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Innovation and stock market performance: A model with ambiguity-averse agents

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Abstract

Empirical evidence on stock prices shows that firms investing successfully in radical innovation experience higher stock returns. This paper provides a model that sheds light on the relationship between the degree of firm innovativeness and stock returns, the movements of which capture expectations on firm’s profitability and growth. The model is grounding on Neo-Schumpeterian growth models and relies on the crucial assumption of radical innovation, characterized by “ambiguity” or Knightian uncertainty: due to its uniqueness and originality, no distribution of probability can be reasonably associated with radical innovation success or failure. Different preferences (α-maxmin, Choquet) are here compared. Results show that the assumption of ambiguity-aversion is crucial in determining higher returns in the presence of radical innovation and that the specific definition of expected utility shapes the extent of the returns. This result holds also in the case of endogenous innovation; risk attitude plays no role.

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Notes

  1. Demand for labor is omitted because we focus on demand for capital since innovation occurs through the introduction of a new capital good.

  2. The subjective expectations that agents formulate on probabilities are assumed to be “rational”: the assumption of rationality per se does not specify the exact expectations that people hold but asserts that agents hold objectively correct expectations conditional on the information they possess (Manski 2004). More on this in footnote 3.

  3. An expert agent might be capable to formulate subjective distributional probability also in highly uncertain environments (Cooke 1991; O'Hagan et al. 2006), relying on sophisticated techniques such as, for instance, stochastic programming (e.g. Keppo and van der Zwaan 2012) or the determination of the option value of an innovation (e.g. Siddiqui et al. 2007). However, in practice an expert can only make a finite number (and usually a rather small number) of statements of belief about a random variable (Garthwaite et al. 2005): even when familiar with probabilities and their meaning, experts might face difficulties in assessing a probability value for an event accurately.

  4. As emphasized in Einhorn and Hogarth (1982), in ambiguous situations people use an anchoring-and-adjustment strategy in which an initial probability is used as the anchor, and adjustments are made for ambiguity. This means that, for instance, receiving new information reduces ambiguity because reduces the range of possible outcomes without changing the anchor probability.

  5. Gambardella (1995) compares the “random search” phase with the “guided search” phase of the pharmaceutical industry, and provides some insight on why there may be less uncertainty associated with high innovation.

References

  • Adams ME, Day GS, Dougherty D (1998) Enhancing new product development performance: An organizational learning perspective. J Prod Innov Manag 15(5):403–422

    Article  Google Scholar 

  • Aizenman J (1997) Investment in new activities and the welfare cost of uncertainty. J Dev Econ 52:259–277

    Article  Google Scholar 

  • Athanassoglou S, Bosetti V, DeMaere G (2012) Ambiguous aggregation of expert opinions: The case of optimal R&D investment. FEEM Working Paper n. 004

  • Battaggion MR, Grieco D (2009) Radical Innovation and R&D Competition. Rivista italiana degli economisti 14(2):345–360

    Google Scholar 

  • Beck M, Lopes Bento C, Schenker-Wicki A (2016) Radical or incremental: Where does R&D policy hit? Res Policy (in press)

  • Butler JV, Guiso L, Jappelli T (2011) The role of intuition and reasoning in driving aversion to risk and ambiguity. CEPR Discussion Paper No. DP8334. Available at SSRN: http://ssrn.com/abstract=1810297

  • Camerer C, Weber M (1992) Recent developments in modeling preferences: Uncertainty and ambiguity. J Risk Uncertain 5(4):325–370

    Article  Google Scholar 

  • Campbell JY (2000) Asset pricing at the millennium. J Financ 55:1515–1567

    Article  Google Scholar 

  • Campbell JY, Shiller RJ (1984) A Simple Account of the Behavior of Long-Term Interest Rates. Am Econ Rev 74(2):44–48

    Google Scholar 

  • Campbell JY, Shiller RJ (1988) Stock prices, earnings, and expected dividends. J Financ 43(3):661–676

    Article  Google Scholar 

  • Campbell JY, Lettau M, Malkiel BG, Yexiao X (2000) Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk. J Financ 56:1–43

    Article  Google Scholar 

  • Chesson HW, Viscusi WK (2003) Commonalities in time and ambiguity aversion for long-term risks. Theor Decis 54(1):57–71

    Article  Google Scholar 

  • Choquet G (1955) Theory of capacities. Annual Institute Fourier (Grenoble) 5:131–295

    Article  Google Scholar 

  • Cooke RM (1991) Experts in uncertainty: opinion and subjective probability in science. New York: Oxford University Press

  • Dewar RD, Dutton JE (1986) The adoption of radical and incremental changes: An empirical analysis. Manag Sci 32(11):1422–1433

    Article  Google Scholar 

  • Dixit AK, Stiglitz JE (1977) Monopolistic competition and optimum product diversity. Am Econ Rev 67(3):297–308

