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Deterministic Portfolio Selection Models, Selection Bias, and an Unlikely Hero

In: Advances In Quantitative Analysis Of Finance And Accounting

Author

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  • Herbert E. Phillips

    (Temple University, USA)

Abstract

Portfolio selection models are programmed by their respective efficiency criteria to fall into a state of first-order condition love with the right sort of outliers. Nothing is changed, unfortunately, when, in general, a deterministic portfolio optimization model's inputs are stochastic rather than parametric. Distributional properties of the input estimator functions employed by four common portfolio selection models are reviewed and their solution algorithms studied in search of unique interactive effects that may mitigate the estimation error problem. Empirical and analytic support is provided for the conclusion that there is one model, an unlikely hero, that is least susceptible.

Suggested Citation

  • Herbert E. Phillips, 2008. "Deterministic Portfolio Selection Models, Selection Bias, and an Unlikely Hero," World Scientific Book Chapters, in: Cheng-Few Lee (ed.), Advances In Quantitative Analysis Of Finance And Accounting, chapter 10, pages 179-203, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789812791696_0010
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    More about this item

    Keywords

    Hedging Strategies; Expense Mismatching; Stock Split; Trading Volume; Portfolio Optimization; Intraday Patterns; Earnings Management; International Winner-Loser Effect;
    All these keywords.

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services
    • G3 - Financial Economics - - Corporate Finance and Governance

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