560 research outputs found

    Field lab: analysis of quantitative investment strategies constructing a multi-factor investment strategy optimized sector momentum

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    In the combined part a multi-factor investment strategy is constructed out of four different enhanced single factor strategies using an optimization to maximize the Sharpe ratio of the combined portfolio. In the individual part an optimized sector momentum strategy is constructed that consists of a combination of a long and a market-neutral long/short strategy utilizing a simple regime-switching process with the goal to minimize momentum crashes and achieve robust returns in all market states

    Reportage: Gallery Walk

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     Buchbesprechung "Team of Teams" von Stanley McChristal.Im Rahmen eines Change-Management-Seminars beschäftigten sich Studierende des 2. Semesters im Masterstudiengangs Kommunikation und Management mit dem Buch ‚Team of Teams‘ von Stanley McChristal. Der ehemalige General der US-Army beschreibt darin, wie Führung auch in Extremsituationen gemeistert werden kann. Zum Abschluss des Seminars gestalteten die Studierenden Poster zu den Schlüsselbegriffen 'Leading like a gardener', 'Team of Teams', 'Empowered Execution', 'Adaptability' und 'Shared Concsiousness', die Stanleys Konzept prägen. Ziel dieser Handlungskonzepte ist es, dass Organisationen ihre Anpassungsfähigkeit in einer komplexen Welt verbessern können

    Metabonomics and Intensive Care

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    This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency medicine 2016. Other selected articles can be found online at http://www.biomedcentral.com/collections/annualupdate2016. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901

    Feature selection in the reconstruction of complex network representations of spectral data

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    Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitud
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