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

2025

• Drago, Carlo & Costantiello, Alberto & Savorgnan, Marco & Leogrande, Angelo, 2025, "Driving AI Adoption in the EU: A Quantitative Analysis of Macroeconomic Influences," MPRA Paper, University Library of Munich, Germany, number 124973, Jun.
• Bahaa Aly, Tarek, 2025, "Nonlinear Macroeconomic Granger Causality: An ANN Input Occlusion Approach on MSSA-Denoised Data," MPRA Paper, University Library of Munich, Germany, number 125453, Jul.
• Resta, Onofrio & Resta, Emanuela & Costantiello, Alberto & Liuzzi, Piergiuseppe & Leogrande, Angelo, 2025, "Environmental Complexity and Respiratory Health: A Data-Driven Exploration Across European Regions," MPRA Paper, University Library of Munich, Germany, number 126073, Sep.
• Boughabi, Houssam, 2025, "Inflation Persistence and Involuntary Unemployment in Pakistan: A Keynesian Econometric Study," MPRA Paper, University Library of Munich, Germany, number 126294, Aug.
• Pinto, Claudio, 2025, "Combining machine learning techniques with NDEA methodology: the use of R.F. and A.N.N," MPRA Paper, University Library of Munich, Germany, number 126539, Sep.
• Slonimczyk, Fabian, 2025, "This Candidate is [MASK]. Prompt-based Sentiment Extraction and Reference Letters," MPRA Paper, University Library of Munich, Germany, number 126675, Oct.
• Kikuchi, Tatsuru, 2025, "Network Contagion Dynamics in European Banking: A Navier-Stokes Framework for Systemic Risk Assessment," MPRA Paper, University Library of Munich, Germany, number 126729.
• Vasilios Plakandaras & Rangan Gupta & Qiang Ji, 2025, "Unraveling Financial Fragility of Global Markets Using Machine Learning," Working Papers, University of Pretoria, Department of Economics, number 202511, Mar.
• Manuel Rosinus, 2025, "Comparison of Classical Arima Forecasting Methods to the Machine Learning LSTM Method: a Case Study on DAX® 50 ESG Index," ACTA VSFS, University of Finance and Administration, volume 19, issue 1, pages 32-52.
• Martín Saldias & Sophia Mizinski, 2025, "Sectoral Interconnectedness in Portugal and the Role of Non-Bank Financial Institutions," Working Papers, Banco de Portugal, Economics and Research Department, number w202521.
• Yunus Emre Korkmaz & Serpil Altınırmak & Çağlar Karamaşa, 2025, "Makine Öğrenmesi Yöntemleri ile Kripto Varlık Fiyat Tahmini ve En İyi Yöntemin ÇKKV Teknikleri ile Belirlenmesi
  [Cryptocurrency Price Prediction Using Machine Learning Methods and Determining the Best Method Using MCDM Techniques]," Business and Economics Research Journal, Bursa Uludag University, Faculty of Economics and Administrative Sciences, volume 16, issue 4, pages 463-492, October, DOI: 10.20409/berj.2025.477.
• Igor A. Kokorev & Vladimir M. Kiselev & Vladimir V. Velikorossov & Andrey L. Poltarykhin & Galiya S. Ukubassova, 2025, "Use of AI technologies in improving the business processes of companies," Economic Consultant, Scientific and Educational Initiative LLC, issue 4, pages 4-16, December, DOI: 10.46224/ecoc.2025.4.1.
• Weige Huang & Zishan Chen & Hua Wang, 2025, "Does the History of Investment Matter for an IPO? A Machine Learning Approach," SAGE Open, , volume 15, issue 4, pages 21582440251, November, DOI: 10.1177/21582440251383077.
• Carlos Moreno-Pérez & Marco Minozzo, 2025, "Natural language processing and financial markets: semi-supervised modelling of coronavirus and economic news," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), volume 19, issue 3, pages 769-793, September, DOI: 10.1007/s11634-024-00596-4.
• Zaheer Anwer & Ashraf Khan & Muhammad Abubakr Naeem & Aviral Kumar Tiwari, 2025, "Modelling systemic risk of energy and non-energy commodity markets during the COVID-19 pandemic," Annals of Operations Research, Springer, volume 345, issue 2, pages 1193-1227, February, DOI: 10.1007/s10479-022-04879-x.
• Sabri Boubaker & Zhenya Liu & Yifan Zhang, 2025, "Forecasting oil commodity spot price in a data-rich environment," Annals of Operations Research, Springer, volume 345, issue 2, pages 685-702, February, DOI: 10.1007/s10479-022-05004-8.
• Ludovic Goudenège & Andrea Molent & Antonino Zanette, 2025, "Backward hedging for American options with transaction costs," Decisions in Economics and Finance, Springer;Associazione per la Matematica, volume 48, issue 1, pages 541-569, June, DOI: 10.1007/s10203-024-00472-y.
• D. Nam & D. B. Skillicorn, 2025, "Detecting Pump &Dump stock market manipulation from online forums," Digital Finance, Springer, volume 7, issue 1, pages 1-20, March, DOI: 10.1007/s42521-024-00121-4.
• Monia Antar & Tahar Tayachi, 2025, "Partial dependence analysis of financial ratios in predicting company defaults: random forest vs XGBoost models," Digital Finance, Springer, volume 7, issue 4, pages 997-1012, December, DOI: 10.1007/s42521-025-00135-6.
