SUERF Policy Notes and SUERF Policy Briefs
SUERF Policy Notes & Briefs offer accessible and concise, high-quality, authoritative and informed analysis on topical issues related to European money and finance. They make research by SUERF Member institutions and academic researchers available to the SUERF network and beyond. SUERF Policy Notes & Briefs are published weekly on the SUERF webpage. The views expressed are those of the author(s) and not necessarily those of the institution(s) the author(s) is/are affiliated with.
To submit your findings to the SUERF Policy Note and Brief series please contact suerf@oenb.at.
All SUERF Policy Notes & Briefs are available in PDF format.
Stay always up to date and subscribe to the monthly SUERF Policy Notes & Briefs Newsletter!
| Date published | SUERF | Issue | Title | Author(s) |
| Nov 2023 | Policy Brief | No 745 | The BBVA Research Geopolitics Monitor: Tracking Geopolitical Sentiment and Events using Natural Language Techniques | Alvaro Ortiz, Tomasa Rodrigo, Jorge Sicilia |
| Nov 2023 | Policy Brief | No 729 | How and why? Monitoring cement production with satellites and neural networks | Alexandre d’Aspremont, Simon Ben Arous, Jean-Charles Bricongne, Benjamin Lietti, Baptiste Meunier |
| Jun 2023 | Policy Brief | No 599 | Measuring the Temporal Dimension of Text: An Application to Policymaker Speeches | David Byrne, Robert Goodhead, Michael McMahon, Conor Parle |
| May 2023 | Policy Brief | No 580 | Can machine learning help forecasting fiscal crises in emerging and developing countries? | Raffaele De Marchi, Alessandro Moro |
| Apr 2023 | Policy Brief | No 570 | Learning with uncertain inflation target | Stefano Marzioni, Guido Traficante |
| Mar 2023 | Policy Brief | No 553 | Machine Learning Methods in Climate Finance: a Systematic Review | Andrés Alonso-Robisco, José Manuel Carbó, José Manuel Marqués |
| Mar 2023 | Policy Brief | No 544 | Using machine learning to measure financial risk in China | Alexander Al-Haschimi, Apostolos Apostolou, Andres Azqueta-Gavaldon, Martino Ricci |
| Feb 2023 | Policy Brief | No 528 | Federal Reserve Speeches Meet Transformer Models | Christoph Bertsch, Isaiah Hull, Robin L. Lumsdaine, Xin Zhang |
| Feb 2023 | Policy Brief | No 521 | Can Machine Learning Methods Help Nowcast GDP? | Andreas Pick, Jasper de Winter |
| Oct 2022 | Policy Brief | No 455 | Forecasting and Understanding US Inflation with Artificial Intelligence | Philippe Goulet Coulombe |
| Sep 2022 | Policy Brief | No 410 | Should we trust the credit decisions provided by machine learning models? | Andrés Alonso, José Manuel Carbó |
| Jul 2022 | Policy Brief | No 386 | Application of machine learning models and interpretability techniques to identify the determinants of the price of bitcoin | Sergio Gorjón, Jose Manuel Carbó |
| Sep 2021 | Policy Brief | No 181 | A liquidity risk early warning indicator for Italian banks: a machine learning approach | Maria Ludovica Drudi, Stefano Nobili |
| Jul 2021 | Policy Brief | No 141 | Some warning signals about average inflation targeting | Seppo Honkapohja, Nigel McClung |
| Apr 2021 | Policy Brief | No 67 | How do central banks use big data and machine learning? | Sebastian Doerr, Leonardo Gambacorta, Jose Maria Serena |
| Dec 2020 | Policy Note | No 210 | On the risk-adjusted performance of machine learning models in credit default prediction | Andres Alonso, Jose Manuel Carbo |
| Feb 2020 | Policy Note | No 133 | Making sure your bot colleague is less biased than you! | Frank De Jonghe |
© SUERF - The European Money and Finance Forum 2010-2018 .:. Société Universitaire Européenne de Recherches Financières
Privacy Policy .:. Legal notice
Design by draganmarkovic.net