Research classified by Journal of Economic Literature (JEL) codes

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Top JEL
/ C: Mathematical and Quantitative Methods
/ / C4: Econometric and Statistical Methods: Special Topics
/ / / C45: Neural Networks and Related Topics

2026

• Alexa Kaminski & Alistair Macaulay & Wenting Song, 2026, "Monetary Policy Narratives and the Transmission of Monetary Policy," School of Economics Discussion Papers, School of Economics, University of Surrey, number 0126, Jan.
• Johan Fourie & Calumet Links, 2026, "Demographic Pressure, Emancipation and Selection into the Great Trek," Working Papers, Stellenbosch University, Department of Economics, number 06/2026.
• Zongwu Cai & Xiyuan Liu & Liangjun Su, 2026, "A Functional-Coefficient VAR Model for Dynamic Quantiles and Its Application to Constructing Nonparametric Financial Network," Journal of Business & Economic Statistics, Taylor & Francis Journals, volume 44, issue 1, pages 162-176, January, DOI: 10.1080/07350015.2025.2511960.
• Sicco Kooiker & Janneke van Brummelen & Julia Schaumburg & Marcin Zamojski, 2026, "Self-driving neural networks for term structure modeling," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 26-007/III, Feb.
• Nicholas Lacoste & Zehra Farooq, 2026, "Optimal Audit Targeting with Machine Learning: Evidence from Pakistan," Working Papers, Tulane University, Department of Economics, number 2603, Feb.
• Santiago Picasso, 2026, "Measuring Services Complexity: A Novel Machine Learning Approach Using U.S. Input–Output Data," Documentos de Trabajo (working papers), Department of Economics - dECON, number 0126, Feb.
• Rony, Sidharth, 2026, "LLM Meets Job Advertisements: Unmasking Skill Premia in the UK," MERIT Working Papers, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT), number 010, Aug, DOI: 10.53330/QGGU3545.
• Oleksandr Castello & Marco Corazza, 2026, "Machine Learning techniques for synthetic data generation in Energy and Financial Markets," Working Papers, Department of Economics, University of Venice "Ca' Foscari", number 2026: 11.
• Antonella Basso & Marco Corazza & Lorenzo Tonon, 2026, "Recurrent Neural Networks for real estate evaluation in the Italian market," Working Papers, Department of Economics, University of Venice "Ca' Foscari", number 2026: 22.
• Yachou Najlae & Abahman Omar & Hakimi Khalid, 2026, "Designing an LSTM-Based Model for Financial Asset Forecasting Using Machine Learning," Central European Economic Journal, Paradigm, volume 13, issue 60, pages 1-23, DOI: 10.2478/ceej-2026-0001.
• Mishra Somanath Kumar & Patri Prasanta, 2026, "Flood Forecasting Using Artificial Intelligence (AI): A Prisma – Guided Systematic Review," Economic and Regional Studies / Studia Ekonomiczne i Regionalne, Paradigm, volume 19, issue 2, pages 345-366, DOI: 10.2478/ers-2026-0020.
• Cafieri Simona & Borrata Gianmarco, 2026, "AI Techniques for Survey Data Quality: Transformers and GANs," Journal of Social and Economic Statistics, Paradigm, volume 15, issue 1, pages 57-68, DOI: 10.2478/jses-2026-0005.
• Węgrzyn Joanna, 2026, "Mining Real Estate Data: A Systematic Review of Text-Based Approaches," Real Estate Management and Valuation, Paradigm, volume 34, issue 1, pages 1-15, DOI: 10.2478/remav-2026-0001.
• Pham Thuy Tu, 2026, "Global Information Uncertainty and Real Estate Stock Valuation in Emerging Markets: an Integrated Behavioral - Theoretical and Machine Learning Framework," Real Estate Management and Valuation, Paradigm, volume 34, issue 1, pages 63-83, DOI: 10.2478/remav-2026-0006.
• Thanh Hang Hoang Thi & Pham Thuy Tu, 2026, "Bridging Predictive Power and Interpretability: A Hybrid Deep Learning Framework for Real Estate Performance Under Systemic Uncertainty," Real Estate Management and Valuation, Paradigm, volume 34, issue 3, pages 62-76, DOI: 10.2478/remav-2026-0025.
• Horák Jakub & Kučera Jiří, 2026, "Trends, Shocks and Predictions in the Price Development of Food-Grade Wheat," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Paradigm, volume 36, issue 3, pages 128-165, DOI: 10.2478/sues-2026-0015.
• Kostiantyn Okhrimenko, 2026, "Painting Price: A Machine Learning Approach to Art Valuation. Proof of Concept and Market Structure Diagnosis," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2026-18.
• Jabeur Salhi & Ichrak Dridi & Oussama Gafrej, 2026, "Unlocking success in tech reward crowdfunding: A hybrid probit-machine learning approach with SHAP-driven feature analysis," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., volume 13, issue 01, pages 1-43, March, DOI: 10.1142/S2424786326500027.
• Sabbor Hussain & Jo-Hui Chen & Dramane Thiombiano, 2026, "The impact of macroeconomic indicators on ETF: A grey relational analysis-machine learning approach," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., volume 13, issue 03, pages 1-30, September, DOI: 10.1142/S2424786326500234.
• Anshul Agrawal & Sanjeev Kadam & Mohd Afjal, 2026, "Evaluating Predictive Robustness of Machine Learning Models During Black Swan Crises: Insights from Bitcoin Price Forecasting," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., volume 17, issue 02, pages 1-22, June, DOI: 10.1142/S1793993325500267.
• Qingqing Ren & Hualing Liu, 2026, "Fraud Detection and Risk Management in Internet Finance," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 14457, ISBN: ARRAY(0x61f07b48), May.
• Krishan Arora & Himanshu Sharma (ed.), 2026, "AI in Finance:Shaping the Future of Intelligent Automation and Financial Services," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number q0542, ISBN: ARRAY(0x5d164688), May.
• Brijlal Mallik & Shivangi Kashyap & Robert Ślepaczuk & Manish Kumar & Dev Kumar Mandal, 2026, "AI-Driven Automation: Revolutionizing Financial Operations and Efficiency," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 1, in: Krishan Arora & Himanshu Sharma, "AI in Finance Shaping the Future of Intelligent Automation and Financial Services".
• Shikha Tuteja & Ravinder Tonk & Moushumi Das & Vishal Jagota & Rajan Vohra, 2026, "Integrating AI with Traditional Financial Systems," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 2, in: Krishan Arora & Himanshu Sharma, "AI in Finance Shaping the Future of Intelligent Automation and Financial Services".
