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...

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...

The advent of reinforcement learning (RL) in financial markets is driven by several advantages inherent to this field of artificial intelligence. In particular, RL allows to combine the "prediction" and the "portfolio construction" task in one integrated step, thereby closely aligning the...

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 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...

Partial cointegration is a weakening of cointegration that allows for the "cointegrating" process to contain a random walk and a mean-reverting component. We derive its representation in state space, provide a maximum likelihood based estimation routine, and a suitable likelihood ratio test....

In recent years, machine learning research has gained momentum: New developments in the field of deep learning allow for multiple levels of abstraction and are starting to supersede well-known and powerful tree-based techniques mainly operating on the original feature space. All these methods...

We develop a copula-based pairs trading framework and apply it to the S&P 100 index constituents from 1990 to 2014. We propose an integrated approach, using copulas for pairs selection and trading. Essentially, we fit t-copulas to all possible combinations of pairs in a 12 month formation...