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Postmodern Portfolio Theory

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Postmodern Portfolio Theory

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Abstract

Modern portfolio theory, its name notwithstanding, needs a thorough renovation. The reaction of an informed contemporary critic to this venerable model of financial analysis would be comparable to that of a postmodern architect who encounters the naked geometry of a Brutalist monument for the first time: the edifice has nice “bones,” so to speak, but it needs to be rebuilt with human needs and emotions in mind before anyone will live in it.1

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Notes

  1. 1.

    See, e.g., Christopher Alexander, Sara Ishikawa & Murray Silverstein, A Pattern Language: Towns, Buildings, Construction (1977); Christopher Alexander, The Timeless Way of Building (1979).

  2. 2.

    See John R. Graham & Campbell R. Harvey, The Theory and Practice of Corporate Finance: Evidence from the Field, 60 J. Fin. Econ. 187–243 (2001).

  3. 3.

    See Dirk Brounen, Abe DeJong & Kees G. Koedijk, Corporate Finance in Europe: Confronting Theory with Practice, 33 Fin. Mgmt. 71–101 (2004) (reporting that 45 percent of European financial practitioners relied on the CAPM); cf. Michael Keppler, Risiko Ist Nicht Gleich Volatilität, 11 Die Bank: Zeitschrift für Bankpolitik und Bankpraxis 610–614, 611 (1990) (“Die Anziehungskraft der Modernen Portfolio-Theorie scheint jedoch trotz der Fragwürdigkeit einiger ihrer grundlegenden Annahmen—insbesondere in der europäischen Investment-Szene—ungebrochen.”) (as translated into English by Keppler at http://www.kamny.com/load/publications/p03_eng: “Despite its obvious flaws, [modern portfolio theory] continues to enjoy wide popularity among investment professionals, particularly in Europe.”).

  4. 4.

    Compare Burton G. Malkiel, A Random Walk Down Wall Street (1996) with Andrew W. Lo & A. Craig MacKinlay, A Non-Random Walk Down Wall Street (2002).

  5. 5.

    See generally, e.g., M.F.M. Osborne, Brownian Motion in the Stock Market, 7 Operations Research 145–173 (1959).

  6. 6.

    See, e.g., Daniel C. Goldie & Gordon S. Murray, The Investment Answer 13–15 (2011); Carl Richards, The Behavior Gap: Simple Ways to Stop Doing Dumb Things with Money (2012).

  7. 7.

    Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable 279 (2007).

  8. 8.

    See Fischer Black & Myron S. Scholes, The Pricing of Options and Corporate Liabilities, 81 J. Pol. Econ. 637–654 (1973); Robert C. Merton, The Theory of Rational Option Pricing, 4 Bell J. Econ. 141–183 (1973).

  9. 9.

    See Robert C. Merton, On the Pricing of Corporate Debt: The Risk Structure of Interest Rates, 29 J. Fin. 449–470 (1974).

  10. 10.

    See Jorge Mina & Jerry Yi Xiao. Return to RiskMetrics: The Evolution of a Standard (2001); Jeremy Berkowitz & James O’Brien, How Accurate Are Value-at-Risk Models at Commercial Banks?, 57 J. Fin. 1093–1111 (2002).

  11. 11.

    See Roger B. Nelsen, An Introduction to Copulas (1999); David X. Liu, On Default Correlation: A Copula Function Approach, 9:4 J. Fixed Income 43–54 (March 2000).

  12. 12.

    See generally Benoit B. Mandelbrot & Richard L. Hudson, The (Mis)Behavior of Markets: A Fractal View of Risk, Ruin, and Reward (2004).

  13. 13.

    See generally Brian M. Rom & Kathleen W. Ferguson, Post-Modern Portfolio Theory Comes of Age, 2:4 J. Investing 27–33 (Winter 1993).

  14. 14.

    See, e.g., John Y. Campbell, Andrew W. Lo & A. Craig MacKinlay, The Econometrics of Financial Markets 17, 81, 172, 498 (1997). For an exploration of the implications of departures from Gaussian expectations in a variety of legal settings, see Daniel A. Farber, Probabilities Behaving Badly: Complexity Theory and Environmental Uncertainty, 37 U.C. Davis L. Rev. 145–173 (2003).

  15. 15.

    Mandelbrot & Hudson, supra note 12, at 18.

  16. 16.

    Fama & French, The Cross-Section of Expected Stock Returns, Chap. 2, supra note 55, at 438.

  17. 17.

    See generally Hiromitsu Kumamoto & Ernest J. Henley, Probabilistic Risk Assessment and Management for Engineers and Scientists (1996).

  18. 18.

    For an application of probabilistic risk assessment to the problem of estimating the average annual loss of buildings in Taipei to earthquakes, see Daigee Shaw, Chin-Hsun Yeh, Wen-Yu Jean, Chin-Hsiung Loh & Yen-Lien Kuo, A Probabilistic Seismic Risk Assessment of Building Losses in Taipei: An Application of HAZ-Taiwan with Its Pre-Processor and Post-Processor, 30 J. Chinese Inst. Eng’rs 289–297 (2007).

