FinTech online lending to consumers has grown rapidly in the post-crisis era. As argued by its advocates, one key advantage of FinTech lending is that lenders can predict loan outcomes more accurately by employing complex analytical tools, such as machine learning (ML) methods. This study...

We investigate economic and institutional determinants of ICT infrastructure for a broad cross section ofmore than 100 countries. The ICT variable is constructed from a principal components analysis. The explanatory variables are selected by variants of the Lasso estimator from the machine...

If we reassess the rationality question under the assumption that the uncertainty of the natural world is largely unquantifiable, where do we end up? In this article the author argues that we arrive at a statistical, normative, and cognitive theory of ecological rationality. The main casualty of...

Pre-existing public debt vulnerabilities have been exacerbated by the effects of the pandemic, raising the risk of fiscal crises in emerging markets and low-income countries. This underscores the importance of models designed to capture the main determinants of fiscal distress episodes and...

FinTech online lending to consumers has grown rapidly in the post-crisis era. As argued by its advocates, one key advantage of FinTech lending is that lenders can predict loan outcomes more accurately by employing complex analytical tools, such as machine learning (ML) methods. This study...

Our study focuses on the Social Cost of Carbon in Relation to Climate Risk. Since information on the social cost of carbon and its effects on the climate is not easily accessible, we searched the internet for pertinent data sets. We have information on emissions from 470 oil and gas companies in...

We comprehensively examine the superiority of various statistical methods and analysts’ forecasts in predicting earnings to provide an updated view on the best surrogate of earnings expectations. We also evaluate the impact of specification choices used in recent studies that implement ML...

Machine learning has become increasingly popular in asset pricing research. However, common modeling choices can lead to biases and overfitting. I show that group-specific machine learning models outperform models trained on a broader cross-section of stocks, challenging the common belief that...

This report investigates the use of artificial intelligence and machine learning (AI-ML) techniques used by the Italian financial intermediaries to assess creditworthiness. The analysis aims to evaluate how financial intermediaries use AI-ML techniques for customer selection and management...

The complexity of machine learning models presents a substantial barrier to their adoption for many investors. The algorithms that generate machine learning predictions are sometimes regarded as “black box”, demanding interpretation and additional explanation. In this paper, we present a...