Zeitpunktsignale zum aktiven Portfoliomanagement
[Time-Point-Signals for Active Portfolio Management]
AbstractThe 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 39565.
Date of creation: Jun 2012
Date of revision:
Fundamental Law of Active Management; Information Coefficient; Information Ratio; aktives Fondsmanagement; Strukturbruch; Zeitpunktsignale; Timing;
Find related papers by 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-06-25 (All new papers)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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