Valuing high technology growth firms

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.

This is a preview of subscription content, access via your institution.

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Notes

  1. 1.

    Wall Street Journal (05/17/12): Facebook Prices IPO at Record Value.

  2. 2.

    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.

  3. 3.

    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.

  4. 4.

    In fact, taking a closer look at recent valuation model accuracy studies such as Liu et al. (2002) or Bhojraj and Lee (2002), most of them exclude all firms that do not fulfill criteria such as positive earnings, analyst coverage, share price larger than $3 and minimum sales of $100 million.

  5. 5.

    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%.

  6. 6.

    Wall Street Journal (12/27/99): Analyst Discovers the Order in Internet Stocks Valuation.

  7. 7.

    For a controversial debate on the effect of default risk on firm value we refer to Homburg et al. (2004, 2005), Kruschwitz et al. (2005) and Rapp (2006).

  8. 8.

    Assuming exponential decay, the half-life can be derived by solving the following equation for \( t_{h} :e^{{ - \kappa t_{h} }} = \frac{1}{2} \).

  9. 9.

    These parameters are the long term variable costs, the long term volatility of variable costs, the capital expenditure rate and the depreciation rate.

  10. 10.

    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).

  11. 11.

    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.

  12. 12.

    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.

  13. 13.

    We thank an anonymous referee for this suggestion.

  14. 14.

    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.

  15. 15.

    We thank an anonymous referee for this suggestion.

References

  1. Abarbanell JS, Bushee BJ (1998) Abnormal returns to a fundamental analysis strategy. Acc Rev 73(1):19–45

    Google Scholar 

  2. Alford AW (1992) The effect of the set of comparable firms on the accuracy of the price-earnings valuation method. J Acc Res 30(1):94–108

    Article  Google Scholar 

  3. Armstrong C, Davila A, Foster G (2006) Venture-backed private equity valuation and financial statement information. Rev Acc Stud 11(1):119–154

    Article  Google Scholar 

  4. Baek C, Dupoyet BV, Prakash AJ (2004) Debt and equity valuation of IT companies: a real option approach. SSRN eLibrary http://ssrn.com/paper=627064

  5. Baek C, Dupoyet BV, Prakash AJ (2009) Fundamental capital valuation for IT companies: a real options approach. SSRN eLibrary http://ssrn.com/paper=1512523

  6. Baker M, Wurgler J (2006) Investor sentiment and the cross-section of stock returns. J Finance 61(4):1645–1680

    Article  Google Scholar 

  7. Baker M, Wurgler J (2007) Investor sentiment in the stock market. J Econ Perspect 21(2):129–151

    Article  Google Scholar 

  8. Ballwieser W (2011) Unternehmensbewertung: Prozeß, Methoden und Probleme. Schäffer-Poeschel, 3. Aufl., Stuttgart

    Google Scholar 

  9. Bartov E, Mohanram P, Seethamraju C (2002) Valuation of Internet stocks—an IPO perspective. J Acc Res 40(2):321–346

    Article  Google Scholar 

  10. Baule R, Tallau C (2009) Stock price dynamics of listed growth companies: evidence from the options market. SSRN eLibrary http://ssrn.com/paper=903375

  11. Bauman MP, Das S (2004) Stock market valuation of deferred tax assets: evidence from Internet Firms. J Bus Finance Acc 31:1223–1260

    Article  Google Scholar 

  12. Bhattacharya N, Demers EA, Joos P (2010) The relevance of accounting information in a stock market bubble: evidence from internet IPOs. J Bus Finance Acc 37(3–4):291–321

    Google Scholar 

  13. Bhojraj S, Lee CMC (2002) Who is my peer? A valuation-based approach to the selection of comparable firms. J Acc Res 40(2):407–439

    Article  Google Scholar 

  14. Carhart MM (1997) On persistence in mutual fund performance. J Finance 52(1):57–82

    Article  Google Scholar 

  15. Coakley J, Fuertes A-M (2006) Valuation ratios and price deviations from fundamentals. J Bank Finance 30(8):2325–2346

    Article  Google Scholar 

  16. Core JE, Guay WR, Buskirk AV (2003) Market valuations in the new economy: an investigation of what has changed. J Acc Econ 34(1–3):43–67

    Article  Google Scholar 

  17. Cumming DJ (2008) Contracts and exits in venture capital finance. Rev Financ Stud 21(5):1947–1982

    Article  Google Scholar 

  18. Cumming DJ, MacIntosh JG (2003) A cross-country comparison of full and partial venture capital exists. J Bank Finance 27(3):511–548