• S. Babu Reddy & R. Ganesh & Nirmalya Pal & Sammarth Choudhury & Riya Sil, 2026, "Securing the Cloud: Mitigating Data Security and Privacy Challenges in Cloud Computing," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 3, in: Krishan Arora & Himanshu Sharma, "AI in Finance Shaping the Future of Intelligent Automation and Financial Services".
• Manik Rakhra & Tiyas Sarkar, 2026, "Transforming Investment Management Strategies: The Impact of Intelligent Systems on Modern Financial Planning Utilizing Robo-Advisors," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 4, in: Krishan Arora & Himanshu Sharma, "AI in Finance Shaping the Future of Intelligent Automation and Financial Services".
• Shivangi Kashyap, 2026, "AI or Bye: Tackling Ethical Dilemmas in Financial Automation," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 5, in: Krishan Arora & Himanshu Sharma, "AI in Finance Shaping the Future of Intelligent Automation and Financial Services".
• Chirra Baburao & Sakha Gangadhara Rama Rao & Lova Baliji & Firdous Ahmad Malik & Krishan Arora, 2026, "Artificial Intelligence in Portfolio Management: Transforming Financial Decision-Making and Optimizing Risk Management," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 6, in: Krishan Arora & Himanshu Sharma, "AI in Finance Shaping the Future of Intelligent Automation and Financial Services".
• Tiyas Sarkar & Manik Rakhra, 2026, "Transforming Indian Banking: The Impact of Intelligent Systems and Process Automation on Financial Innovation," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 7, in: Krishan Arora & Himanshu Sharma, "AI in Finance Shaping the Future of Intelligent Automation and Financial Services".
• Pankhuri Kapoor & Tushinder Preet Kaur, 2026, "AI, Finance, and the Future of Healthcare and Medical Tourism in Delhi," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 8, in: Krishan Arora & Himanshu Sharma, "AI in Finance Shaping the Future of Intelligent Automation and Financial Services".
• Prabhjeet Kaur & Amandeep Kaur & Ramandeep Sandhu & Deepika Ghai & Veer P. Gangwar & Lokesh Jasrai, 2026, "Role of Artificial Intelligence in Cybersecurity: Innovations and Challenges," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 9, in: Krishan Arora & Himanshu Sharma, "AI in Finance Shaping the Future of Intelligent Automation and Financial Services".
• Rajesh Singh & Anita Gehlot & Shaik Vaseem Akram & Mohammed Ismail Iqbal & Praveen Kumar Malik, 2026, "Role of Industry 5.0 in Enabling Technologies for Manufacturing Systems: A Sustainability and Intelligence Perspective," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 10, in: Krishan Arora & Himanshu Sharma, "AI in Finance Shaping the Future of Intelligent Automation and Financial Services".
• Nahita Pathania & Balraj Singh & Isha Batra, 2026, "AI-Based Real-Time Problem-Solving Using Smart Technologies," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 11, in: Krishan Arora & Himanshu Sharma, "AI in Finance Shaping the Future of Intelligent Automation and Financial Services".
• Boughabi, Houssam, 2026, "Fiscal and labour-market conditions and wage-income dynamics in Morocco: A Kaleckian-inspired reduced-form analysis," ZÖSS-Discussion Papers, University of Hamburg, Centre for Economic and Sociological Studies (CESS/ZÖSS), number 131.
• Fausch, Jürg & Frigg, Moreno & Ruenzi, Stefan & Weigert, Florian, 2026, "Machine learning mutual fund flows," CFR Working Papers, University of Cologne, Centre for Financial Research (CFR), number 26-03.
• Weibels, Sebastian, 2026, "Hard to process: Atypical firms and the cross-section of expected stock returns," CFR Working Papers, University of Cologne, Centre for Financial Research (CFR), number 26-05.
• Gondauri, Davit, 2026, "Millennium Economics: Seven Mathematical Architectures for Measuring Global Economic Complexity," EconStor Books, ZBW - Leibniz Information Centre for Economics, number 342001, May.
• Gondauri, Davit, 2026, "Global Hodge-Econometric Modeling of the World Economy: A Regional Benchmark Prototype for Topological Flow Decomposition, Systemic Circulation, Shock Transmission, and Macroeconomic Resilience," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 341543.
• Gondauri, Davit, 2026, "Economic Yang–Mills Mass Gap in Global and Corridor Flow Networks: A Finite-Network Gauge-Econometric Framework for Measuring Systemic Shock Thresholds," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 341544.
• Plüghan, Oliver & Rehfeld, Katharina-Maria, 2026, "Assessing wage inequality with machine learning: Approaches for measuring the adjusted gender pay gap," IU Discussion Papers - Human Resources, IU International University of Applied Sciences, number 4 (März 2026), DOI: 10.56250/4118.
• Werner, Sven & Trotter, Philipp, 2026, "When development finance spurs entrepreneurship: New evidence from 5 million projects using a machine learning classifier," Ruhr Economic Papers, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, number 1205, DOI: 10.4419/96973390.

2025

• Angel Anchev & Borislav Stoyanov & Milka Atanasova & Yaroslav Argirov & Boris Petkov, 2025, "Improving in microhardness of C45 steel obtained via electron beam hardening using a one-factor-at-a-time technique," International Journal of Innovative Research and Scientific Studies, Innovative Research Publishing, volume 8, issue 5, pages 1255-1270.
• Habib ZOUAOUI & Meryem-Nadjat NAAS, 2025, "Portfolio Optimization Based on MPT-LSTM Neural Networks: A case study of Cryptocurrency Markets," Finance, Accounting and Business Analysis, Academic Publishing UNWE, volume 7, issue 1, pages 82-98, June.
• Adedeji Gbadebo, 2025, "Stock Price Forecasting Using a Time-Series Long Short-Term Memory Model," Finance, Accounting and Business Analysis, Academic Publishing UNWE, volume 7, issue 2, pages 304-322, December.
• Daria Dzyabura & Renana Peres & Irina Linevich, 2025, "Color Analytics for Data-Driven Brand Communications," Working Papers, New Economic School (NES), number w0292, Dec.
• Alejandro Adame-Castaneda & Ivan Alejandro Salas-Durazo, 2025, "Politicas publicas para el desarrollo rural integral en Mexico una aproximacion multidimensional al ODS 2 Hambre Cero," Scientia et PRAXIS, AMIDI Editorial, volume 5, issue 10, pages 34-63.
