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Zeitpunktsignale zum aktiven Portfoliomanagement
[Time-Point-Signals for Active Portfolio Management]

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Listed:
  • Czinkota, Thomas

Abstract

The successful active portfolio manager has to have at least two main competencies: Felicitous asset allocation choice and the competence to do so at the right point in time. Based on an extension of Grinold and Kahn’s Fundamental Law of Active Management, this paper describes a method to identify such points. We construct a Chow-Test for the identification of structural breaks within a default competence-structure. Time-Point-Signals identified this way are special in three ways: First, our method identifies the signals immediately after their occurrence. For statistical reasons, this has been difficult to achieve, yet represents a necessity for active management. Typically, 30 days worth of data are required to conduct statistical tests after a structural break. Such a long delay often leads only to the achievement of typical expected rates of return. In active markets, 30 days are the long run. Second, those time-point-signals are independent from a specific portfolio allocation and are therefore generally applicable to a selected investment universe. This means, it does not matter whether the indicated timing point is used by a good or an extremely good active manager, for both benefit from the support. In fact, we show with the help of the theoretical framework that the support is more valuable to less exceptional managers. Third, the theoretical link of time-point-signals to the framework of Grinold and Kahn is of significant use to practitioners. By understanding the timing of signals, portfolios are not just strengthened through intuition but also due to theoretical insights.

Suggested Citation

  • Czinkota, Thomas, 2012. "Zeitpunktsignale zum aktiven Portfoliomanagement [Time-Point-Signals for Active Portfolio Management]," MPRA Paper 39565, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:39565
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    References listed on IDEAS

    as
    1. William P. Jones & George W. Furnas, 1987. "Pictures of relevance: A geometric analysis of similarity measures," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 38(6), pages 420-442, November.
    2. Bruce E. Hansen, 2001. "The New Econometrics of Structural Change: Dating Breaks in U.S. Labour Productivity," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 117-128, Fall.
    3. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    4. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    5. Hubert Dichtl & Wolfgang Drobetz, 2009. "Does tactical asset allocation work? Another look at the fundamental law of active management," Journal of Asset Management, Palgrave Macmillan, vol. 10(4), pages 235-252, October.
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    More about this item

    Keywords

    Fundamental Law of Active Management; Information Coefficient; Information Ratio; aktives Fondsmanagement; Strukturbruch; Zeitpunktsignale; Timing;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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