  19. 19.

    See Douglas W. Hubbard, The Failure of Risk Management 67 (2009) (distinguishing the simple assignment of probabilities in modern portfolio theory from more comprehensive structural analyses of risk in probabilistic risk assessment).

  20. 20.

    Id. These qualifications notwithstanding, Hubbard uses modern portfolio theory to measure the financial worth of anything that might be assigned a business value. See Douglas W. Hubbard, How to Measure Anything: Finding the Value of Intangibles in Business (2007).

  21. 21.

    See Roll, Chap. 2, supra note 48, at 136; see also Eugene F. Fama & James D. MacBeth, Risk, Return, and Equilibrium: Empirical Tests, 81 J. Pol. Econ. 607–636, 610 (1973).

  22. 22.

    Compare Roll, Chap. 2, supra note 48, at 138 (stating that tests of asset pricing theory assume that “the market portfolio must be identifiable”) with id. at 146 (noting the concession made by Marshall E. Blume & Irwin Friend, A New Look at the Capital Asset Pricing Model, 28 J. Fin. 19–34, 22–23 (1973), that the CAPM “cannot explain the observed returns of all financial assets,” but “may be … adequate … for a subset … such as common stocks on the NYSE”).

  23. 23.

    See Roll, Chap. 2, supra note 48, at 155 (speculating that a mixture of “bonds, human capital, and real estate in reasonable proportions” with “all-equity proxies” might better reflect the efficiency of the market, but conceding that this hypothesis is “not testable …, again for the simple reason that the true market portfolio has an unknown composition”).

  24. 24.

    Sergio M. Focadi & Frank J. Fabozzi, The Mathematics of Financial Modeling and Investment Management 521 (2004).

  25. 25.

    See id.

  26. 26.

    See, e.g., Travis J. Carter & Thomas Gilovich, The Relative Relativity of Material and Experiential Purchases, 98 J. Personality & Soc. Psych. 146–159 (2010); Ryan G. Howell, Paulina Pchelin & Ravi Iyer, The Preference for Experiences over Possessions: Measurement and Construct Validation of the Experiential Buying Tendency Scale, 7 J. Positive Psych. 57–71 (2012); Murray Millar & Rebecca Thomas, Discretionary Activity and Happiness: The Role of Materialism, 43 J. Research in Personality 699–702 (2009).

  27. 27.

    See, e.g., Manfred Max-Neef, Economic Growth and Quality of Life: A Threshold Hypothesis, 15 Ecol. Econ. 115–118 (1995).

  28. 28.

    See, e.g., Robert Costanza et al., The Value of the World’s Ecosystem Services and Natural Capital, 387 Nature 253–260 (1997); Karl-Göran Mäler, Sara Aniyar & Åsa Jansson, Accounting for Ecosystem Services as a Way to Understand the Requirements for Sustainable Development, 105 PNAS 9501–9506 (2008); Sheng Zhao, Huasheng Hong & Luoping Zhang, Linking the Concept of Ecological Footprint and Valuation of Ecosystem Services: A Case Study of Economic Growth and Natural Carrying Capacity, 15 Int’l J. Sustainable Dev. & World Ecol. 448–456 (2008).

  29. 29.

    See, e.g., Ida Kubiszewski, Robert Costanza, Carol Franco, Philip Lawn, John Talberth, Tim Jackson & Camille Aylmer, Beyond GDP: Measuring and Achieving Global Genuine Progress, 93 Ecol. Econ. 57–68 (2013).

  30. 30.

    See, e.g., Philip A. Lawn, A Theoretical Foundation to Support the Index of Sustainable Economic Welfare (ISEW), Genuine Progress Indicator (GPI), and Other Related Indexes, 44 Ecol. Econ. 105–118 (2003).

  31. 31.

    See, e.g., Farhad Noorbaksh, A Modified Human Development Index, 26 World Dev. 517–528 (1998); Ambuj D. Sagar & Adil Najam, The Human Development Index: A Critical Review, 25 Ecol. Econ. 249–264 (1998).

  32. 32.

    See Stefan Preisner, Gross National Happiness: Bhutan’s Vision of Development and Its Challenges, in Indigeneity and Universality in Social Science: A South Asian Response 212–232 (Partha Nath Mukherji & Chandan Sengupta eds., 2004). See generally, e.g., Philip Layard, Happiness: Lessons from a New Science (2005); Economics and Happiness: Framing the Analysis (Luigino Bruni & Pier Luigi Porta eds., 2006).

  33. 33.

    Edward O. Wilson, Consilience: The Unity of Knowledge 237 (1998). With respect to the notion that humans feel an innate emotional connection to other species, see generally Edward O. Wilson, Biophilia: The Human Bond with Other Species (1984); Ursula Goodenough, The Sacred Depths of Nature (1998).