• Ahmet Akusta, 2025, "Predicting Market Sensitivity: The Role of Board Structure in the Beta Coefficient of Software Companies on the Nasdaq Global Select Market," Journal of Finance Letters (Maliye ve Finans Yazıları), Maliye ve Finans Yazıları Yayıncılık Ltd. Şti., volume 40, issue 123, pages 14-34, April, DOI: https://doi.org/10.33203/mfy.159699.
• Johannes Haushofer & Paul Niehaus & Carlos Paramo & Edward Miguel & Michael Walker, 2025, "Targeting Impact versus Deprivation," American Economic Review, American Economic Association, volume 115, issue 6, pages 1936-1974, June, DOI: 10.1257/aer.20221650.
• Marlène Koffi, 2025, "Innovative Ideas and Gender (In)equality," American Economic Review, American Economic Association, volume 115, issue 7, pages 2207-2236, July, DOI: 10.1257/aer.20211811.
• Lorenzo Magnolfi & Jonathon McClure & Alan Sorensen, 2025, "Triplet Embeddings for Demand Estimation," American Economic Journal: Microeconomics, American Economic Association, volume 17, issue 1, pages 282-307, February, DOI: 10.1257/mic.20220248.
• Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2025, "A Machine Learning Approach to Analyze and Support Anticorruption Policy," American Economic Journal: Economic Policy, American Economic Association, volume 17, issue 2, pages 162-193, May, DOI: 10.1257/pol.20210618.
• Sendhil Mullainathan, 2025, "Economics in the Age of Algorithms," AEA Papers and Proceedings, American Economic Association, volume 115, pages 1-23, May, DOI: 10.1257/pandp.20251118.
• Abi Adams & Mathias Fjællegaard Jensen & Barbara Petrongolo, 2025, "The Contribution of Employee-Led and Employer-Led Work Flexibility to the Motherhood Wage Gap," AEA Papers and Proceedings, American Economic Association, volume 115, pages 243-247, May, DOI: 10.1257/pandp.20251015.
• Lefteris Andreadis & Eleni Kalotychou & Manolis Chatzikonstantinou & Christodoulos Louca & Christos A. Makridis, 2025, "Local Heterogeneity in Artificial Intelligence Jobs over Time and Space," AEA Papers and Proceedings, American Economic Association, volume 115, pages 29-34, May, DOI: 10.1257/pandp.20251001.
• Tania Babina & Anastassia Fedyk & Alex He & James Hodson, 2025, "Artificial Intelligence Makes Firm Operating Performance Less Volatile," AEA Papers and Proceedings, American Economic Association, volume 115, pages 35-39, May, DOI: 10.1257/pandp.20251002.
• Avi Goldfarb & Xianda (Henry) He & Florenta Teodoridis, 2025, "Patterns of Artificial Intelligence Adoption by Hospitals," AEA Papers and Proceedings, American Economic Association, volume 115, pages 40-45, May, DOI: 10.1257/pandp.20251003.
• Stefania Albanesi & António Dias da Silva & Juan F. Jimeno & Ana Lamo & Alena Wabitsch, 2025, "AI and Women's Employment in Europe," AEA Papers and Proceedings, American Economic Association, volume 115, pages 46-50, May, DOI: 10.1257/pandp.20251044.
• Benjamin Labaschin & Tyna Eloundou & Sam Manning & Pamela Mishkin & Daniel Rock, 2025, "Extending "GPTs Are GPTs" to Firms," AEA Papers and Proceedings, American Economic Association, volume 115, pages 51-55, May, DOI: 10.1257/pandp.20251045.
• Mauro Cazzaniga & Augustus Panton & Longji Li & Carlo Pizzinelli & Marina M. Tavares, 2025, "A Gender Lens on Labor Market Exposure to AI," AEA Papers and Proceedings, American Economic Association, volume 115, pages 56-61, May, DOI: 10.1257/pandp.20251046.
• Philippe Aghion & Simon Bunel & Xavier Jaravel & Thomas Mikaelsen & Alexandra Roulet & Jakob Søgaard, 2025, "How Different Uses of AI Shape Labor Demand: Evidence from France," AEA Papers and Proceedings, American Economic Association, volume 115, pages 62-67, May, DOI: 10.1257/pandp.20251047.
• Abe Dunn & Eric English & Kyle Hood & Lowell Mason & Brian Quistorff, 2025, "Economic Measurement Lost in a Random Forest? A Case Study of Employment Data," AEA Papers and Proceedings, American Economic Association, volume 115, pages 68-72, May, DOI: 10.1257/pandp.20251103.
• Tatjana Dahlhaus & Reinhard Ellwanger & Gabriela Galassi & Pierre-Yves Yanni, 2025, "From Online Job Postings to Economic Insights: A Machine Learning Approach to Structuring Naturally Occurring Data," AEA Papers and Proceedings, American Economic Association, volume 115, pages 73-78, May, DOI: 10.1257/pandp.20251104.
• Gary Cornwall & Marina Gindelsky, 2025, "Nowcasting Distributional National Accounts for the United States: A Machine Learning Approach," AEA Papers and Proceedings, American Economic Association, volume 115, pages 79-84, May, DOI: 10.1257/pandp.20251105.
• Andrew Caplin, 2025, "Data Engineering for Cognitive Economics," Journal of Economic Literature, American Economic Association, volume 63, issue 1, pages 164-196, March, DOI: 10.1257/jel.20241351.
• Melissa Dell, 2025, "Deep Learning for Economists," Journal of Economic Literature, American Economic Association, volume 63, issue 1, pages 5-58, March, DOI: 10.1257/jel.20241733.
• George Loewenstein & Zachary Wojtowicz, 2025, "The Economics of Attention," Journal of Economic Literature, American Economic Association, volume 63, issue 3, pages 1038-1089, September, DOI: 10.1257/jel.20241665.
• Quintana Pablo & Herrera-Gomez Marcos, 2025, "Redefining Regions in Space and Time: A Deep Learning Method for Spatio-Temporal Clustering," Asociación Argentina de Economía Política: Working Papers, Asociación Argentina de Economía Política, number 4831, Dec.
• Kutlu ERGÃœN, 2025, "From Man to Man with AI Navigation: An Essay on the Homo Economicus Strengthened and Weakened by AI," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, volume 1, issue 3, pages 31-42, September.
• Katleho Makatjane & Claris Shoko, 2025, "Explainable Deep Learning for Financial Risk: Joint VaR and ES Forecasting Using ESRNN in the Bitcoin Market," The African Finance Journal, Africagrowth Institute, volume 27, issue 1, pages 53-69.
• Jieyu Chen & Sebastian Lerch & Melanie Schienle & Tomasz Serafin & Rafal Weron, 2025, "Probabilistic intraday electricity price forecasting using generative machine learning," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/25/05.