    Google Scholar 

  • Dow J, da Costa Werlang SR (1992) Excess volatility of stock prices and Knightian uncertainty. Eur Econ Rev 36(2):631–638

    Article  Google Scholar 

  • Dow J, Werlang SRC (1992) Uncertainty aversion, risk aversion, and the optimal choice of portfolio. Econometrica 60:197–204

    Article  Google Scholar 

  • Einhorn HJ, Hogarth RM (1982) Prediction, diagnosis, and causal thinking in forecasting. J Forecast 1(1):23–36

    Article  Google Scholar 

  • Ellsberg D (1961) Risk, ambiguity, and the Savage axioms. Q J Econ 75(4):643–669

    Article  Google Scholar 

  • Epstein LG, Schneider M (2010) Ambiguity and asset markets. Annu Rev Financ Econ 2(1):315–346

    Article  Google Scholar 

  • Epstein LG, Wang T (1994) Intertemporal asset pricing under Knightian uncertainty. Econometrica 62(2):283–322

  • Ethier WJ (1982) National and international returns to scale in the modern theory of international trade. Am Econ Rev 72(3):389–405

    Google Scholar 

  • Fleming L (2001) Recombinant uncertainty in technological search. Manag Sci 47(1):117–132

    Article  Google Scholar 

  • Gambardella A (1995) Science and Innovation: the US Pharmaceutical Industry During the 1980s. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Garcia R, Calantone R (2002) A critical look at technological innovation typology and innovativeness terminology: a literature review. J Prod Innov Manag 19(2):110–132

    Article  Google Scholar 

  • Garthwaite PH, Kadane JB, O'Hagan A (2005) Statistical methods for eliciting probability distributions. J Am Stat Assoc 100(470):680–701

    Article  Google Scholar 

  • Ghirardato P, Marinacci M (2001) Risk, ambiguity, and the separation of utility and beliefs. Math Oper Res 26:864–890

    Article  Google Scholar 

  • Ghirardato P, Marinacci M (2002) Ambiguity made precise: A comparative foundation. J Econ Theory 102(2):251–289

    Article  Google Scholar 

  • Ghirardato P, Maccheroni F, Marinacci M (2004) Differentiating ambiguity and ambiguity attitude. J Econ Theory 118:133–173

    Article  Google Scholar 

  • Gilboa I (ed) (2004) Uncertainty in Economic Theory: Essays in Honor of David Schmeidler’s 65th Birthday. Routledge, London

    Google Scholar 

  • Gilboa I, Marinacci M (2011) Ambiguity and the Bayesian paradigm. Working Papers 379, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University

  • Gilboa I, Schmeidler D (1989) Maxmin expected utility with non-unique prior. J Math Econ 18(2):141–153

    Article  Google Scholar 

  • Grossman GM, Helpman E (1991) Quality ladders in the theory of growth. Rev Econ Stud 58(1):43–61

    Article  Google Scholar 

  • Guiso L, Sapienza P, Zingales L (2008) Trusting the stock market. the. J Financ 63(6):2557–2600

    Article  Google Scholar 

  • Henderson R (1993) Underinvestment and incompetence as responses to radical innovation: Evidence from the photolithographic alignment equipment industry. RAND J Econ 24:248–270

    Article  Google Scholar 

  • Henderson RM, Clark KB (1990) Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms. Adm Sci Q 35:9–32

    Article  Google Scholar 

  • Jones LE, Manuelli R (1990) A convex model of equilibrium growth. NBER Working Papers 3241, National Bureau of Economic Research

  • Jovanovic B, Greenwood J (1999) The IT revolution and the stock market. NBER Working Papers 6931, National Bureau of Economic Research

  • Jovanovic B, MacDonald G (1994) The life-cycle of a competitive industry. NBER Working Papers 4441, National Bureau of Economic Research

  • Kaluzny A, Veney JE, Gentry JT (1972) Innovation of health services: A comparative study of hospitals and health departments. Q Health Soc 52(1):51–82

    Google Scholar 

  • Keppo I, van der Zwaan B (2012) The impact of uncertainty in climate targets and CO2 storage availability on long-term emissions abatement. Environ Model Assess 17(1–2):177–191

    Article  Google Scholar 

  • Klibanoff P, Marinacci M, Mukerji S (2005) A smooth model of decision making under ambiguity. Econometrica 73(6):1849–1892

    Article  Google Scholar 

  • Klibanoff P, Marinacci M, Mukerji S (2009) Recursive smooth ambiguity preferences. J Econ Theory 144(3):930–976

    Article  Google Scholar 

  • Knight FH (1921) Risk, Uncertainty and Profit. Harper and Row, New York

    Google Scholar 

  • Knox TA (2003) Foundations for learning how to invest when returns are uncertain. Working paper, University of Chicago Graduate School of Business

