Long Short-Term Memory Networks bei der Renditeprognose. Inwiefern lassen sich die Ergebnisse von Fischer/Krauss (2017) replizieren und nachvollziehen
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Projektarbeit aus dem Jahr 2020 im Fachbereich VWL - Finanzwissenschaft, Note: 1,7, Universität Bremen, Sprache: Deutsch, Abstract: Diese Arbeit erklärt die wichtigsten Entwicklungsschritte von einfachen neuronalen Netzen bis hin zu den LSTM Netzwerken und arbeitet Vor- und Nachteile heraus. Im zweiten Teil der Arbeit werden die Ergebnisse des Papiers von Fischer/Krauss (2017) kritisch gewürdigt. Die Resultate in dieser Untersuchung weichen zum Teil deutlich von denen des Fischer/Krauss Papieres ab. In dieser Arbeit werden selektiv die Zeiträume der Jahre 1994, 2001, 2008 und 2015 untersucht. Lediglich in den untersuchten Jahren 1994 und 2001 konnte gegenüber dem S&P500 eine signifikante Outperformance durch das LSTM-Networks, mit durchschnittlich täglichen Renditen von 0,022 respektive 0,0074 festgestellt werden. Eine allgemeingültige Prognosefähigkeit des Modells oder eine Überlegenheit zu etwaigen BenchmarkModellen lässt sicher allerdings nicht darstellen. Das gewählte Benchmark-Modell, die logistische Regression, liefert in 1994 ähnlich gute Ergebnisse wie das LSTM. Da die Daten und Methoden denen von Fischer/Krauss (2017) folgen, gilt es eine Erklärung der differenten Resultate zu finden. Machine Learning ist seit den 90er Jahren ein verbreitetes Thema der Finanzwissenschaft und Informatik. Mit der neuen Big Data Welle kommt das mittlerweile in die Jahre gekommene Wissenschaftsgebiet wieder in den Fokus der Wissenschaft. Die Gründe liegen auf der Hand. Gerade die besseren Rechenkapazitäten und grö eren Mengen an öffentlich zugänglichen Kapitalmarktdaten gestatten es, Machine Learning erneut auf den Prüfstand zu stellen. Dies führte zu einer erneuten Prominenz des Forschungsgebietes, des Deep Learning. Das Papier von den Autoren Fischer/Krauss (2017) zum Thema Aktienmarktprognose mittels neuronaler Netze sorgte aufgrund ihrer Ergebnisse für Aufsehen. Die Autoren nutzen in ihrer Arbeit zur Prognose von Aktienmarktrenditen insbesondere Long Short-Term Memory N
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