• Arkadiusz Lipiecki & Kaja Bilinska & Nikolaos Kourentzes & Rafal Weron, 2025, "Stealing accuracy: Predicting day-ahead electricity prices with Temporal Hierarchy Forecasting (THieF)," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/25/06.
• Ayşegül PEKER & Duygu TUNALI, 2025, "The Comparison of Artificial Neural Networks and Panel Data Analysis on Profitability Prediction: The Case of Real Estate Investment Trusts," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 10, issue 1, pages 160-183, DOI: https://doi.org/10.30784/epfad.1602.
• Yunus Emre Akdoğan, 2025, "The Role of Financial Indicators in the Prediction of Voluntary Carbon Disclosure: A Comparative Analysis with Machine Learning Methods," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 10, issue 3, pages 949-970, DOI: 10.30784/epfad.1651693.
• Çiğdem Yerli, 2025, "Evaluating the Impact of ESG and Decarbonization Metrics on Stock Price Prediction," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 10, issue SI, pages 252-274, DOI: 10.30784/epfad.1669184.
• Yunus Emre Gür & Ahmet İhsan Şimşek & Emre Bulut, 2025, "Artificial Intelligence-Assisted Machine Learning Methods For Forecasting Green Bond Index: A Comparative Analysis," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 9, issue 4, pages 628-655, DOI: https://doi.org/10.30784/epfad.1495.
• Lev A. Bulanov & Alexei V. Kalina & Vadim V. Krivorotov, 2025, "Clustering of Russian Manufacturing Companies by Indicators of Their Financial Condition Using Machine Learning Technologies," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, volume 24, issue 2, pages 584-621, DOI: https://doi.org/10.15826/vestnik.20.
• Lev A. Bulanov & Alexei V. Kalina & Vadim V. Krivorotov, 2025, "Selection of Informative Indicators for Assessing the Economic Security of Russian Companies," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, volume 24, issue 4, pages 1371-1415, DOI: https://doi.org/10.15826/vestnik.20.
• Mona MAHYAOUI & Malak LAZRAK & Rachid KRAMI, 2025, "Modeling volatility with multivariate GARCH models through the integration of deep Learning: A literature review," International Journal of Accounting, Finance, Auditing, Management and Economics, Faculté des Sciences Juridiques, Économiques et Sociales, Université Ibn Tofaïl, volume 6, issue 11, pages 765-778.
• Anas JABOURI & Abdelali EZZIADI, 2025, "Capacité prédictive des technologies émergentes pour le contrôle de gestion de la construction : Une analyse bibliométrique," International Journal of Accounting, Finance, Auditing, Management and Economics, Faculté des Sciences Juridiques, Économiques et Sociales, Université Ibn Tofaïl, volume 6, issue 12, pages 53-74.
• Chaymae SAHRAOUI & Tarek ZARI, 2025, "Targeting Social Assistance Beneficiaries Using Machine Learning: A Poverty Probability-Based Approach," International Journal of Accounting, Finance, Auditing, Management and Economics, Faculté des Sciences Juridiques, Économiques et Sociales, Université Ibn Tofaïl, volume 6, issue 9, pages 303-318.
• Pablo Quintana & Marcos Herrera-Gómez, 2025, "Redefining Regions in Space and Time: A Deep Learning Method for Spatio-Temporal Clustering," Working Papers, Red Nacional de Investigadores en Economía (RedNIE), number 368, Aug.
• Jonathan Garita-Garita & César Ulate-Sancho, 2025, "Forecasting Nominal Exchange Rate using Deep Neural Networks," Documentos de Trabajo, Banco Central de Costa Rica, number 2505, Jul.
• Kevin Ungar & Camelia Oprean-Stan, 2025, "Optimizing Financial Data Analysis: A Comparative Study of Preprocessing Techniques for Regression Modeling of Apple Inc.'s Net Income and Stock Prices," Papers, arXiv.org, number 2501.06587, Jan.
• Arkadiusz Lipiecki & Kaja Bilinska & Nicolaos Kourentzes & Rafal Weron, 2025, "Stealing Accuracy: Predicting Day-ahead Electricity Prices with Temporal Hierarchy Forecasting (THieF)," Papers, arXiv.org, number 2508.11372, Aug, revised Mar 2026.
• Elliot Beck & Franziska Eckert & Linus Kuhne & Helge Liebert & Rina Rosenblatt-Wisch, 2025, "Measuring economic outlook in the news," Papers, arXiv.org, number 2511.04299, Nov, revised Feb 2026.
• Rubén Fernández-Fuertes, 2025, "Monetary Policy Shocks: A New Hope. Large Language Models and Central Bank Communication," BAFFI CAREFIN Working Papers, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy, number 25257.
• Kamelia Ahmadkhan & Abdolreza Yazdani-Chamzini & Alireza Bakhshizadeh & Jonas Šaparauskas & Zenonas Turskis & Niousha Zeidyahyaee, 2025, "Promoting reverse logistics decisions using a new hybrid model based on deep learning and failure mode and effects analysis approaches," E&M Economics and Management, Technical University of Liberec, Faculty of Economics, volume 28, issue 4, pages 79-98, December, DOI: 10.15240/tul/001/2025-4-006.
• Julien Pascal, 2025, "Solving economic models with neural networks without backpropagation," BCL working papers, Central Bank of Luxembourg, number 196, Apr.
• Firdevs Nur UYKUN & Busra Zeynep TEMOCIN, 2025, "A Machine Learning Integrated Portfolio Rebalance Framework with Risk Aversion Adjustment," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, volume 19, issue 2, pages 173-197.
• Pilar García & Diego Torres, 2025, "Perceiving central bank communications through press coverage," Working Papers, Banco de España, number 2505, Jan, DOI: https://doi.org/10.53479/38922.
• Canio Benedetto & Sara Crestini & Alessandro de Gregorio & Marco de Leonardis & Andrea del Monaco & Daniele Gulino & Paolo Massaro & Francesca Monacelli & Lorenzo Rubeo, 2025, "Applying artificial intelligence to support regulatory reporting management: the experience at Banca d'Italia," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 927, Apr.
• Daniele Licari & Canio Benedetto & Daniele Bovi & Praveen Bushipaka & Alessandro De Gregorio & Marco De Leonardis & Tommaso Cucinotta, 2025, "A novel multi-step-prompt approach for LLM-based Q&As on banking supervisory regulations," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 935, Apr.