  • Leifer R, Colarelli O'Connor G, Rice M (2001) Implementing radical innovation in mature firms: The role of hubs. Acad Manag Rev 3:102–113

    Google Scholar 

  • Lucas RE (1988) On the mechanisms of economic development. J Monet Econ 22(1):3–42

    Article  Google Scholar 

  • Machina MJ (1982) Expected utility analysis without the Independence Axiom. Econometrica 50:277–323

    Article  Google Scholar 

  • Manski CF (2004) Measuring expectations. Econometrica 72(5):1329–1376

    Article  Google Scholar 

  • Mazzucato M (2003) Risk, variety and volatility: Innovation, growth and stock prices in old and new industries. J Evol Econ 13(5):491–512

    Article  Google Scholar 

  • Mazzucato M (2006) Innovation and stock prices: A review of some recent work. Revue de L’Observatoire Francais de Conjonctures Economiques, Specia

  • Mazzucato M, Semmler W (1999) Stock market volatility and market share instability during the US auto industry life-cycle. J Evol Econ 9(1):67–96

    Article  Google Scholar 

  • Mazzucato M, Tancioni M (2005) Innovation and Idiosyncratic Risk. Computing in Economics and Finance, 81, Society for Computational Economics

  • Mazzucato M, Tancioni M (2008) Innovation and idiosyncratic risk: an industry-and firm-level analysis. Ind Corp Chang 17(4): 779–811

    Article  Google Scholar 

  • Mazzucato M, Tancioni M (2012) R&D, patents and stock return volatility. J Evol Econ 22:811–832

    Article  Google Scholar 

  • Nord WR, Tucker S (1987) Implementing Routine and Radical Innovations. Lexington Books, Lexington

    Google Scholar 

  • O'Connor GC (1998) Market learning and radical innovation: A cross case comparison of eight radical innovation projects. J Prod Innov Manag 15(2):151–166

    Article  Google Scholar 

  • O'Hagan A, Buck CE, Daneshkhah A, Eiser JR, Garthwaite PH, Jenkinson DJ, Rakow T (2006) Uncertain Judgements: Eliciting Experts' Probabilities. Wiley, New York

    Book  Google Scholar 

  • Pakes A (1982) On the asymptotic bias of the Wald-type estimators of a straight line when both variables are subject to error. Int Econ Rev 23:491–497

    Article  Google Scholar 

  • Pastor L, Veronesi P (2003) Stock valuation and learning about profitability. J Financ 58:1749–1790

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Rebelo S (1991) Long-run policy analysis and long-run growth. J Polit Econ 99(3):500–521

    Article  Google Scholar 

  • Rivera-Batiz LA, Romer PM (1991) International trade with endogenous technological change. Eur Econ Rev 35(4):971–1001

    Article  Google Scholar 

  • Romer PM (1986) Increasing returns and long-run growth. J Polit Econ 94:1002–1038

    Article  Google Scholar 

  • Romer PM (1987) Growth based on increasing returns due to specialization. Am Econ Rev 7(2):56–72

    Google Scholar 

  • Romer PM (1990) Endogenous technological change. J Polit Econ 98(5):71–102

    Article  Google Scholar 

  • Romer P (1994) New goods, old theory, and the welfare costs of trade restrictions. J Dev Econ 43(1):5–38

    Article  Google Scholar 

  • Sarin RK, Weber M (1993) Effects of ambiguity in market experiments. Manag Sci 39:602–615

    Article  Google Scholar 

  • Savage LJ (1954) The Foundations of Statistics. Wiley, New York

    Google Scholar 

  • Schmeidler D (1989) Subjective probability and expected utility without additivity. Econometrica 57(3):571–583

    Article  Google Scholar 

  • Shiller R (2000) Irrational Exuberance. Princeton University Press, Princeton

    Google Scholar 

  • Siddiqui AS, Marnay C, Wiser RH (2007) Real options valuation of US federal renewable energy research, development, demonstration, and deployment. Energ Policy 35(1):265–279

    Article  Google Scholar 

  • Sorescu A, Chandy R, Prabhu J (2003) Sources and financial consequences of radical innovation. J Mark 66:82–102

    Article  Google Scholar 

  • Trautmann ST, Vieider FM, Wakker PP (2008) Causes of ambiguity aversion: Known versus unknown preferences. J Risk Uncertain 36:225–243

    Article  Google Scholar 

  • Veronesi P (2000) How does information quality affect stock returns? J Financ 55(2):807–837

    Article  Google Scholar 

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  1. Department of Economics, Bocconi University, Via Roentgen 1, 20136, Milan, Italy

    Daniela Grieco

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Grieco, D. Innovation and stock market performance: A model with ambiguity-averse agents. J Evol Econ 28, 287–303 (2018). https://doi.org/10.1007/s00191-017-0537-1

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