We analyze how a benevolent, privately informed government agency would optimally release information about the economy's growth rate when the agents hold heterogeneous beliefs. We model two types of agent: "conforming'' and "dissenting.'' The former has a prior that is identical to that of 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,...

The level of capital tax gains has high explanatory power regarding the question of what drives economic inequality. On this basis, the authors develop a simple, yet micro-founded portfolio selection model to explain the dynamics of wealth inequality given empirical tax series in the US. The...

Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We deploy LSTM networks for predicting out-of-sample directional movements for the...

Most statistical arbitrage strategies in the academic literature soley rely on price time series. By contrast, alternative data sources are of growing importance for professional investors. We contribute to bridging this gap by assessing the price-predictive value of more than nine million...