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Ein Beispiel sind Aktien der Firma BioNTech.\r\n \r\n Zahlreiche empirische Studien deuten darauf hin, dass bestimmte Aktienmerkmale mit einer langfristig höheren Rendite verbunden sein können. Dazu gehören die Faktoren: \r\n \r\n Size: Aktien kleiner und mittelgroßer Unternehmen erzielen tendenziell höhere Renditen als große Unternehmen. \r\n \r\n Value: Günstig bewertete Aktien entwickeln sich häufig besser als teuer bewertete Titel. \r\n \r\n Quality: Unternehmen mit stabilen Erträgen, soliden Bilanzen und nachhaltigen Geschäftsmodellen bieten langfristig oft überdurchschnittliche Renditen. \r\n \r\n Momentum: Aktien mit starker Kursentwicklung in der jüngeren Vergangenheit tendieren dazu, diese Performance fortzusetzen.\r\n \r\n Low Risk: Aktien mit geringerer Schwankungsintensität weisen auf lange Sicht häufig ein attraktives Rendite-Risiko-Profil auf. \r\n \r\n Es ist grundsätzlich vorgesehen, monatlich etwa 500–1.000 Aktien anhand von rund 20–50 Aktienmerkmalen zu analysieren, um daraus ein meiner Analyse nach optimales Portfolio zu berechnen. Dabei sollen Methoden der KI – unter anderem Neuronale Netze, Random Forests und Gradient Boosting – zum Einsatz kommen, um die jeweils relevantesten Aktienmerkmale und optimalen Faktorkombinationen zu identifizieren. \r\n \r\n Typischerweise soll in etwa 40–100 Aktien investiert werden, um eine breite Diversifikation sicherzustellen und die Zielfaktoren bestmöglich abzubilden. \r\n \r\n In Krisenphasen ist geplant, einen höheren Kassenanteil zu halten, um mögliche Kursrückgänge zu begrenzen oder Gewinne zu sichern.\r\n \r\n Sämtliche Transaktionen werden manuell durchgeführt. 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