• Ameer Tamoor Khan & Shuai Li & Xinwei Cao, 2025, "Bridging finance and AI: a comprehensive survey of large language models in financial system," Digital Finance, Springer, volume 7, issue 4, pages 679-701, December, DOI: 10.1007/s42521-025-00146-3.
• William Nordansjö & Fredrik Fourong & Muhammad Qasim, 2025, "Financial sentiment analysis with FUNNEL: filtered UNion for NER-based ensemble labeling," Digital Finance, Springer, volume 7, issue 4, pages 725-744, December, DOI: 10.1007/s42521-025-00162-3.
• Valeriia Baklanova, 2025, "The relationships between RedditSI and BTC exchange characteristics: Do Reddit users still control the market?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, volume 15, issue 1, pages 285-306, March, DOI: 10.1007/s40822-024-00304-9.
• Saïd Toumi & Abdussalam Aljadani & Hassen Toumi & Bilel Ammouri & Moez Dhiabi, 2025, "AI for climate change: unveiling pathways to sustainable development through greenhouse gas emission predictions," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, volume 15, issue 4, pages 923-963, December, DOI: 10.1007/s40822-024-00295-7.
• Yang Zhou & Chi Xie & Gang-Jin Wang & Jue Gong & You Zhu, 2025, "Forecasting cryptocurrency volatility: a novel framework based on the evolving multiscale graph neural network," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 11, issue 1, pages 1-52, December, DOI: 10.1186/s40854-025-00768-x.
• Yeonchan Kang & Doojin Ryu & Robert I. Webb, 2025, "How well do machine learning models in finance work?," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 11, issue 1, pages 1-30, December, DOI: 10.1186/s40854-025-00870-0.
• Aleksandar Arandjelović & Thorsten Rheinländer & Pavel V. Shevchenko, 2025, "Importance sampling for option pricing with feedforward neural networks," Finance and Stochastics, Springer, volume 29, issue 1, pages 97-141, January, DOI: 10.1007/s00780-024-00549-x.
• Dina Ait Lahcen & Nour-Eddin Amghar, 2025, "Econometric modeling for proactive risk management of financial failure in Moroccan SMEs: a stepwise logistic regression approach in python," Future Business Journal, Springer, volume 11, issue 1, pages 1-25, December, DOI: 10.1186/s43093-025-00613-8.
• D. Dharani & K. Lavanya Latha, 2025, "Beyond the Click: Unveiling the Role of User-Centric Factors in Public Perception and Engagement with Search Engine Marketing," International Journal of Global Business and Competitiveness, Springer, volume 20, issue 2, pages 143-154, December, DOI: 10.1007/s42943-025-00121-0.
• Aditi Nag, 2025, "Tourism Planning Meets AI: A Fuzzy-Logic and PLS-SEM–ANN Framework for Stakeholder-Centric Destination Competitiveness Forecasting," International Journal of Global Business and Competitiveness, Springer, volume 20, issue 2, pages 117-131, December, DOI: 10.1007/s42943-025-00125-w.
• Joost Bosker & Marc Gürtler & Marvin Zöllner, 2025, "Machine learning-based variable selection for clustered credit risk modeling," Journal of Business Economics, Springer, volume 95, issue 4, pages 617-652, May, DOI: 10.1007/s11573-024-01213-8.
• Manu Sharma & Vinish Kathuria, 2025, "Macroeconomic Nowcasting: What can Central Banks Learn from a Structured Literature Review?," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), volume 23, issue 2, pages 333-388, June, DOI: 10.1007/s40953-024-00421-x.
• Manuel Muth & Michael Lingenfelder & Gerd Nufer, 2025, "The application of machine learning for demand prediction under macroeconomic volatility: a systematic literature review," Management Review Quarterly, Springer, volume 75, issue 3, pages 2759-2802, September, DOI: 10.1007/s11301-024-00447-8.
• Yong-Hyong Kim & Song-Jun Ham & Chong-Sim Ri & Won-Hyok Kim & Wi-Song Ri, 2025, "Application of empirical wavelet transform, particle swarm optimization, gravitational search algorithm and long short-term memory neural network to copper price forecasting," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, volume 24, issue 1, pages 151-169, January, DOI: 10.1007/s10258-024-00252-x.
• Ioannis A. Nikas & Athanasios Koutras & Antonopoulou Theodora, 2025, "A Conceptual Framework for Developing and Evaluating Personalized Tourist Recommendation Systems Using Large Language Models," Springer Proceedings in Business and Economics, Springer, in: Vicky Katsoni & Carlos Costa, "Innovation and Creativity in Tourism, Business and Social Sciences", DOI: 10.1007/978-3-031-78471-2_20.
• Marius Brede & Hannes Gerstel & Arnt Wöhrmann & Andreas Bausch, 2025, "Mind the gap: the effect of cultural distance on mergers and acquisitions—evidence from glassdoor reviews," Review of Managerial Science, Springer, volume 19, issue 8, pages 2279-2326, August, DOI: 10.1007/s11846-024-00811-8.
• Ulysses Araújo Bispo & Mathias Schneid Tessmann, 2025, "Does Deep Learning with Multilayer Perceptron Perform Well in Predicting Credit Risk?," Journal of Applied Finance & Banking, SCIENPRESS Ltd, volume 15, issue 4, pages 1-1.
