} = 1970} \left( {\frac{{capx_{t} }}{{sale_{t} }}} \right), \, for \,T = 1992,\, \ldots ,\, 2009 \) |
17 | dp | = industry median depreciation rate | = \( median_{sic3\;t^{T} = 1970} \left( {\frac{{dp_{t} }}{{ppent_{t} + ao_{t} }}} \right), \,for\, T = 1992,\, \ldots ,\, 2009 \) |
18 | τ | = tax rate | = 0.35 |
19 | \( r_{f} \) | = risk free rate | = \( \root{4} \of {(1 + 0.055)} - 1 = 0.0135 \) |
20 | \( \lambda_{R} \) | = risk premium sales | = \( \rho_{{r_{M} ,sales}} \cdot \sigma_{{r_{M} }} = \frac{{Cov(r_{M} , sales)}}{{\sigma_{sales} }} \) |
21 | \( \lambda_{\mu } \) | = risk premium sales growth | = \( \rho_{{r_{M} ,\mu }} \cdot \sigma_{{r_{M} }} = \frac{{Cov(r_{M} , \mu )}}{{\sigma_{\mu } }} \) |
22 | \( \lambda_{\gamma } \) | = risk premium variable costs | = \( \rho_{{r_{M} ,\gamma }} \cdot \sigma_{{r_{M} }} = \frac{{Cov(r_{M} , \gamma )}}{{\sigma_{\gamma } }} \) |
M | = terminal value multiple | = 10 | |
\( EV_{t} \) | = company (entity) value | = \( price \cdot shrout + dlttq + dlcq \) | |
\( RNOA_{t} \) | = return on net operating assets | = \( \frac{{\mathop \sum \nolimits_{t = 1}^{ - 4} EBITQ_{t} }}{ppentq + actq - lctq} \) |
Appendix 2: Data sources
COMPUSTAT | |||||
---|---|---|---|---|---|
Quarterly data (q) | Annual data (a) | ||||
Item number | Mnemonic | Description | Item number | Mnemonic | Description |
#1 | xsgaq | Selling, general, and administrative expenses | #8 | ppent | PP&E (net)—total |
#2 | saleq | Sales (net) | #12 | sale | Sales (net) |
#5 | dpq | Depreciation and amortization | #14 | dp | Depreciation and amortization |
#21 | oibdpq | Operating income before depreciation (EBITDA) | #41 | cogs | Cost of goods sold |
#30 | cogsq | Cost of goods sold | #52 | tlcf | Tax loss carry forward |
#36 | cheq | Cash and equivalents | #69 | ao | Assets—other |
#37 | rectq | Receivables—total | #128 | capx | Capital expenditures |
#39 | acoq | Current assets—other | #189 | xsga | Selling, general, and administrative expenses |
#40 | actq | Current assets—total | |||
#42 | ppentq | PP&E (net)—Total | |||
#43 | aoq | Assets—other | |||
#44 | atq | Assets—total | |||
#45 | dlcq | Debt in current liabilities | |||
#46 | apq | Accounts payable | |||
#49 | lctq | Current liabilities—total | |||
#51 | dlttq | Long-term debt—total | |||
#54 | ltq | Liabilities—total | |||
#58 | req | Retained earnings—quarterly | |||
#59 | ceqq | Common equity–total | |||
#69 | niq | Net income (loss) | |||
#98 | tstkq | Treasury Stock—dollar amount—total |
CRSP | |||||
---|---|---|---|---|---|
Monthly data | |||||
n.a. | Price | Stock price (adjusted for stock splits etc.) | |||
n.a. | Shrout | Shares outstanding (adjusted for stock splits etc.) |
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Klobucnik, J., Sievers, S. Valuing high technology growth firms. J Bus Econ 83, 947–984 (2013). https://doi.org/10.1007/s11573-013-0684-2
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DOI: https://doi.org/10.1007/s11573-013-0684-2