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Turnover activity in wealth portfolios

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  • Castaldi, Carolina
  • Milakovic, Mishael

Abstract

We examine several named subsets of the wealthiest individuals in the US and the UK that are compiled by Forbes Magazine and Sunday Times. The data support conventional wisdom of a wealth distribution with power law-distributed right tail, and they allow us to calibrate a statistical equilibrium model of wealth distribution. Such a model is not only able to account for the observed power law tail of wealth distribution, but is also consistent with the asymmetric laplacian distribution of portfolio returns that we observe in both our samples. In addition, with information on the distribution of portfolio returns that we construct from the subsets, the model provides an indicator for how often changes in the composition of the wealthiest portfolios occur – an indicator we call turnover activity. Finally, we also calculate a simple mobility measure from the subsets and look at trends in equality, mobility and turnover activity.
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Suggested Citation

  • Castaldi, Carolina & Milakovic, Mishael, 2007. "Turnover activity in wealth portfolios," Journal of Economic Behavior & Organization, Elsevier, vol. 63(3), pages 537-552, July.
  • Handle: RePEc:eee:jeborg:v:63:y:2007:i:3:p:537-552
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    References listed on IDEAS

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    8. Giulio Bottazzi, 2004. "Subbotools User's Manual," LEM Papers Series 2004/14, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
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    Citations

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    Cited by:

    1. Philip Hans Franses & Stephanie Vermeer, 2012. "Inequality amongst the wealthiest and its link with economic growth," Applied Economics, Taylor & Francis Journals, vol. 44(22), pages 2851-2858, August.
    2. Alfarano, Simone & Milakovic, Mishael, 2008. "Does classical competition explain the statistical features of firm growth?," Economics Letters, Elsevier, vol. 101(3), pages 272-274, December.
    3. Mikhail Goykhman, 2017. "Wealth dynamics in a sentiment-driven market," Papers 1705.07092, arXiv.org.
    4. Sylvain Barde, 2012. "Back to the future: economic rationality and maximum entropy prediction," Studies in Economics 1202, School of Economics, University of Kent.
    5. Antonio Doria, Francisco, 2011. "J.B. Rosser Jr. , Handbook of Research on Complexity, Edward Elgar, Cheltenham, UK--Northampton, MA, USA (2009) 436 + viii pp., index, ISBN 978 1 84542 089 5 (cased)," Journal of Economic Behavior & Organization, Elsevier, vol. 78(1-2), pages 196-204, April.
    6. repec:eee:phsmap:v:488:y:2017:i:c:p:132-148 is not listed on IDEAS
    7. Troy Tassier, 2013. "Handbook of Research on Complexity, by J. Barkley Rosser, Jr. and Edward Elgar," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 39(1), pages 132-133.
    8. Fiaschi, Davide & Marsili, Matteo, 2012. "Distribution of wealth and incomplete markets: Theory and empirical evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 81(1), pages 243-267.
    9. Rishabh Kumar, 2016. "Capital and the Hindu rate of growth: Top Indian wealth holders 1961-1986," Working Papers 1608, New School for Social Research, Department of Economics.
    10. Thomas Lux, 2009. "Applications of Statistical Physics in Finance and Economics," Chapters,in: Handbook of Research on Complexity, chapter 9 Edward Elgar Publishing.
    11. Alfarano, Simone & Milaković, Mishael & Irle, Albrecht & Kauschke, Jonas, 2012. "A statistical equilibrium model of competitive firms," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 136-149.
    12. Lux, Thomas, 2008. "Applications of statistical physics in finance and economics," Kiel Working Papers 1425, Kiel Institute for the World Economy (IfW).
    13. Giulio Bottazzi, 2007. "On the Pareto Type III distribution," LEM Papers Series 2007/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    14. Sylvain Barde, 2015. "Back to the Future: Economic Self-Organisation and Maximum Entropy Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 337-358, February.

    More about this item

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General

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