• Dmitrii Gimmelberg & Alexey Belinskiy & Alexey Belinskiy & Marta Głowacka & Marta Głowacka & Sergei Korotkii & Valentin Artamonov & Iveta Ludviga, 2025, "Market moves predictions using Retrieval-Augmented Generation (RAG) analysis of capital market expert opinions in social media," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, volume 13, issue 1, pages 175-188, September, DOI: 10.9770/w9365778559.
• Yao Luo & Peijun Sang, 2025, "Efficient Estimation of Structural Models via Sieves," Working Papers, University of Toronto, Department of Economics, number tecipa-801, Jun.
• James Alm & Rida Belahouaoui, 2025, "Emerging Trends in Tax Fraud Detection Using Artificial Intelligence-Based Technologies," Working Papers, Tulane University, Department of Economics, number 2511, Nov.
• Eoin T. Flaherty & Sean O'Boyle & Giselle Myles, 2025, "Sectorally Concentrated? The Irish Economy in European Context," Working Papers, Geary Institute, University College Dublin, number 202503, Jan.
• Capilla, Javier & Alcaraz, Alba & Valarezo, à ngel & García-Hiernaux, Alfredo & Pérez Amaral, Teodosio, 2025, "Eco-RETINA: a green flexible algorithm for model building," Documentos de Trabajo del ICAE, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico, number 2025-01.
• Mario Alberto Morales Sánchez & Héctor Eduardo Díaz Rodríguez, 2025, "Level of development as a determinant of innovation capability. Evidence for 132 economies using artificial neural networks," Estudios de Economia, University of Chile, Department of Economics, volume 52, issue 1 Year 20, pages 59-95, June.
• Diana Barro & Antonella Basso & Marco Corazza & Guglielmo Alessandro Visentin, 2025, "A Neural Network-VAR for Long-Term Forecasting: An Application to Monetary Policy Effects in the Euro Area," Working Papers, Department of Economics, University of Venice "Ca' Foscari", number 2025: 24.
• Ardelia L. Amardana & Diana Barro & Marco Corazza, 2025, "Sustainability in LSTM Price Prediction for Portfolio Optimization in the European Market," Working Papers, Department of Economics, University of Venice "Ca' Foscari", number 2025: 25.
• ANGHEL, Bogdan Ionut, 2025, "Forecasting Stock Market Liquidity With Machine Learning: An Empirical Evaluation In The German Market," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", volume 29, issue 2, pages 34-47, June.
• Michalak Joanna, 2025, "Affect Indicators for Stock Market Forecasting," Central European Economic Journal, Sciendo, volume 12, issue 59, pages 412-432, DOI: 10.2478/ceej-2025-0024.
• Perez Katarzyna & Bartkowiak Marcin, 2025, "Chasing Returns of Open-End Investment Funds Using Recurrent Neural Networks. A Long-Term Study," Central European Economic Journal, Sciendo, volume 12, issue 59, pages 49-65, DOI: 10.2478/ceej-2025-0004.
• Cellmer Radosław & Kobylińska Katarzyna, 2025, "Housing Price Prediction - Machine Learning and Geostatistical Methods," Real Estate Management and Valuation, Sciendo, volume 33, issue 1, pages 1-10, DOI: 10.2478/remav-2025-0001.
• Ergenç Cansu & Aktaş Rafet, 2025, "A Supervised Machine Learning in Financial Forecasting: Identifying Effective Models for the BIST100 Index," Review of Economic Perspectives, Sciendo, volume 25, issue 1, pages 66-90, DOI: 10.2478/revecp-2025-0005.
• Neagu Dan Claudiu, 2025, "Bertweetro: Pre-Trained Language Models for Romanian Social Media Content," Studia Universitatis Babeș-Bolyai Oeconomica, Sciendo, volume 70, issue 1, pages 83-111, DOI: 10.2478/subboec-2025-0005.
• Ungar Kevin & Oprean-Stan Camelia, 2025, "Optimizing Financial Data Analysis: A Comparative Study of Preprocessing Techniques for Regression Modeling of Apple Inc.’S Net Income and Stock Prices," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Sciendo, volume 35, issue 1, pages 49-82, DOI: 10.2478/sues-2025-0004.
• Jędrzej Maskiewicz & Paweł Sakowski, 2025, "Can Artificial Intelligence Trade the Stock Market?," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2025-14.
• Yufei Sun, 2025, "A survey of statistical arbitrage pair trading with machine learning, deep learning, and reinforcement learning methods," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2025-22.
• Zofia Bracha & Jakub Michańków & Paweł Sakowski, 2025, "Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2025-25.
• Muhammad Farooq & Ayesha Ayesha & Ahtasham Gul, 2025, "Forecasting the Future: Neural Networks vs. Time Series in Pakistan’s CPI," Economic Research Guardian, Mutascu Publishing, volume 15, issue 2, pages 219-231, December.
• Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2025, "Corrigendum: Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, volume 93, issue 4, pages 1491-1496, July, DOI: 10.3982/ECTA22259.
• Paola Bongini & Monica Rossolini & Andrea Maurino & Francesco Osborne, 2025, "The Information Power Of Social Media For Investment Decisions: An Ai-Driven Analysis Of Reddit Posts," Journal of Financial Management, Markets and Institutions (JFMMI), World Scientific Publishing Co. Pte. Ltd., volume 13, issue 02, pages 1-35, December, DOI: 10.1142/S2282717X25500082.