• Milovan Rankov, 2025, "Komparativna Analiza Modela Kreditnog Skoringa: Konvencijalni Vs Modeli Bazirani Na Mašinskom I Dubokom Učenju," Ekonomske ideje i praksa, Faculty of Economics and Business, University of Belgrade, issue 57, pages 29-45, June.
• Luis Menéndez & Daniel Montolio & Hannes Mueller & Francesco Slataper, 2025, "Breaking the Echo Chamber: Social Media Networks and Political Conflict," Working Papers, Barcelona School of Economics, number 1505, Sep.
• Joan Christine S. Allon-Pineda, 2025, "Inflation Unpacked: Breaking Down the Key Components Using a Neural Phillips Curve," BSP Discussion Paper Series, Bangko Sentral ng Pilipinas, number 202505, Apr.
• Katia Boria & Andrea Luciani & Sabina Marchetti & Marco Viticoli, 2025, "Siamese neural networks for detecting banknote printing defects," IFC Bulletins chapters, Bank for International Settlements, in: Bank for International Settlements, "Data science in central banking: enhancing the access to and sharing of data".
• Hanno Kase & Leonardo Melosi & Matthias Rottner, 2025, "Estimating nonlinear heterogeneous agent models with neural networks," BIS Working Papers, Bank for International Settlements, number 1241, Jan.
• Hanno Kase & Matthias Rottner & Fabio Stohler, 2025, "Generative economic modeling," BIS Working Papers, Bank for International Settlements, number 1312, Dec.
• Elizaveta Volgina, 2025, "Forecasting Inflation Using News Indices," Russian Journal of Money and Finance, Bank of Russia, volume 84, issue 1, pages 26-59, March.
• Anastasia Matevosova, 2025, "Modelling Trust in the Central Bank Using Sentiment Analysis," Russian Journal of Money and Finance, Bank of Russia, volume 84, issue 1, pages 3-25, March.
• Oleg Kryzhanovskiy & Anastasia Mogilat & Zhanna Shuvalova & Dmitry Gvozdev, 2025, "Using LSTM Neural Networks for Nowcasting and Forecasting GVA of Industrial Sectors," Russian Journal of Money and Finance, Bank of Russia, volume 84, issue 1, pages 93-104, March.
• Marcus Buckmann & Quynh Anh Nguyen & Ed Hill, 2025, "Revealing economic facts: LLMs know more than they say," Bank of England Staff Working Paper series, Bank of England, number 1150, Oct.
• Max Ahrens & Dragos Gorduza & Micheal McMahon, 2025, "EcoFinBench – a natural language processing benchmark for economics and finance," Bank of England Staff Working Paper series, Bank of England, number 1163, Dec.
• Marcus Buckmann & Ed Hill, 2025, "Improving text classification: logistic regression makes small LLMs strong and explainable ‘tens-of-shot’ classifiers," Bank of England Staff Working Paper series, Bank of England, number 1127, May.
• Mattera Raffaele, 2025, "Forecasting High-Dimensional Portfolios," Journal of Time Series Econometrics, De Gruyter, volume 17, issue 1, pages 35-67, DOI: 10.1515/jtse-2023-0011.
• Baronchelli Adelaide & Ricciuti Roberto, 2025, "The Battlefield and the Wire: Linking Cyber and Material Conflicts, 2000–2014," Peace Economics, Peace Science, and Public Policy, De Gruyter, volume 31, issue 3, pages 365-380, DOI: 10.1515/peps-2025-0050.
• Goulet Coulombe Philippe, 2025, "To Bag is to Prune," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 29, issue 6, pages 669-697, DOI: 10.1515/snde-2023-0030.
• Clément Gorin & Stephan Heblich & Yanos Zylberberg, 2025, "State of the Art: Economic Development Through the Lens of Paintings," Bristol Economics Discussion Papers, School of Economics, University of Bristol, UK, number 25/793, Apr.
• Bachmair, K. & Schmitz, N., 2025, "Forecasting Macro with Finance," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2574, Nov.
• Benjamin Born & Nora Lamersdorf & Jana-Lynn Schuster & Sascha Steffen, 2025, "From Tweets to Transactions: High-Frequency Inflation Expectations, Consumption, and Stock Returns," CESifo Working Paper Series, CESifo, number 12361.
• Bryan T. Kelly & Boris Kuznetsov & Semyon Malamud & Teng Andrea Xu, 2025, "Artificial Intelligence Asset Pricing Models," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 25-08, Jan.
• Lucien Chaffa & Martin Trépanier & Thierry Warin, 2025, "Beyond PPML: Exploring Machine Learning Alternatives for Gravity Model Estimation in International Trade," CIRANO Working Papers, CIRANO, number 2025s-14, May.
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• Alvaro Riascos Villegas, 2025, "El Potencial Impacto del Aprendizaje de M√°quinas en el Dise√±o de las Pol√≠ticas P√∫blicas en Colombia: Una d√©cada de experiencias," Documentos CEDE, Universidad de los Andes, Facultad de Economía, CEDE, number 21340, Feb.
• Juan Jos√© Rinc√≥n Brice√±o, 2025, "Colombian economic activity nowcasting: addressing nonlinearities and high dimensionality through machine-learning," Documentos CEDE, Universidad de los Andes, Facultad de Economía, CEDE, number 21388, Jun.
• Carlos Castro-Iragorri & Manuel Parra-Diaz, 2025, "Stability focused end to end frameworks for risk budgeting portfolios," Documentos de Trabajo, Universidad del Rosario, number 21367, Mar.
• Adriana María Flórez Laiseca & Elkin Argiro Muñoz Arroyave, 2025, "Business resilience in the Quindío agro-industrial cluster: a forward-looking approach based on business networks," Revista Tendencias, Universidad de Narino, volume 26, issue 02, pages 217-240, July, DOI: 10.22267/rtend.2526.
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• Liao, Yuan & Ma, Xinjie & Neuhierl, Andreas & Schilling, Linda, 2025, "The Uncertainty of Machine Learning Predictions in Asset Pricing," CEPR Discussion Papers, Centre for Economic Policy Research, number 20080, Mar.
• Gorin, Clement & Heblich, Stephan & Zylberberg, Yanos, 2025, "State of the Art: Economic Development Through the Lens of Paintings," CEPR Discussion Papers, Centre for Economic Policy Research, number 20416, Jul.
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• Born, Benjamin & Lamersdorf, Nora & Schuster, Jana-Lynn & Steffen, Sascha, 2025, "From Tweets to Transactions: High-Frequency Inflation Expectations, Consumption, and Stock Returns," CEPR Discussion Papers, Centre for Economic Policy Research, number 20977, Dec.
