We apply state-of-the-art financial machine learning to assess the return-predictive value of more than 45,000 earnings announcements on a majority of S&P1500 constituents. To represent the diverse information content of earnings announcements, we generate predictor variables based on various...

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...

We apply state-of-the-art financial machine learning to assess the return-predictive value of more than 45,000 earnings announcements on a majority of S&P1500 constituents. To represent the diverse information content of earnings announcements, we generate predictor variables based on various...

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...

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 is increasingly applied to time series data, as it constitutes an attractive alternative to forecasts based on traditional time series models. For independent and identically distributed observations, cross-validation is the prevalent scheme for estimating out-of-sample...

This paper presents the first large-scale application of deep reinforcement learning to optimize the placement of limit orders at cryptocurrency exchanges. For training and out-of-sample evaluation, we use a virtual limit order exchange to reward agents according to the realized shortfall over a...

Machine learning is increasingly applied to time series data, as it constitutes an attractive alternative to forecasts based on traditional time series models. For independent and identically distributed observations, cross-validation is the prevalent scheme for estimating out-of-sample...

This paper presents the first large-scale application of deep reinforcement learning to optimize the placement of limit orders at cryptocurrency exchanges. For training and out-of-sample evaluation, we use a virtual limit order exchange to reward agents according to the realized shortfall over a...