• Saira Yamin & Saqib Gulzar, 2025, "Multiples And Stock Price, New Approach For Relative Valuation Through Neural Network," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., volume 70, issue 04, pages 953-971, June, DOI: 10.1142/S0217590820480045.
• Kian Guan Lim, 2025, "Machine Learning in Business Finance using Python," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 14271, ISBN: ARRAY(0x5fcec600), September.
• Horst Simon (ed.), 2025, "Transactions of ADIA Lab:Interdisciplinary Advances in Data and Computational Science," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 14310, ISBN: ARRAY(0x60210760), September.
• Alexandre V. Antonov & Koushik Balasubramanian & Alexander Lipton & Marcos Lopez de Prado, 2025, "A Geometric Approach to Asset Allocation with Investor Views," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 1, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Álvaro F. Macías & Jorge P. Zubelli, 2025, "Static Liquidation and Risk Management," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 2, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Alexandre Antonov & Alexander Lipton & Marcos Lopez de Prado, 2025, "Overcoming Markowitz’s Instability with the Help of the Hierarchical Risk Parity (HRP): Theoretical Evidence," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 3, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Vinicius V. L. Albani & Leonardo Sarmanho & Jorge P. Zubelli, 2025, "A Statistical Learning Approach to Local Volatility Calibration and Option Pricing," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 4, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Laura Sanz Martín & Javier Parra Domínguez & Guillermo Rivas & Alexander Lipton & Juan Manuel Corchado, 2025, "Challenges of Artificial Intelligence and Quantum Potential in the Digital Economy: A Literature Review," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 5, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Manuel J. Cobo & Nadia Karina Gamboa-Rosales & José Ricardo López-Robles & Enrique Herrera-Viedma, 2025, "Exploring the Digital Economy: Current Research Trends, Challenges, and Opportunities," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 6, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Thomas Hardjono & Alexander Lipton & Alex Pentland, 2025, "Interoperability Challenges in Tokenized Asset Networks," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 7, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• David Garvin & Oleksiy Kondratyev & Alexander Lipton & Marco Paini, 2025, "Symmetric Encryption on a Quantum Computer," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 8, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Joshua Chung & Marcos Lopez de Prado & Horst D. Simon & Kesheng Wu, 2025, "Performance-Driven Dimensionality Reduction: A Data-Centric Approach to Feature Engineering in Machine Learning," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 9, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Torsten Hoefler & Alexandru Calotoiu & Anurag Dipankar & Thomas Schulthess & Xavier Lapillonne & Oliver Fuhrer, 2025, "Toward Specialized Supercomputers for Climate Sciences: Computational Requirements of the Icosahedral Nonhydrostatic Weather and Climate Model," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 10, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Ana Paula Peron & Emilio Porcu, 2025, "Dimension Walks on Generalized Spaces," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 11, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Francisco Herrera & Andres Herrera & Javier Del Ser & Enrique Herrera-Viedma & Marcos López de Prado, 2025, "Trustworthy Artificial Intelligence: Nature, Requirements, Regulation, and Emerging Discussions," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 12, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Kevin Webster & Nicholas Westray, 2025, "Getting More for Less: Better A/B Testing via Causal Regularization," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 13, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Alik Sokolov & Fabrizzio Sabelli & Behzad Azadie Faraz & Wuding Li & Luis Seco, 2025, "Toward Automating Causal Discovery in Financial Markets and Beyond," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 14, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Geiger, Felix & Kanelis, Dimitrios & Lieberknecht, Philipp & Sola, Diana, 2025, "Monetary-Intelligent Language Agent (MILA)," Technical Papers, Deutsche Bundesbank, number 01/2025.
• Janda, Karel & Rozsahegyi, Marketa & Quang Van Tran & Zhang, Binyi, 2025, "The Impact of Machine Learning Derived Green Bonds Sentiment on Performance of Green Bond Portfolio," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 335550.
• Ficura, Milan & Ibragimov, Rustam & Janda, Karel, 2025, "Artificial Intelligence–Based Forecasting of Oil Prices: Evidence from Neural Network Models," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 335571.
• Janda, Karel & Rozsahegyi, Marketa & Quang Van Tran & Zhang, Binyi, 2025, "Using Natural Language Processing to Identify Sentiment of Green Investors," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 335572.
• Capilla, Javier & Alcaráz, Alba & Valarezo, Angel & Garcia-Hiernaux, Alfredo & Pérez-Amaral, Teodosio, 2025, "Eco-RETINA: a green flexible algorithm for model building," 33rd European Regional ITS Conference, Edinburgh, 2025: Digital innovation and transformation in uncertain times, International Telecommunications Society (ITS), number 331255.
• Dahlke, Johannes & Schmidt, Sebastian & Lenz, David & Kinne, Jan & Dehghan, Robert & Abbasiharofteh, Milad & Schütz, Moritz & Kriesch, Lukas & Hottenrott, Hanna & Kanilmaz, Umut Nefta & Grashof, Nils , 2025, "The WebAI paradigm of innovation research: Extracting insight from organizational web data through AI," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 25-019.

2024

• Gharad Bryan & Dean Karlan & Adam Osman, 2024, "Big Loans to Small Businesses: Predicting Winners and Losers in an Entrepreneurial Lending Experiment," American Economic Review, American Economic Association, volume 114, issue 9, pages 2825-2860, September, DOI: 10.1257/aer.20220616.
