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Einsatz von Künstlichen Neuronalen Netzen bei der Aktienkursprognose: Eine Stärken-Schwächen-Analyse

Einsatz von Künstlichen Neuronalen Netzen bei der Aktienkursprognose: Eine Stärken-Schwächen-Analyse

          
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About the Book

Inhaltsangabe: Problemstellung: Die Entwicklung globaler Aktienmärkte und der darin gehandelten einzelnen Aktienwerte wird bestimmt von Einflußfaktoren aus der unternehmensinternen Wertschöpfungskette und von dem Diskontinuitäten auslösenden situativen Kontext der betrachteten Unternehmen. Für die Realisation von Gewinnen und zur Abschätzung zukünftiger Gewinnpotentiale ist eine eingehende Diagnose der aktienkursbestimmenden Informationen und Einflußfaktoren notwendig. Die mit der Diagnose verbundene Analyse des Ist-Zustandes und Prognose zukünftiger Wird-Zustände setzt den Einsatz von Instrumenten für den mit dieser Aufgabe verbundenen Informationsverarbeitungsprozeß voraus. In der Vergangenheit sind zu diesem Zweck eine Reihe von - in aller Regel nur einen Ausschnitt betrachtenden - Analyse- und Prognoseinstmmenten entwickelt und angewandt worden. Die Betrachtung nur einzelner aktienkursbestimmender Einflußfaktoren erscheint für die Prognosequalität und des damit einhergehenden möglichst hohen lnformations- und Sicherheitsgrades als nicht ausreichend. Vor diesem Hintergrund rückten Verfahren, die eine weitestgehende Berücksichtigung von verfügbaren Informationen zur Verbesserung der Prognosequalität ermöglichen, in das Zentrum des Interesses. Eine Möglichkeit dieses Ziel zu erreichen, könnte im Einsatz von Künstlichen Neuronalen Netzen bei der Aktienkursprognose liegen. Der Einsatz Künstlicher Neuronaler Netze im ökonomischen Bereich etabliert sich seit dem Ende der achtziger Jahre, nachdem das Hauptinteresse zuvor der Theorie galt. Als Einsatzgebiete sind solche Aufgabenfelder denkbar, für die kein oder nur unzureichend deskriptives Wissen zur Problemlösung zur Verfügung steht, wodurch der Einsatz konventioneller Algorithmen ausgeschlossen oder zumindest eingeschränkt wird. Des weiteren wird den Künstlichen Neuronalen Netzen eine Lernfähigkeit gegenüber variierenden Problemfeldern und eine Approximationsfähigkeit bei nur rudimentärem Datenmaterial zugesprochen.


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Product Details
  • ISBN-13: 9783838613420
  • Publisher: Diplom.de
  • Binding: Paperback
  • Language: German
  • Returnable: N
  • Spine Width: 8 mm
  • Weight: 177 gr
  • ISBN-10: 3838613422
  • Publisher Date: 31 Mar 1999
  • Height: 210 mm
  • No of Pages: 126
  • Series Title: German
  • Sub Title: Eine Stärken-Schwächen-Analyse
  • Width: 148 mm


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Einsatz von Künstlichen Neuronalen Netzen bei der Aktienkursprognose: Eine Stärken-Schwächen-Analyse
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Einsatz von Künstlichen Neuronalen Netzen bei der Aktienkursprognose: Eine Stärken-Schwächen-Analyse
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