    Article  Google Scholar 

  19. Dechow PM, Amy PH, Richard GS (1999) An empirical assessment of the residual income valuation model. J Acc Econ 26(1):1–34

    Google Scholar 

  20. Demers E, Lev B (2001) A rude awakening: internet Shakeout in 2000. Rev Acc Stud 6(2/3):331–359

    Article  Google Scholar 

  21. Denrell J (2004) Random walks and sustained competitive advantage. Manage Sci 50(7):922–934

    Article  Google Scholar 

  22. Drukarczyk J, Schüler A (2007) Unternehmensbewertung. Vahlen, 5. Aufl., München

  23. Dubreuille S, Lleo S, Mchawrab S (2011) Schwartz and Moon valuation model: evidence from IT companies. SSRN eLibrary http://ssrn.com/paper=1871867

  24. Easterwood JC, Nutt SR (1999) Inefficiency in analysts’ earnings forecasts: systematic misreaction or systematic optimism? J Finance 54(5):1777–1797

    Article  Google Scholar 

  25. Ehrhardt O, Merlaud V (2004) Bewertung von Wachstumsunternehmen mit der DCF-Methode und dem Schwartz/Moon-Realoptionsmodell: eine Fallstudie aus der Halbleiterbranche. FinanzBetrieb 6:777–785

    Google Scholar 

  26. Endlich L (2004) Optical illusions: lucent and the crash of telecom. Simon & Schuster Verlag, London

    Google Scholar 

  27. Fama EF, Kenneth RF (1993) Common risk-factors in the returns on stocks and bonds. J Financ Econ 33(1):3–56

    Article  Google Scholar 

  28. Finter P, Niessen-Ruenzi A, Ruenzi S (2012) The impact of investor sentiment on the German stock market. Zeitschrift für Betriebswirtschaft 82:133–163

    Article  Google Scholar 

  29. Hand JRM (2005) The value relevance of financial statements in the venture capital market. Acc Rev 80(2):613–648

    Article  Google Scholar 

  30. Harrison JM, David MK (1979) Martingales and arbitrage in multiperiod securities markets. J Econ Theor 20(3):381–408

    Article  Google Scholar 

  31. Homburg C, Stephan J, Weiß M (2004) Unternehmensbewertung bei atmender Finanzierung und Insolvenzrisiko. Die Betriebswirtschaft 64:276–295

    Google Scholar 

  32. Homburg C, Jörg S, Matthias W (2005) Zur Bedeutung des Insolvenzrisikos im Rahmen von DCF-Bewertungen: Replik auf die Stellungnahme von Thomas Hering zum Beitrag—Unternehmensbewertung bei atmender Finanzierung und Insolvenzrisiko. Die Betriebswirtschaft 65:199–203

    Google Scholar 

  33. Husmann S, Kruschwitz L, Löffler A (2002) Unternehmensbewertung unter deutschen Steuern. Die Betriebswirtschaft 62:24–42

    Google Scholar 

  34. Husmann S, Kruschwitz L, Löffler A (2006) WACC and a generalized tax code. Eur J Finance 12(1):33–40

    Article  Google Scholar 

  35. Iman RL, Conover WJ (1979) The use of the rank transform in regression. Technometrics 21(4):499–509

    Article  Google Scholar 

  36. Inderst R, Muller HM (2004) The effect of capital market characteristics on the value of start-up firms. J Financ Econ 72(2):319–356

    Article  Google Scholar 

  37. Kapadia N (2011) Tracking down distress risk. J Financ Econ 102(1):167–182

    Article  Google Scholar 

  38. Keiber K, Kronimus A, Rudolf M (2002) Bewertung von Wachstumsunternehmen am Neuen Markt. Zeitschrift für Betriebswirtschaft 72:735–764

    Google Scholar 

  39. Keim DB, Ananth M (1998) The cost of institutional equity trades. Financ Anal J 54(4):50–69

    Article  Google Scholar 

  40. Kile CO, Mary EP (2009) Using industry classification codes to sample high-technology firms: analysis and recommendations. J Acc Audit Finance 24:35–58

    Google Scholar 

  41. Krafft M, Rudolf M, Rudolf-Sipötz E (2005) Valuation of costumers in growth companies—a scenario based model. Schmalenbach Bus Rev 57:103–127