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• Christopher Mwololo Fred, 2025, "Comparative Analysis of Machine Learning Algorithms for Enhancing Social Media Marketing and Decision-Making in Kenyan SMEs," African Journal of Commercial Studies, African Journal of Commercial Studies, volume 6, issue 1, DOI: 10.59413/ajocs/v6.i.1.4.
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• Echevin, Damien & Fotso, Guy & Bouroubi, Yacine & Coulombe, Harold & Li, Qing, 2025, "Combining survey and census data for improved poverty prediction using semi-supervised deep learning," Journal of Development Economics, Elsevier, volume 172, issue C, DOI: 10.1016/j.jdeveco.2024.103385.
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• Ma, Dan & Zhu, Yanjin & Lee, Chien-Chiang, 2025, "The impact of new energy pilot city policies on urban green innovation: Evidence from China’s city level," Economic Analysis and Policy, Elsevier, volume 87, issue C, pages 585-604, DOI: 10.1016/j.eap.2025.06.029.
• Peng, Michael & Stern, Elisheva R. & Hu, Hanwen, 2025, "Forecasting China bond default with severe class-imbalanced data: A simple learning model with causal inference," Economic Modelling, Elsevier, volume 144, issue C, DOI: 10.1016/j.econmod.2024.106985.
• Limosani, Michele & Millemaci, Emanuele & Mustica, Paolo, 2025, "Do green policies enhance short-term economic growth? Assessing EU Recovery and Resilience Plans through the lens of Sustainable Development Goals," Economic Modelling, Elsevier, volume 147, issue C, DOI: 10.1016/j.econmod.2025.107044.
• Zhang, Heng-Guo & Wang, Shihong & Xie, Yuchi, 2025, "How does news-driven monetary policy frictions affect nonperforming loans?--Taking Chinese commercial banks as an example," The North American Journal of Economics and Finance, Elsevier, volume 76, issue C, DOI: 10.1016/j.najef.2024.102353.
• Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025, "Machine learning the macroeconomic effects of financial shocks," Economics Letters, Elsevier, volume 250, issue C, DOI: 10.1016/j.econlet.2025.112260.
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• Ahrens, Maximilian & Erdemlioglu, Deniz & McMahon, Michael & Neely, Christopher J. & Yang, Xiye, 2025, "Mind your language: Market responses to central bank speeches," Journal of Econometrics, Elsevier, volume 249, issue PC, DOI: 10.1016/j.jeconom.2024.105921.
• Haghighi, Maryam & Joseph, Andreas & Kapetanios, George & Kurz, Christopher & Lenza, Michele & Marcucci, Juri, 2025, "Machine Learning for Economic Policy," Journal of Econometrics, Elsevier, volume 249, issue PC, DOI: 10.1016/j.jeconom.2025.105970.
• Caner, Mehmet & Daniele, Maurizio, 2025, "Deep learning based residuals in non-linear factor models: Precision matrix estimation of returns with low signal-to-noise ratio," Journal of Econometrics, Elsevier, volume 251, issue C, DOI: 10.1016/j.jeconom.2025.106083.
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• Tsionas, Mike & Zelenyuk, Valentin & Zhang, Xibin, 2025, "Goodness-of-fit in production models: A Bayesian perspective," European Journal of Operational Research, Elsevier, volume 324, issue 2, pages 644-653, DOI: 10.1016/j.ejor.2025.01.030.
• Gong, Jue & Wang, Gang-Jin & Zhou, Yang & Xie, Chi, 2025, "Cross-market volatility forecasting with attention-based spatial–temporal graph convolutional networks," Journal of Empirical Finance, Elsevier, volume 83, issue C, DOI: 10.1016/j.jempfin.2025.101639.
• Agakishiev, Ilyas & Härdle, Wolfgang Karl & Kopa, Milos & Kozmik, Karel & Petukhina, Alla, 2025, "Multivariate probabilistic forecasting of electricity prices with trading applications," Energy Economics, Elsevier, volume 141, issue C, DOI: 10.1016/j.eneco.2024.108008.
• Albani, V.V.L. & Marcavillaca, R.T. & Moreira, P.S.E. & Avila, S.L. & Geremia, M. & Piovezan, R.P.B. & Sica, E.T. & Santos, E., 2025, "Short-term forecasting of forward prices in the Brazilian electricity market with a hybrid stochastic-neural network model," Energy Economics, Elsevier, volume 148, issue C, DOI: 10.1016/j.eneco.2025.108651.
• Hanus, Luboš & Baruník, Jozef, 2025, "Learning the probability distributions of day-ahead electricity prices," Energy Economics, Elsevier, volume 152, issue C, DOI: 10.1016/j.eneco.2025.108988.
• Dragicevic, Arnaud Z. & Thongtai, Chanon & Pecora, Nicolò, 2025, "Assessing the potential for biofuel production within a conventional fuel system," Energy Economics, Elsevier, volume 152, issue C, DOI: 10.1016/j.eneco.2025.108991.
• Gupta, Aparna & Osipov, Denis, 2025, "Performance risk scoring of risk-free renewable generation bids," Energy, Elsevier, volume 338, issue C, DOI: 10.1016/j.energy.2025.138796.
• Magazzino, Cosimo & Gattone, Tulia & Horky, Florian, 2025, "Economic and financial development as determinants of crypto adoption," International Review of Financial Analysis, Elsevier, volume 103, issue C, DOI: 10.1016/j.irfa.2025.104217.
• Mertzanis, Charilaos, 2025, "Artificial intelligence and investment management: Structure, strategy, and governance," International Review of Financial Analysis, Elsevier, volume 107, issue C, DOI: 10.1016/j.irfa.2025.104599.
• Zhao, Dongshuai & Wang, Zhongli & Schweizer-Gamborino, Florian & Sornette, Didier, 2025, "Polytope Fraud Theory," International Review of Financial Analysis, Elsevier, volume 97, issue C, DOI: 10.1016/j.irfa.2024.103734.
• Sina, A. & Billio, M. & Dufour, A. & Rocciolo, F. & Varotto, S., 2025, "The systemic risk of leveraged and covenant-lite loan syndications," International Review of Financial Analysis, Elsevier, volume 97, issue C, DOI: 10.1016/j.irfa.2024.103738.
• Hu, Nan & Yin, Xuebao & Yao, Yuhang, 2025, "A novel HAR-type realized volatility forecasting model using graph neural network," International Review of Financial Analysis, Elsevier, volume 98, issue C, DOI: 10.1016/j.irfa.2024.103881.
• François, Pascal & Gauthier, Geneviève & Godin, Frédéric & Mendoza, Carlos Octavio Pérez, 2025, "Is the difference between deep hedging and delta hedging a statistical arbitrage?," Finance Research Letters, Elsevier, volume 73, issue C, DOI: 10.1016/j.frl.2024.106590.
