We present a comprehensive simulation study to assess and compare the performance of popular machine learning algorithms for time series prediction tasks. Specifically, we consider the following algorithms: multilayer perceptron (MLP), logistic regression, naïve Bayes, knearest neighbors,...

We leverages computational linguistics to determine how the narrative content of earnings conference calls influences investors' uncertainty about a firm's future valuation. By applying statistical topic modeling to a corpus of 18,254 conference calls, we extract topics and tones from both...

Machine learning research has gained momentum-also in finance. Consequently, initial machine-learning-based statistical arbitrage strategies have emerged in the U.S. equities markets in the academic literature, see e.g., Takeuchi and Lee (2013); Moritz and Zimmermann (2014); Krauss et al....

The majority of electronic markets worldwide employ limit order books, and the recently emerging exchanges for cryptocurrencies pose no exception. With this work, we empirically analyze whether commonly observed empirical properties from established limit order exchanges transfer to the...

This paper examines earnings momentum strategies in the U.S. stock universe from an investor's perspective. Specifically, we use the software Stock Investor Pro from the American Association of Individual Investors (AAII) to obtain the composition of the U.S. stock universe from 2005-2015 on a...

We develop a multivariate statistical arbitrage strategy based on vine copulas - a highly flexible instrument for linear and nonlinear multivariate dependence modeling. In an empirical application on the S&P 500, we find statistically and economically significant returns of 9.25 percent p.a. and...