• Anil Kumar & Che-Yuan Liang, 2024, "Labor Market Effects of Credit Constraints: Evidence from a Natural Experiment," American Economic Journal: Economic Policy, American Economic Association, volume 16, issue 3, pages 1-26, August, DOI: 10.1257/pol.20200683.
• Nikhil Agarwal & Ray Huang & Alex Moehring & Pranav Rajpurkar & Tobias Salz & Feiyang Yu, 2024, "Comparative Advantage of Humans versus AI in the Long Tail," AEA Papers and Proceedings, American Economic Association, volume 114, pages 618-622, May, DOI: 10.1257/pandp.20241071.
• Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2024, "The Unreasonable Effectiveness of Algorithms," AEA Papers and Proceedings, American Economic Association, volume 114, pages 623-627, May, DOI: 10.1257/pandp.20241072.
• Annie Liang & Jay Lu, 2024, "Algorithmic Fairness and Social Welfare," AEA Papers and Proceedings, American Economic Association, volume 114, pages 628-632, May, DOI: 10.1257/pandp.20241073.
• Victoria Angelova & Will Dobbie & Crystal S. Yang, 2024, "Algorithmic Recommendations When the Stakes Are High: Evidence from Judicial Elections," AEA Papers and Proceedings, American Economic Association, volume 114, pages 633-637, May, DOI: 10.1257/pandp.20241074.
• Abbate Nicolás Francisco & Gasparini Leonardo & Ronchetti Franco & Quiroga Facundo, 2024, "High-Resolution Income Estimates Using Satellite Imagery: A Deep Learning Approach applied in Buenos Aires," Asociación Argentina de Economía Política: Working Papers, Asociación Argentina de Economía Política, number 4701, Nov.
• Acedo Colli Luis Abel, 2024, "IED, capital humano y términos de intercambio: un enfoque de efectos de umbral," Asociación Argentina de Economía Política: Working Papers, Asociación Argentina de Economía Política, number 4702, Nov.
• Khulood Mohammed BaLashwar & Yuosuf Khalid Al-Hamar & Seyed-Ali Sadegh-Zadeh, 2024, "Optimizing Bank Stability Through MSME Loan Securitization: A Predictive and Prescriptive Analytics Approach," The African Finance Journal, Africagrowth Institute, volume 26, issue 2, pages 58-79.
• Hauke Licht & Ronja Sczepanski & Moritz Laurer & Ayjeren Bekmuratovna, 2024, "No More Cost in Translation: Validating Open-Source Machine Translation for Quantitative Text Analysis," ECONtribute Discussion Papers Series, University of Bonn and University of Cologne, Germany, number 276, Feb.
• Hauke Licht & Ronja Sczepanski, 2024, "Who are They Talking About? Detecting Mentions of Social Groups in Political Texts with Supervised Learning," ECONtribute Discussion Papers Series, University of Bonn and University of Cologne, Germany, number 277, Feb.
• Simon D Angus, 2024, "Tracking Policy-relevant Narratives of Democratic Resilience at Scale: from experts and machines, to AI & the transformer revolution," SoDa Laboratories Working Paper Series, Monash University, SoDa Laboratories, number 2024-07, Dec.
• Michal Mec & Mikulas Zeman & Klara Cermakova, 2024, "Stock market prediction using Generative Adversarial Network (GAN) – Study case Germany stock market," International Journal of Economic Sciences, European Research Center, volume 13, issue 2, pages 87-103, December.
• Anna A. Maigur, 2024, "Machine learning algorithms for predicting unemployment duration in Russia," Russian Journal of Economics, ARPHA Platform, volume 10, issue 4, pages 365-384, December, DOI: 10.32609/j.ruje.10.128611.
• Jeff Dominitz & Charles F. Manski, 2024, "Comprehensive OOS Evaluation of Predictive Algorithms with Statistical Decision Theory," Papers, arXiv.org, number 2403.11016, Mar, revised Apr 2025.
• Bartosz Bieganowski & Robert Slepaczuk, 2024, "Supervised Autoencoder MLP for Financial Time Series Forecasting," Papers, arXiv.org, number 2404.01866, Apr, revised Jun 2024.
• Arkadiusz Lipiecki & Bartosz Uniejewski & Rafa{l} Weron, 2024, "Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression," Papers, arXiv.org, number 2404.02270, Apr, revised Oct 2024.
• Annie Liang & Jay Lu, 2024, "Algorithmic Fairness and Social Welfare," Papers, arXiv.org, number 2404.04424, Apr.
• Adam Korniejczuk & Robert 'Slepaczuk, 2024, "Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market," Papers, arXiv.org, number 2406.10695, Jun.
• Zuzanna Kostecka & Robert 'Slepaczuk, 2024, "Improving Realized LGD Approximation: A Novel Framework with XGBoost for Handling Missing Cash-Flow Data," Papers, arXiv.org, number 2406.17308, Jun.
• Kamil Kashif & Robert 'Slepaczuk, 2024, "LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies," Papers, arXiv.org, number 2406.18206, Jun.
• Maciej Wysocki & Robert 'Slepaczuk, 2024, "Construction and Hedging of Equity Index Options Portfolios," Papers, arXiv.org, number 2407.13908, Jul.
• Melissa Dell, 2024, "Deep Learning for Economists," Papers, arXiv.org, number 2407.15339, Jul, revised Nov 2024.