• Hu, Wendi & Shao, Chujian & Zhang, Wenyu, 2025, "Predicting U.S. bank failures and stress testing with machine learning algorithms," Finance Research Letters, Elsevier, volume 75, issue C, DOI: 10.1016/j.frl.2025.106802.
• Doğan, Murat & Sayılır, Özlem & Komath, Muhammed Aslam Chelery & Çimen, Emre, 2025, "Prediction of market value of firms with corporate sustainability performance data using machine learning models," Finance Research Letters, Elsevier, volume 77, issue C, DOI: 10.1016/j.frl.2025.107085.
• Lu, Zhichao & Xu, Yuhong & Zhang, Yue & Zhao, Xinyao, 2025, "Is it difficult to predict the price movements of high-volatility assets," Finance Research Letters, Elsevier, volume 85, issue PB, DOI: 10.1016/j.frl.2025.107980.
• Liu, Ying & Liu, Shuang & Lu, Yu, 2025, "Supply chain financial risk assessment: A modified graph attention neural network," Finance Research Letters, Elsevier, volume 86, issue PA, DOI: 10.1016/j.frl.2025.108285.
• Chon, Sora & Kim, Jaehoon & Kim, Jaeho, 2025, "Multifaceted variability in LLM-driven stock recommendations," Finance Research Letters, Elsevier, volume 86, issue PG, DOI: 10.1016/j.frl.2025.108923.
• Lütkebohmert, Eva & Sester, Julian & Shen, Hongyi, 2025, "Name concentration risk in Multilateral Development Banks’ portfolios: Measurement and capital adequacy implications," Global Finance Journal, Elsevier, volume 67, issue C, DOI: 10.1016/j.gfj.2025.101154.
• Baumgärtner, Martin & Zahner, Johannes, 2025, "Whatever it takes to understand a central banker — Embedding their words using neural networks," Journal of International Economics, Elsevier, volume 157, issue C, DOI: 10.1016/j.jinteco.2025.104101.
• Padha, Vimarsh & Chaubal, Aditi, 2025, "Multiscale foreign exchange dynamics in India: A wavelet approach," International Economics, Elsevier, volume 184, issue C, DOI: 10.1016/j.inteco.2025.100652.
• Li, Shuyue & Yarovaya, Larisa & Mishra, Tapas, 2025, "Machine learning, memory and efficiency in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 105, issue C, DOI: 10.1016/j.intfin.2025.102210.
• Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2025, "Predicting IPO first-day returns: Evidence from machine learning analyses," Journal of Banking & Finance, Elsevier, volume 178, issue C, DOI: 10.1016/j.jbankfin.2025.107500.
• Nutarelli, Federico & Edet, Samuel & Gnecco, Giorgio & Riccaboni, Massimo, 2025, "Predicting the technological complexity of global cities based on unsupervised and supervised machine learning methods," Journal of Economic Behavior & Organization, Elsevier, volume 234, issue C, DOI: 10.1016/j.jebo.2025.107011.
• Carow, Johannes & Witzig, Niklas M., 2025, "Time pressure and strategic risk-taking in professional chess," Journal of Economic Behavior & Organization, Elsevier, volume 238, issue C, DOI: 10.1016/j.jebo.2025.107218.
• Liu, Mengxiao & Wang, Luhang & Yi, Yimin, 2025, "Quality innovation, cost innovation, exporting, and firm productivity evolution: Evidence from the Chinese electronics industry," Journal of Economic Behavior & Organization, Elsevier, volume 239, issue C, DOI: 10.1016/j.jebo.2025.107232.
• Mertzanis, Charilaos & Kampouris, Ilias & Samitas, Aristeidis, 2025, "Climate change and U.S. Corporate bond market activity: A machine learning approach," Journal of International Money and Finance, Elsevier, volume 151, issue C, DOI: 10.1016/j.jimonfin.2024.103259.
• Ashwin, Julian & Beaudry, Paul & Ellison, Martin, 2025, "Neural network learning for nonlinear economies," Journal of Monetary Economics, Elsevier, volume 149, issue C, DOI: 10.1016/j.jmoneco.2024.103723.
• Kampouris, Ilias & Mertzanis, Charilaos & Samitas, Aristeidis, 2025, "Natural disaster shocks and commodity market volatility: A machine learning approach," Pacific-Basin Finance Journal, Elsevier, volume 90, issue C, DOI: 10.1016/j.pacfin.2024.102618.
• Chiu, I-Chan & Hung, Mao-Wei, 2025, "Finance-specific large language models: Advancing sentiment analysis and return prediction with LLaMA 2," Pacific-Basin Finance Journal, Elsevier, volume 90, issue C, DOI: 10.1016/j.pacfin.2024.102632.
• Cheng, Zijian & Li, Tianze & Liu, Zhangxin (Frank), 2025, "Unveiling the veil: Identifying potential shell firms using machine learning approaches," Pacific-Basin Finance Journal, Elsevier, volume 92, issue C, DOI: 10.1016/j.pacfin.2025.102798.
• Li, Xiao-Xin & Xie, Chi & Wang, Gang-Jin & Zhu, You & Li, Zhao-Chen & Zhang, Zhi-Yu, 2025, "Enhancing stock market return predictability by using a novel autoencoder-based aggregate EPU index," Pacific-Basin Finance Journal, Elsevier, volume 93, issue C, DOI: 10.1016/j.pacfin.2025.102873.
• Morier, Bruno & Valls Pereira, Pedro L., 2025, "Forecasting intraday volatility and densities using deep learning," The Quarterly Review of Economics and Finance, Elsevier, volume 104, issue C, DOI: 10.1016/j.qref.2025.102076.
• Kumar, Satish & Rao, Amar & Dhochak, Monika, 2025, "Hybrid ML models for volatility prediction in financial risk management," International Review of Economics & Finance, Elsevier, volume 98, issue C, DOI: 10.1016/j.iref.2025.103915.
• Jiang, Yifu & Olmo, Jose & Atwi, Majed, 2025, "High-dimensional multi-period portfolio allocation using deep reinforcement learning," International Review of Economics & Finance, Elsevier, volume 98, issue C, DOI: 10.1016/j.iref.2025.103996.
• Thomas Persson, 2025, "Machine Learning Methods," Journal of Economics and Econometrics, Economics and Econometrics Society, volume 68, issue 2, pages 106-129.
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• Koukorinis, Andreas & Peters, Gareth W. & Germano, Guido, 2025, "Generative-discriminative machine learning models for high-frequency financial regime classification," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 128016, Jun.
