Machine learning in finance has been on the rise in the past decade. The applications of machine learning have become a promising methodological advancement. The paper’s central goal is to use a metadata-based systematic literature review to map the current state of neural networks and machine learning in the finance field. After collecting a large dataset comprised of 5053 documents, we conducted a computational systematic review of the academic finance literature intersected with neural network methodologies, with a limited focus on the documents’ metadata. The output is a meta-analysis of the two-decade evolution and the current state of academic inquiries into financial concepts. Researchers will benefit from a mapping resulting from computational-based methods such as graph theory and natural language processing.

The European sovereign debt crisis has increased the uncertainty regarding the benefits and costs of membership in the Economic and Monetary Union. In this article, we decided not to propose a general model but to focus on one of the important components of the European financial integration. In particular, we develop a structural gravity model, influenced by some very recent theoretical and econometric advancements. This new structural gravity approach provides needed theoretical underpinnings as well as strong support for the econometric estimation of gravity models.

This research analyzes the speeches and interviews of high-level European Central Bank decision makers from 1997 to 2021 to identify clusters of prominent topics. Transparency is a crucial aspect of modern monetary policy, and public speeches and interviews by central bankers play a vital role in achieving it. Our study employs structural topic modeling to compare the prevalence of topics during the Global Financial Crisis, pandemic crisis, and periods of heightened inflation. Additionally, we explore the impact of central bank independence on official rhetoric.

The Mundellian Trilemma has maintained its reputation of a sensible guiding framework regarding policy trade-offs among monetary independence, degree of exchange rate flexibility, and financial openness. This data article offers a unique access to explore the determinants and implications of different Trilemma configurations across 195 countries and over 58 years by providing macroeconomic, financial and policy choice data for 103 variables. The dataset has the potential to contribute to the investigation of the consequences of different Trilemma configurations by income group, size, and geographical position of the economies; to enrich international macroeconomics literature on the role of country idiosyncrasies in navigating through the Mundellian Trilemma, and to formulate policy-relevant conclusions.