• Natalia Roszyk & Robert 'Slepaczuk, 2024, "The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models," Papers, arXiv.org, number 2407.16780, Jul.
• Stefania Albanesi & Domonkos F. Vamossy, 2024, "Credit Scores: Performance and Equity," Papers, arXiv.org, number 2409.00296, Aug.
• Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2024, "Large Language Models: An Applied Econometric Framework," Papers, arXiv.org, number 2412.07031, Dec, revised Dec 2025.
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• Ajit Desai & Anneke Kosse & Jacob Sharples, 2024, "Finding a Needle in a Haystack: A Machine Learning Framework for Anomaly Detection in Payment Systems," Staff Working Papers, Bank of Canada, number 24-15, May, DOI: 10.34989/swp-2024-15.
• Pierre Beck & Pablo Garcia Sanchez & Alban Moura & Julien Pascal & Olivier Pierrard, 2024, "Deep learning solutions of DSGE models: A technical report," BCL working papers, Central Bank of Luxembourg, number 184, May.
• Piotr Gorzelańczyk, 2024, "Forecasting the number of road accidents caused by pedestrians in Poland using neural networks," Cognitive Sustainability, Cognitive Sustainability Ltd., volume 3, issue 4, pages 5-14, December, DOI: 10.55343/CogSust.102.
• Furkan TURKOGLU & Eda GOCECEK & Yavuz YUMRUKUZ, 2024, "Predictive Abilities of Machine Learning and Deep Learning Approaches for Exchange Rate Prediction," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, volume 18, issue 2, pages 186-210.
• Francesco Braggiotti & Nicola Chiarini & Giulio Dondi & Luciano Lavecchia & Valeria Lionetti & Juri Marcucci & Riccardo Russo, 2024, "Predicting buildings' EPC in Italy: a machine learning based-approach," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 850, Jun.
• José Manuel Carbó Martinez & Sergio Gorjón Rivas, 2024, "Determinants of the price of bitcoin: An analysis with machine learning and interpretability techniques," IFC Bulletins chapters, Bank for International Settlements, in: Bank for International Settlements, "Granular data: new horizons and challenges".
• Ajit Desai & Jacob Sharples & Anneke Kosse, 2024, "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," IFC Bulletins chapters, Bank for International Settlements, in: Bank for International Settlements, "Granular data: new horizons and challenges".
• Ajit Desai & Anneke Kosse & Jacob Sharples, 2024, "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," BIS Working Papers, Bank for International Settlements, number 1188, May.
• Linmei Shang & Jifeng Wang & David Schäfer & Thomas Heckelei & Juergen Gall & Franziska Appel & Hugo Storm, 2024, "Surrogate modelling of a detailed farm‐level model using deep learning," Journal of Agricultural Economics, Wiley Blackwell, volume 75, issue 1, pages 235-260, February, DOI: 10.1111/1477-9552.12543.
• SIDOROV Andrei, 2024, "The Impact Of Announcements On Cryptocurrency Prices," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, volume 76, issue 4, pages 69-94, December, DOI: 10.56043/reveco-2024-0035.
• Burda Martin & Schroeder Adrian K., 2024, "Recurrent Neural Network GO-GARCH Model for Portfolio Selection," Journal of Time Series Econometrics, De Gruyter, volume 16, issue 2, pages 67-81, DOI: 10.1515/jtse-2023-0012.
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• Francesco Audrino & Jessica Gentner & Simon Stalder, 2024, "Quantifying Uncertainty: A New Era of Measurement through Large Language Models," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 24-68, Aug.
• Pavel Vidal & Lya Paola Sierra-Suárez & Julieth Cerón, 2024, "Indicator for the Regional Labor Market Using Machine Learning Techniques: Application to Colombian Cities," Revista de Economía del Rosario, Universidad del Rosario, volume 27, issue 1, pages 1-31, DOI: 10.12804/revistas.urosario.edu.co/e.
• Carlos Giraldo & Iader Giraldo & Jose E. Gomez-Gonzalez & Jorge M. Uribe, 2024, "High Frequency Monitoring of Credit Creation: A New Tool for Central Banks in Emerging Market Economies," Documentos de trabajo, FLAR, number 21077, Mar.
• Peter B. Dixon & Maureen T. Rimmer & Florian Schiffmann, 2024, "Neural-Network approximation of reduced forms for CGE models explained by elementary examples," Centre of Policy Studies/IMPACT Centre Working Papers, Victoria University, Centre of Policy Studies/IMPACT Centre, number g-348, Nov.
• Peter Dixon & Michael Jerie & Dean Mustakinov & Maureen T. Rimmer & Nicholas Sheard & Florian Schiffmann & Glyn Wittwer, 2024, "Constructing a Destructive Events Tool using Small Rectangular Areas, Computable General Equilibrium Modelling and Neural Networks," Centre of Policy Studies/IMPACT Centre Working Papers, Victoria University, Centre of Policy Studies/IMPACT Centre, number g-349, Dec.
• Ashwin, Julian & Beaudry, Paul & Ellison, Martin, 2024, "Neural Network Learning for Nonlinear Economies," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 19295, Jul.
• Hauzenberger , Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2024, "Bayesian Neural Networks for Macroeconomic Analysis," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 19381, Aug.
• Julen Iglesias Tejedor, 2024, "Creación de una cartera de inversión que venza la inflación atendiendo a criterios ESG gestionada mediante machine learning," Revista de Economía y Finanzas (REyF), Asociación Cuadernos de Economía, volume 2, issue 5, pages 79-100, Mayo.
