Showing 1 - 10 of 19
Wir untersuchen den Querschnitt von über 1200 Kryptowährungen, gesammelt von 350 Handelsplätzen, in der Zeitspanne von Januar 2014 bis Juni 2020. Im speziellen untersuchen wir, ob weit verbreitete Charakteristika, wie Beta (Fama/MacBeth (1973)), Size (Banz (1981)) oder Momentum...
Persistent link: https://www.econbiz.de/10012940081
The characteristics book-to-market equity ratio, size and momentum are highly correlated with the average returns of common stocks. Fama and French (J Financ Econ 33(1):3-56, 1993), (J Finance 50(1):131-155, 1995) and (J Finance 51(1):55-84, 1996) argue (for size and the book-to-market equity...
Persistent link: https://www.econbiz.de/10011488338
We estimate and test several default risk models using new and unique data on corporate defaults in the German stock market. While defaults were extremely rare events in the 1990s, they have been a characteristic feature of the German stock market since the early 2000s. We apply the structural...
Persistent link: https://www.econbiz.de/10012983935
Dieser Artikel zeigt, dass eine Beimischung von Kryptowährungen in ein Portfolio, bestehend aus mehreren deutschen Asset-Klassen, mit Vorsicht zu betrachten ist. Auf Grund einer hohen realisierten Volatilität werden Kryptowährungen unter einem Markowitz- und Risikoparitätsansatz nur...
Persistent link: https://www.econbiz.de/10012053532
Persistent link: https://www.econbiz.de/10013371160
Persistent link: https://www.econbiz.de/10014226390
We compare the performance of the linear regression model, which is the current standard in science and practice for cross-sectional stock return forecasting, with that of machine learning methods, i.e., penalized linear models, support vector regression, random forests, gradient boosted trees...
Persistent link: https://www.econbiz.de/10015190303
Through an implementation of the 2-level-approach due to Vesanto & Alhoniemi (2000), this paper addresses a number of problems typically seen when visualized interpretation of Self Organizing Maps (SOM) are applied to derive a systematic classification system in the hedge fund literature....
Persistent link: https://www.econbiz.de/10015217947
We analyze the cross-section of more than 1200 cryptocurrencies derived from 350 exchanges in the time period from January 2014 to June 2020. Specifically, we investigate whether well-known cross-sectional characteristics like beta (Fama/MacBeth (1973)), size (Banz (1981)) or momentum (...
Persistent link: https://www.econbiz.de/10014001303
Die Renditen von Hedgefonds können nur unzulänglich anhand von traditionellen Faktormodellen erklärt werden, da Hedgefonds dynamische Handelsstrategien verfolgen und ihre Renditen nur gering mit den Renditen traditioneller Asset-Klassen korreliert sind. Hedgefonds erzielen jedoch keinesfalls...
Persistent link: https://www.econbiz.de/10014521457