    Google Scholar 

  42. Kruschwitz L, Löffler A (2005) Discounted cash flow. A theory of the valuation of firms. Wiley Finance, Chichester

    Google Scholar 

  43. Kruschwitz L, Lodowicks A, Löffler A (2005) Zur Bewertung insolvenzbedrohter Unternehmen. Die Betriebswirtschaft 65:221–236

    Google Scholar 

  44. Liu W, Strong N (2008) Biases in decomposing holding period portfolio returns. Rev Financ Stud 21(5):2243–2274

    Article  Google Scholar 

  45. Liu J, Nissim D, Thomas J (2002) Equity valuation using multiples. J Acc Res 40(1):135–172

    Article  Google Scholar 

  46. Lucas RE Jr (1967) Adjustment costs and theory of supply. J Polit Econ 75(4):321–334

    Article  Google Scholar 

  47. Lundholm R, O’Keefe T (2001) Reconciling value estimates from the discounted cash flow model and the residual income model. Contemp Acc Res 18(2):311–335

    Article  Google Scholar 

  48. Mansfield E (1985) How rapidly does new industrial technology leak out? J Ind Econ 34(2):217–223

    Article  Google Scholar 

  49. McGrath RG (1997) A real options logic for initiating technology positioning investments. Acad Manag Rev 22(4):974–996

    Google Scholar 

  50. Miller MH (1977) Debt and taxes. J Finance 32(2):261–275

    Google Scholar 

  51. Modigliani F, Miller MH (1958) The cost of capital, corporation finance and the theory of investment. Am Econ Rev 48(3):261–297

    Google Scholar 

  52. Mueller DC (1977) The Persistence of profits above the norm. Economica 44(176):369–380

    Article  Google Scholar 

  53. Ofek E, Richardson MP (2003) DotCom mania: the rise and fall of internet stock prices. J Finance 58(3):1113–1137

    Article  Google Scholar 

  54. Pástor L, Veronesi P (2003) Stock valuation and learning about profitability. J Finance 58(5):1749–1790

    Article  Google Scholar 

  55. Pástor L, Veronesi P (2006) Was there a Nasdaq bubble in the late 1990s? J Financ Econ 81(1):61–100

    Article  Google Scholar 

  56. Petersen MA (2009) Estimating standard errors in finance panel data sets: comparing approaches. Rev Financ Stud 22(1):435–480

    Article  Google Scholar 

  57. Rapp MS (2006) Die arbitragefreie Adjustierung von Diskontierungssätzen bei einfacher Gewinnsteuer. Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung 58(6):771–806

    Google Scholar 

  58. Ross SA (1985) Debt and taxes and uncertainty. J Finance 40(3):637–657

    Article  Google Scholar 

  59. Schwartz ES, Moon M (2000) Rational pricing of internet companies. Financ Anal J 56(3):62–75

    Article  Google Scholar 

  60. Schwartz ES, Moon M (2001) Rational Pricing of Internet Companies Revisited. Financial Review 36(4):7–26

    Article  Google Scholar 

  61. Simon HA, Bonini CP (1958) The size distribution of business firms. Am Econ Rev 48(4):607–617

    Google Scholar 

  62. Stambaugh RF, Yu J, Yu Y (2012) The short of it: investor sentiment and anomalies. J Financ Econ 104(2):288–302

    Article  Google Scholar 

  63. Trueman B, Franco Wong MH, Zhang X-J (2000) The eyeballs have it: searching for the value in internet stocks. J Acc Res 38(3):137–162

    Article  Google Scholar 

  64. Trueman B, Franco Wong MH, Zhang X-J (2001) Back to basics: forecasting the revenues of internet firms. Rev Acc Stud 6(2/3):305–329

    Article  Google Scholar 

  65. Vassalou M, Xing YH (2004) Default risk in equity returns. J Finance 59(2):831–868

    Article  Google Scholar 

  66. Waring GF (1996) Industry differences in the persistence of firm-specific returns. Am Econ Rev 86(5):1253–1265

    Google Scholar 

  67. Zingales L (2000) In search of new foundations. J Finance 55(4):1623–1653

    Article  Google Scholar 

Download references

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.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jan Klobucnik.

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.)    

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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

Download citation

Keywords

  • Schwartz-Moon model
  • Market mispricing
  • Empirical test
  • Company valuation
  • Trading strategy

JEL classification

  • G11
  • G12
  • G17
  • G33