Anwendung eines künstlichen neuronalen Netzes zur Kursprognose am Beispiel des DAX
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Diplomarbeit aus dem Jahr 2008 im Fachbereich BWL - Bank, Börse, Versicherung, Note: 1,3, Eberhard-Karls-Universität Tübingen, Sprache: Deutsch, Abstract: Im Rahmen dieser Arbeit soll ein künstliches neuronales Netz zur Kursprognose des Deutschen Aktienindex (DAX) entwickelt und eingesetzt werden. Dabei wird im ersten Teil anfangs auf den biologischen Ursprung künstlicher neuronaler Netze eingegangen. Anschlie end wird beschrieben, was genau unter einem künstlichen neuronalen Netz zu verstehen ist. Dies geschieht, indem neben der Entstehungsgeschichte und den verschiedenden Typen neuronaler Netze auch auf ausgewählte Lernregeln und Optimierungsmöglichkeiten eingegangen wird. Im praxisorientierten zweiten Teil dieser Arbeit wird anhand ausgewählter Determinanten ein künstliches neuronales Netz entwickelt und angewendet. Dabei werden zunächst einige Vorüberlegungen hinsichtlich der Selektion der relevanten Einflussgrö en angestellt. Anschlieend wird das Netz für verschiedene Horizonte zur DAX-Prognose eingesetzt. Um eine mögliche Verbesserung der erzielten Resultate zu erreichen, werden einige der theoretisch beschriebenen Optimierungsverfahren auf das Netz angewendet. Nach einer weiterführenden Untersuchung hinsichtlich der Prognosefähigkeit des Netzes anhand ausgewählter Inputgrö en, werden die wichtigsten Ergebnisse im letzten Abschnitt noch einmal zusammengefasst und mit den Resultaten anderer Prognoseverfahren verglichen. Schwerpunkt der nachfolgenden Betrachtungen ist dabei allerdings weniger der Prognoseerfolg im Verhältnis zu anderen Verfahren als viel mehr die systematische Anwendung eines künstlichen neuronalen Netzes und die Analyse der Auswirkungen verschiedener Modellvariationen auf dessen Leistungsfähigkeit.
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