• Martín, Roberto Spacey & Ranger, Nicola & Schimanski, Tobias & Leippold, Markus, 2025, "Empirically assessing corporate adaptation and resilience disclosure using AI," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 130809, Sep.
• Artur Kulpa & Grzegorz Wojarnik, 2025, "Prompt Engineering in Finance: An LLM-Based Multi-Agent Architecture for Decision Support," European Research Studies Journal, European Research Studies Journal, volume 0, issue 3, pages 1201-1217.
• Viktor Ivanovich Blanutsa, 2025, "Creating the First Autonomous Systems of Internet in Siberia as a Spatial Diffusion of Innovations," Spatial Economics=Prostranstvennaya Ekonomika, Economic Research Institute, Far Eastern Branch, Russian Academy of Sciences (Khabarovsk, Russia), issue 1, pages 7-32, DOI: https://dx.doi.org/10.14530/se.2025.
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• Monica Bonacina & Romolo Consigna Tokong, 2025, "Is Italy on Track? A Data-Driven Forecast for Road Transport Decarbonisation by 2030," Working Papers, Fondazione Eni Enrico Mattei, number 2025.19, Sep.
• Leland D. Crane & Xiaoyu Ge & Flora Haberkorn & Rithika Iyengar & Seung Jung Lee & Viviana Luccioli & Ryan Panley & Nitish R. Sinha, 2025, "LLM on a Budget: Active Knowledge Distillation for Efficient Classification of Large Text Corpora," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2025-108, Dec, DOI: 10.17016/FEDS.2025.108.
• Martin Neil Baily & David M. Byrne & Aidan T. Kane & Paul E. Soto, 2025, "Generative AI at the Crossroads: Light Bulb, Dynamo, or Microscope?," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2025-053, Jul, DOI: 10.17016/FEDS.2025.053.
• Ahmed Ä°hsan ŞİMÅžEK, 2025, "Using Stacked Generalization Model in Stock Price Forecasting: A Comparative Analysis on BIST100 Index," Fiscaoeconomia, Tubitak Ulakbim JournalPark (Dergipark), issue 1, DOI: 10.25295/fsecon.1444407.
• Cem KARTAL & Mürüvet ACAR KARABOÄžA & Zafer ÖZDÄ°L, 2025, "Examining the Effect of Ease of Doing Business on Exports in OECD Countries," Fiscaoeconomia, Tubitak Ulakbim JournalPark (Dergipark), issue 2, DOI: 10.25295/fsecon.1597719.
• Kamil Abdullah EŞİDÄ°R, 2025, "Forecasting Cell Phone Ownership among Children with TurkStat Micro Data: Comparative Performance of Machine Learning Models," Fiscaoeconomia, Tubitak Ulakbim JournalPark (Dergipark), issue 3, DOI: 10.25295/fsecon.1594029.
• Philippe Aghion & Simon Bunel & Xavier Jaravel & Thomas Mikaelsen & Alexandra Roulet & Jakob Søgaard, 2025, "How Different Uses of AI Shape Labor Demand: Evidence from France," Post-Print, HAL, number halshs-05144088, May, DOI: 10.1257/pandp.20251047.
• Philippe Aghion & Simon Bunel & Xavier Jaravel & Thomas Mikaelsen & Alexandra Roulet & Jakob Søgaard, 2025, "How Different Uses of AI Shape Labor Demand: Evidence from France," PSE-Ecole d'économie de Paris (Postprint), HAL, number halshs-05144088, May, DOI: 10.1257/pandp.20251047.
• Carlo Drago & Massimo Arnone & Angelo Leogrande, 2025, "Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context," Working Papers, HAL, number hal-04994903, Mar.
• Angelo Leogrande & Nicola Magaletti & Valeria Notarnicola & Mauro Di Molfetta & Stefano Mariani, 2025, "Data-Driven Welding Quality Assessment: Leveraging IoT and Machine Learning in Industrial Practice," Working Papers, HAL, number hal-05043506, Apr.
• Margareth Antonicelli & Carlo Drago & Alberto Costantiello & Angelo Leogrande, 2025, "Analyzing Income Inequalities across Italian regions: Instrumental Variable Panel Data, K-Means Clustering and Machine Learning Algorithms," Working Papers, HAL, number hal-05091404, May.
• Carlo Drago & Alberto Costantiello & Marco Savorgnan & Angelo Leogrande, 2025, "Driving AI Adoption in the EU: A Quantitative Analysis of Macroeconomic Influences," Working Papers, HAL, number hal-05102974, Jun.
• Onofrio Resta & Emanuela Resta & Alberto Costantiello & Piergiuseppe Liuzzi & Angelo Leogrande, 2025, "Environmental Complexity and Respiratory Health: A Data-Driven Exploration Across European Regions," Working Papers, HAL, number hal-05243548, Sep.
• Ibhar C. Beramendi Illanes & Ivette Illanes Fajardo, 2025, "Determinantes del empleo informal en Bolivia: Un análisis conjunto de técnicas econométricas tradicionales y métodos de machine learning," Investigación & Desarrollo, Universidad Privada Boliviana, number 1125, DOI: http://10.0.93.73/idupbo.025.2-5e.
• Koji Takahashi & Joon Suk Park, 2025, "Generative AI for Surveys on Payment Apps: AIs' View on Privacy and Technology," IMES Discussion Paper Series, Institute for Monetary and Economic Studies, Bank of Japan, number 25-E-13, Sep.
• Bhaskar Tripathi & Rakesh Kumar Sharma, 2025, "Cryptocurrency Exchanges and Traditional Markets: A Multi-algorithm Liquidity Comparison Using Multi-criteria Decision Analysis," Computational Economics, Springer;Society for Computational Economics, volume 65, issue 5, pages 2649-2677, May, DOI: 10.1007/s10614-024-10655-9.
• Edson Pindza & Jules Clement & Sutene Mwambi & Nneka Umeorah, 2025, "Neural Network for Valuing Bitcoin Options Under Jump-Diffusion and Market Sentiment Model," Computational Economics, Springer;Society for Computational Economics, volume 66, issue 3, pages 2305-2342, September, DOI: 10.1007/s10614-024-10792-1.
• Zareh Asatryan & Carlo Birkholz & Friedrich Heinemann, 2025, "Evidence-based policy or beauty contest? An LLM-based meta-analysis of EU cohesion policy evaluations," International Tax and Public Finance, Springer;International Institute of Public Finance, volume 32, issue 2, pages 625-655, April, DOI: 10.1007/s10797-024-09875-4.