We employ deep learning in forecasting high-frequency returns at multiple horizons for 115 stocks traded on Nasdaq using order book information at the most granular level. While raw order book states can be used as input to the forecasting models, we achieve state-of-the-art predictive accuracy...

Stock markets proved to be statistically predictable on an economically interesting scale over the past decade by fully data driven automatically constructed maps that associate to a set of new factor values a return prediction that is the average of historically observed returns for an area in...

El objetivo del presente estudio radica en construir algunos modelos estadísticos, econométricosy de inteligencia artificial que permitan realizar predicciones sobre el comportamientode mercado de la acción de SURAMINV (Suramericana de Inversiones S. A.).Se obtuvo evidencia a favor de la...

We introduce machine learning in the context of central banking and policy analyses. Our aim is to give an overview broad enough to allow the reader to place machine learning within the wider range of statistical modelling and computational analyses, and provide an idea of its scope and...

We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them. Our approach is based on a combination of Inverse Reinforcement Learning (IRL) and RL. First, the IRL...

A significant portion of information shared in earnings calls is conveyed through verbal communication by corporate managers. However, quantifying the extent of new information provided by managers poses challenges due to the unstructured nature of human language and the difficulty in gauging...

Generative AI tools such as ChatGPT can fundamentally change the way investors process information. We probe the economic usefulness of these tools in summarizing complex corporate disclosures using the stock market as a laboratory. The unconstrained summaries are dramatically shorter, often by...

In this paper, we explore potential uses of generative AI models, such as ChatGPT, for investment portfolio selection. Trusting investment advice from Generative Pre-Trained Transformer (GPT) models is a challenge due to model "hallucinations", necessitating careful verification and validation...

Artificial intelligence (AI), powered by machine learning algorithms, is capable of extracting information efficiently from big data and, therefore, has great potential for improving financial decision-making. In this chapter, we summarize several important applications of AI in this context....

We analyze the joint out-of-sample predictive ability of a comprehensive set of 299 firm characteristics for cross-sectional stock returns. We develop a cross-sectional out-of-sample R2 statistic that provides an informative measure of the accuracy of cross-sectional return forecasts in terms of...