Abstract
Traditionally, portfolio optimization is associated with finding the ideal trade-off between return and risk by maximizing the expected utility. Investors’ preferences are commonly assumed to follow a quadratic or power utility function, and asset returns are often assumed to follow a Gaussian distribution. Investment analysis has therefore long been focusing on the first two moments of the distribution, mean and variance. However, empirical asset returns are not normally distributed, and neither utility function captures investors’ true attitudes towards losses. The impact of these specification errors under realistic assumptions is investigated. As traditional optimization techniques cannot deal reliably with the extended problem, the use of a heuristic optimization approach is suggested. It is found that loss aversion has a substantial impact on what investors consider to be an efficient portfolio and that mean-variance analysis alone can be utterly misguiding.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Basel Committee on Banking Supervision: 2003, Consultative document: The new Basel capital accord, Bank for International Settlement, available from: www.bis.org.
Brabazon, A. and O’Neill, M.: 2006, Biologically inspired algorithms for financial modeling, Springer, New York.
Dueck, G. and Scheuer, T.: 1990, Threshold accepting: A general purpose algorithm appearing superior to simulated annealing, Journal of Computational Physics 90, 161–175.
Fogel, D.B.: 2001, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, 2nd edn, IEEE Press, New York, NY.
Gilli, M. and Këllezi, E.: 2002, The threshold accepting heuristic for index tracking, in Panos Pardalos and Vassilis Tsitsiringos (eds), Financial engineering, E-commerce, and supply chain, Kluwer Academic Press, Bosten, Mass.
Gilli, M. and Këllezi, E.: 2006, An application of extreme value theory for measuring financial risk, Computational Economics 27(2–3), 207–228.
Gilli, M., Këllezi, E. and Hysi, H.: 2006, A data-driven optimization heuristic for downside risk minimization, The Journal of Risk 8(3), 1–18.
Gilli, M., Maringer, D. and Winker, P.: 2008, Applications of heuristics in finance, in D. Seese, C. Weinhardt and F. Schlottmann (eds), IT and Finance, Springer. forthcoming.
Holland, J.H.: 1975, Adaption in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI.
Kahneman, D. and Tversky, A.: 1979, Prospect theory: An analysis of decision under risk, Econometrica 47, 263–291.
Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P.: 1983, Optimization by simulated annealing, Science 220(4598), 671–680.
Konno, H.: 2005, Applications of global optimization to portfolio analysis, in C. Audet, P. Hansen and G. Savard (eds), Essays and Surveys in Global Optimization, Springer, pp. 195–210.
Maringer, D.: 2005, Portfolio Management with Heuristic Optimization, Springer-Verlag.
Maringer, D.: 2008, Constrained index tracking under loss aversion using differential evolution, in A. Brabazon and M. O’Neill (eds), Natural Computing in Computational Economics and Finance, Springer, chapter Constrained Index Tracking under Loss Aversion using Differential Evolution, forthcoming.
Maringer, D. and Meyer, M.: 2008, Smooth transition autoregressive models — new approaches to the model selection process, Studies in Nonlinear Dynamics and Econometrics. forthcoming.
Maringer, D. and Oyewumi, O.: 2007, Index tracking with constrained portfolios, Intelligent Systems in Accounting, Finance and Management 15, 57–71.
Maringer, D. and Parpas, P.: 2008, Global optimization of higher moments in portfolio selection, Journal of Global Optimization. forthcoming.
Markowitz, H.M.: 1952, Portfolio selection, The Journal of Finance 7(1), 77–91.
Odean, T.: 1998, Are investors reluctant to realize their losses?, Journal of Finance 53, 1775–1798.
Parpas, P. and Rustern, B.: 2006, Global optimization of the scenario generation and portfolio selection problems, in M. Gavrilova, O. Gervasi, V. Kumar, C.J.K. Tan, D. Taniar, A. Laganà, Y. Mun and H. Choo (eds), Computational Science and Its Applications — ICCSA 2006, Proceedings Part III, Vol. 3982 of Lecture Notes in Computer Science.
Price, K., Storn, R. and Lampinen, J.: 2005, Differential Evolution: A Practical Approach to Global Optimization, Springer.
Rechenberg, I.: 1965, Cybernetic solution path of an experimental problem, Library Translation 1122, Royal Aircraft Establishment.
Rechenberg, I.: 1973, Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution, Fromman-Holzboog Verlag, Stuttgart.
Samuelson, P.A.: 1963, Risk and uncertainty: A fallacy of large numbers, Scientia XCVIII, 108–113.
Storn, R. and Price, K.: 1995, Differential evolution — a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical report, International Computer Science Institute, Berkeley.
Storn, R. and Price, K.: 1997, Differential evolution — a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization 11(4), 341–359.
Thaler, R.H., Tversky, A., Kahneman, D. and Schwartz, A.: 1997, The effect of myopia and loss aversion on risk taking: An experimental test, Quaterly Journal of Economics 112, 647–661.
Tobin, J.: 1958, Liquidity preference as behavior towards risk, Review of Economic Studies 26(1), 65–86.
Winker, P.: 2001, Optimization Heuristics in Econometrics. Applications of Threshold Accepting, John Wiley & Sons, ltd., Chichester et al.
Winker, P. and Maringer, D.: 2007, The threshold accepting optimization algorithm in economics and statistics, in E.J. Kontoghiorges and C. Gatu (eds), Advances in Computational Economics, Finance and Management Science, Kluwer, pp. 107–125.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Maringer, D. (2008). Risk Preferences and Loss Aversion in Portfolio Optimization. In: Kontoghiorghes, E.J., Rustem, B., Winker, P. (eds) Computational Methods in Financial Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77958-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-77958-2_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77957-5
Online ISBN: 978-3-540-77958-2
eBook Packages: Business and EconomicsEconomics and Finance (R0)