• Iskren Tairov & Nadezhda Stefanova & Aleksandrina Aleksandrova, 2024, "Artificial Intelligence Application in Human Resources Management," Business Management, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, issue 3 Year 20, pages 72-88.
• Christian Manuel Moreno Rocha & Melidey Díaz Ospino & Israel Blanco Ramos & Andres Medina Guzman, 2024, "Enhancing Sustainable Mobility: Multi-Criteria Analysis for Electric Vehicle Integration and Policy Implementation," International Journal of Energy Economics and Policy, Econjournals, volume 14, issue 1, pages 205-218, January.
• Wellcome Peujio Jiotsop-Foze & Adrián Hernández-del-Valle & Francisco Venegas-Martínez, 2024, "Electrical Load Forecasting to Plan the Increase in Renewable Energy Sources and Electricity Demand: a CNN-QR-RTCF and Deep Learning Approach," International Journal of Energy Economics and Policy, Econjournals, volume 14, issue 4, pages 186-194, July.
• Christian Manuel Moreno Rocha & Daina Arenas Buelvas & Sandra De la Hoz Escorcia, 2024, "Evaluation and Ranking of Energy Alternatives for Implementation in Different Geographic Scenarios using Decision Methods: Case Study of Colombia," International Journal of Energy Economics and Policy, Econjournals, volume 14, issue 5, pages 191-202, September.
• Wellcome Peujio Jiotsop-Foze & Adrián Hernández-del-Valle & Francisco Venegas-Martínez, 2024, "Transforming Mexico’s Electric Load Infrastructure: A Quantile Transformer Network Deep Learning Approach, 2019-2020," International Journal of Energy Economics and Policy, Econjournals, volume 14, issue 5, pages 527-533, September.
• Nguyen, Jeremy K., 2024, "Human bias in AI models? Anchoring effects and mitigation strategies in large language models," Journal of Behavioral and Experimental Finance, Elsevier, volume 43, issue C, DOI: 10.1016/j.jbef.2024.100971.
• Pascal, Julien, 2024, "Artificial neural networks to solve dynamic programming problems: A bias-corrected Monte Carlo operator," Journal of Economic Dynamics and Control, Elsevier, volume 162, issue C, DOI: 10.1016/j.jedc.2024.104853.
• Wang, Jia & Wang, Xinyi & Wang, Xu, 2024, "International oil shocks and the volatility forecasting of Chinese stock market based on machine learning combination models," The North American Journal of Economics and Finance, Elsevier, volume 70, issue C, DOI: 10.1016/j.najef.2023.102065.
• Yang, Qu & Yu, Yuanyuan & Dai, Dongsheng & He, Qian & Lin, Yu, 2024, "Can hybrid model improve the forecasting performance of stock price index amid COVID-19? Contextual evidence from the MEEMD-LSTM-MLP approach," The North American Journal of Economics and Finance, Elsevier, volume 74, issue C, DOI: 10.1016/j.najef.2024.102252.
• Zhang, Wenyu, 2024, "Dynamic monitoring of financial security risks: A novel China financial risk index and an early warning system," Economics Letters, Elsevier, volume 234, issue C, DOI: 10.1016/j.econlet.2023.111445.
• Baumgärtner, Martin & Zahner, Johannes, 2024, "Talking fragmentation away – Decoding the ’whatever it takes’ effect," Economics Letters, Elsevier, volume 234, issue C, DOI: 10.1016/j.econlet.2023.111484.
• Abdou, Hussein A. & Elamer, Ahmed A. & Abedin, Mohammad Zoynul & Ibrahim, Bassam A., 2024, "The impact of oil and global markets on Saudi stock market predictability: A machine learning approach," Energy Economics, Elsevier, volume 132, issue C, DOI: 10.1016/j.eneco.2024.107416.
• Zadeh, Omid Razavi & Romagnoli, Silvia, 2024, "Financing sustainable energy transition with algorithmic energy tokens," Energy Economics, Elsevier, volume 132, issue C, DOI: 10.1016/j.eneco.2024.107420.
• Haas, Christian & Budin, Constantin & d’Arcy, Anne, 2024, "How to select oil price prediction models — The effect of statistical and financial performance metrics and sentiment scores," Energy Economics, Elsevier, volume 133, issue C, DOI: 10.1016/j.eneco.2024.107466.
• Tan, Jinghua & Li, Zhixi & Zhang, Chuanhui & Shi, Long & Jiang, Yuansheng, 2024, "A multiscale time-series decomposition learning for crude oil price forecasting," Energy Economics, Elsevier, volume 136, issue C, DOI: 10.1016/j.eneco.2024.107733.
• Deng, Sinan & Inekwe, John & Smirnov, Vladimir & Wait, Andrew & Wang, Chao, 2024, "Seasonality in deep learning forecasts of electricity imbalance prices," Energy Economics, Elsevier, volume 137, issue C, DOI: 10.1016/j.eneco.2024.107770.
• Ouyang, Zisheng & Lu, Min & Ouyang, Zhongzhe & Zhou, Xuewei & Wang, Ren, 2024, "A novel integrated method for improving the forecasting accuracy of crude oil: ESMD-CFastICA-BiLSTM-Attention," Energy Economics, Elsevier, volume 138, issue C, DOI: 10.1016/j.eneco.2024.107851.