  34. 34.

    See, e.g., Jia Wei Zhang, Ryan T. Howell & Ravi Iyer, Engagement with Natural Beauty Moderates the Positive Relation Between Connectedness with Nature and Psychological Well-Being, 38 J. Envtl. Psych. 55–63 (2014).

  35. 35.

    Robert B. Zajonc, Feeling and Thinking: Preferences Need No Inferences, 35 Am. Psychologist 151–175, 154 (1980); accord Meir Statman, Kenneth L. Fisher & Deniz Anginer, Affect in a Behavioral Asset-Pricing Model, 64:2 Fin. Analysts J. 20–29, 20 (March/April 2008).

  36. 36.

    See Robert B. Zajonc, Mere Exposure: A Gateway to the Subliminal, 10 Current Directions in Psych. Sci. 224–228 (2001).

  37. 37.

    See generally Paul Slovic, Melissa Finucane, Ellen Peters & Donald G. Macgregor, The Affect Heuristic, in Heuristics and Biases: The Psychology of Intuitive Judgment 397–420 (Thomas Gilovich, Dale Griffin & Daniel Kahneman eds., 2002).

  38. 38.

    See id. at 397.

  39. 39.

    See, e.g., Baba Shiv & Alexander Fedorikhin, Heart and Mind in Conflict: The Interplay of Affect and Cognition in Consumer Decision Making, 26 J. Consumer Research 278–292 (1999).

  40. 40.

    See Melissa L. Finucane, Ali Alhakami, Paul Slovic & Stephen M. Johnson, The Affect Heuristic in Judgments of Risks and Benefits, 13 J. Behav. Decision Making 1–17 (2000); Paul Slovic, Ellen Peters, Melissa L. Finucane & Donald G. MacGregor, Affect, Risk, and Decision Making, 24 Health Psych. S35–S40 (2005).

  41. 41.

    See, e.g., Géraldine David, Kim Oosterlinck & Ariane Szafarz, Art Market Inefficiency, 121 Econ. Letters 23–25 (2013); Péter Erdos & Mihály Ormos, Random Walk Theory and the Weak-Form Efficiency of the US Art Auction Prices, 34 J. Banking & Fin. 1062–1076 (2010).

  42. 42.

    See, e.g., Elroy Dimson & Christophe Spaenjers, Ex Post: The Investment Performance of Collectible Stamps, 110 J. Fin. Econ. 443–458 (2011).

  43. 43.

    See, e.g., Luc Renneboog & Christophe Spaenjers, Buying Beauty: On Prices and Returns in the Art Market, 59 Mgmt. Sci. 36–53 (2013).

  44. 44.

    See Edward L. Glaeser, A Nation Of Gamblers: Real Estate Speculation And American History (Feb. 2013) (NBER Working Paper No. 18825) (available at http://www.nber.org/papers/w18825).

  45. 45.

    See Orley Ashenfelter, How Auctions Work for Wine and Art, 3 J. Econ. Persp. 23–26 (1989).

  46. 46.

    See Malcolm Baker & Jeffrey Wurgler, Investor Sentiment in the Stock Market, 21 J. Econ. Persp. 129–151 (2007).

  47. 47.

    Statman, Fisher & Anginer, supra note 35, at 20.

  48. 48.

    Id.

  49. 49.

    See, e.g., Harry M. Markowitz, Mean-Variance Analysis in Portfolio Choice and Capital Markets (1987).

  50. 50.

    Harry Markowitz, The “Great Confusion” Concerning MPT, 4 Aestimatio 8–27, 14 (2012). See generally Harry Markowitz, Mean-Variance Approximations to Expected Utility, 234 Eur. J. Operational Research 346–355 (2014); Harry M. Markowitz & Erik L. Van Dijk, Single-Period Mean-Variance Analysis in a Changing World, 59 Fin. Analysts J. 30 (2003), reprinted in Stochastic Programming: The State of the Art in Honor of George B. Dantzig 213–237 (Gerd Infanger ed., 2011); cf. Lawrence B. Pulley, Mean-Variance Approximations to Expected Logarithmic Utility, 31 Operations Research 685–696 (1983).

  51. 51.

    Fama, Efficient Capital Markets II, Chap. 2, supra note 55, at 1575–1576.

  52. 52.

    Id. at 1576. See generally Fama, Efficient Capital Markets: A Review of Theory and Empirical Work, Chap. 2, supra note 55.

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  1. College of Law, Michigan State University, East Lansing, Michigan, USA

    James Ming Chen

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Chen, J.M. (2016). Postmodern Portfolio Theory. In: Postmodern Portfolio Theory. Quantitative Perspectives on Behavioral Economics and Finance. Palgrave Macmillan, New York. https://doi.org/10.1057/978-1-137-54464-3_3

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  • DOI: https://doi.org/10.1057/978-1-137-54464-3_3

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