This paper introduces a reinforcement learning based approach to compute optimal interest rate reaction functions in terms of fulfilling inflation and output gap targets. The method is generally flexible enough to incorporate restrictions like the zero lower bound, nonlinear economy structures...

Using machine learning methods, we develop a new measure of aggregate analyst sentiment. We first train analyst-specific neural network (NN) models that capture each analyst's common biases across firms. Using NN model outputs, we decompose the forecast errors of individual analysts into...

We propose a flexible machine learning (ML) framework for real-time transaction monitoring in high-value payment systems (HVPS), which are a central piece of a country's financial infrastructure. This framework can be used by system operators and overseers to detect anomalous transactions,...

This paper formulates and solves an economic decision problem of the acquisition of costly information in data-driven decision making. The paper assumes an agent predicting a random variable utilizing several costly explanatory variables. Prior to the decision making, the agent learns about the...

We examine whether empirical results using text-based sentiment of U.S. annual reports depend on the underlying context, within documents, from which sentiment is measured. We construct a clause-level measure of context, showing that sentiment is driven by many different contexts and that...

Legal and regulatory understandings of information provision miss the importance of the exploration-exploitation dynamic. This Essay argues that is a mistake and seeks to bring this perspective to the debate about information provision and competition. A general, ongoing problem for an...

This paper evaluates artificial intelligence's (AI's) foray into financial markets and the resultant implications for theory and practice. I argue for reevaluating mainstream financial theories and proactively building governance frameworks. I also advocate for external audits and oversight that...

We review key aspects of forecasting using nonlinear models. Because economic models are typically misspecified, the resulting forecasts provide only an approximation to the best possible forecast. Although it is in principle possible to obtain superior approximations to the optimal forecast...

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in...