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Rational Learning For Risk-Averse Investors By Conditioning On Behavioral Choices

Author

Listed:
  • MICHELE COSTOLA

    (Department of Economics, University Ca’ Foscari of Venice, Cannaregio 873, Venezia, Italy)

  • MASSIMILIANO CAPORIN

    (Department of Economics and Management “Marco Fanno”, University of Padova, Via del Santo 22, Padova, Italy)

Abstract

The authors present a rational learner agent, which considers the information coming from a behavioral counterpart during the allocation process. The learner agent adopts a herding behavior by conditioning her choice on the selection of the portfolio’s constituents. They use the concept of performance measure to define agents’ preferences: the higher the measure, the higher the expected utility of a given asset. The rational learner agent updates her information in a Bayesian manner similarly to the Black–Litterman model. Finally, the authors provide an empirical application including all the assets present in the NASDAQ and NYSE stock exchange from September 1977 to December 2014.

Suggested Citation

  • Michele Costola & Massimiliano Caporin, 2016. "Rational Learning For Risk-Averse Investors By Conditioning On Behavioral Choices," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 1-26, March.
    • Handle: RePEc:wsi:afexxx:v:11:y:2016:i:01:n:s2010495216500032
    DOI: 10.1142/S2010495216500032
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    More about this item

    Keywords

    Combining performance measures; portfolio allocation; learning agent process;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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