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How Investors Face Financial Risk Loss Aversion and Wealth Allocation

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  • Erick Rengifo

    (Fordham University, Department of Economics)

  • Emanuela Trifan

    (J.W. Goethe University Frankfurt am Main, Department of Economics)

Abstract

We study how the wealth-allocation decisions and the loss aversion of non-professional investors change subject to behavioral factors. The optimal wealth assignment between risky and risk-free assets results within a VaR portfolio model, where risk is individually assessed according to an extended prospect-theory framework. We show how the past performance and the portfolio evaluation frequency impact investor behavior. Myopic loss aversion holds at different evaluation frequencies. One year is the optimal frequency at which, under practical constraints, risky holdings are maximized. Previous research using standard VaR-significance levels may underestimate the loss aversion of individual investors.

Suggested Citation

  • Erick Rengifo & Emanuela Trifan, 2008. "How Investors Face Financial Risk Loss Aversion and Wealth Allocation," Fordham Economics Discussion Paper Series dp2008-01, Fordham University, Department of Economics.
    • Handle: RePEc:frd:wpaper:dp2008-01
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    References listed on IDEAS

    as
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    5. Michael S. Haigh & John A. List, 2005. "Do Professional Traders Exhibit Myopic Loss Aversion? An Experimental Analysis," Journal of Finance, American Finance Association, vol. 60(1), pages 523-534, February.
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    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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