• Lipiecki, Arkadiusz & Uniejewski, Bartosz & Weron, Rafał, 2024, "Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107934.
• Yang, Kun & Sun, Yuying & Hong, Yongmiao & Wang, Shouyang, 2024, "Forecasting interval carbon price through a multi-scale interval-valued decomposition ensemble approach," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107952.
• Wang, Yuejing & Ye, Wuyi & Jiang, Ying & Liu, Xiaoquan, 2024, "Volatility prediction for the energy sector with economic determinants: Evidence from a hybrid model," International Review of Financial Analysis, Elsevier, volume 92, issue C, DOI: 10.1016/j.irfa.2024.103094.
• Qiu, Zhiguo & Lazar, Emese & Nakata, Keiichi, 2024, "VaR and ES forecasting via recurrent neural network-based stateful models," International Review of Financial Analysis, Elsevier, volume 92, issue C, DOI: 10.1016/j.irfa.2024.103102.
• Wang, Li & Huang, Yiting & Hong, Zhiwu, 2024, "Digitalization as a double-edged sword: A deep learning analysis of risk management in Chinese banks," International Review of Financial Analysis, Elsevier, volume 94, issue C, DOI: 10.1016/j.irfa.2024.103249.
• Heger, Julia & Min, Aleksey & Zagst, Rudi, 2024, "Analyzing credit spread changes using explainable artificial intelligence," International Review of Financial Analysis, Elsevier, volume 94, issue C, DOI: 10.1016/j.irfa.2024.103315.
• Peng, Yaohao & de Moraes Souza, João Gabriel, 2024, "Chaos, overfitting and equilibrium: To what extent can machine learning beat the financial market?," International Review of Financial Analysis, Elsevier, volume 95, issue PB, DOI: 10.1016/j.irfa.2024.103474.
• Liang, Qing & Li, Zhaohua, 2024, "Litigation risk and the cost of debt financing in M&As," International Review of Financial Analysis, Elsevier, volume 96, issue PA, DOI: 10.1016/j.irfa.2024.103586.
• Oehler, Andreas & Horn, Matthias, 2024, "Does ChatGPT provide better advice than robo-advisors?," Finance Research Letters, Elsevier, volume 60, issue C, DOI: 10.1016/j.frl.2023.104898.
• Baruník, Jozef & Hanus, Luboš, 2024, "Fan charts in era of big data and learning," Finance Research Letters, Elsevier, volume 61, issue C, DOI: 10.1016/j.frl.2024.105003.
• Beckmann, Lars & Hark, Paul F., 2024, "ChatGPT and the banking business: Insights from the US stock market on potential implications for banks," Finance Research Letters, Elsevier, volume 63, issue C, DOI: 10.1016/j.frl.2024.105237.
• Hwang, Yoontae & Park, Junpyo & Kim, Jang Ho & Lee, Yongjae & Fabozzi, Frank J., 2024, "Heterogeneous trading behaviors of individual investors: A deep clustering approach," Finance Research Letters, Elsevier, volume 65, issue C, DOI: 10.1016/j.frl.2024.105481.
• Göncü, Ahmet & Kuzubaş, Tolga U. & Saltoğlu, Burak, 2024, "Predicting oil prices: A comparative analysis of machine learning and image recognition algorithms for trend prediction," Finance Research Letters, Elsevier, volume 67, issue PB, DOI: 10.1016/j.frl.2024.105874.
• Jiang, Yifu & Olmo, Jose & Atwi, Majed, 2024, "Deep reinforcement learning for portfolio selection," Global Finance Journal, Elsevier, volume 62, issue C, DOI: 10.1016/j.gfj.2024.101016.
• Corsaro, Stefania & Marino, Zelda & Scognamiglio, Salvatore, 2024, "Quantile mortality modelling of multiple populations via neural networks," Insurance: Mathematics and Economics, Elsevier, volume 116, issue C, pages 114-133, DOI: 10.1016/j.insmatheco.2024.02.007.
• Nguyen, Hang & Sherris, Michael & Villegas, Andrés M. & Ziveyi, Jonathan, 2024, "Scenario selection with LASSO regression for the valuation of variable annuity portfolios," Insurance: Mathematics and Economics, Elsevier, volume 116, issue C, pages 27-43, DOI: 10.1016/j.insmatheco.2024.01.006.
• Ballester, Laura & González-Urteaga, Ana & Shen, Long, 2024, "Green bond issuance and credit risk: International evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 94, issue C, DOI: 10.1016/j.intfin.2024.102013.
• Huang, Zih-Chun & Sangiorgi, Ivan & Urquhart, Andrew, 2024, "Forecasting Bitcoin volatility using machine learning techniques," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 97, issue C, DOI: 10.1016/j.intfin.2024.102064.
• Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024, "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, volume 40, issue 4, pages 1521-1538, DOI: 10.1016/j.ijforecast.2024.01.001.
• Kurz, Konstantin & Bock, Carolin & Hanschur, Leonard, 2024, "Flip the tweet – the two-sided coin of entrepreneurial empathy and its ambiguous influence on new product development," Journal of Business Venturing, Elsevier, volume 39, issue 2, DOI: 10.1016/j.jbusvent.2023.106378.
• Comola, Margherita & Inguaggiato, Carla & Mendola, Mariapia, 2024, "Social networks and economic transformation: Evidence from a resettled village in Brazil," Journal of Economic Behavior & Organization, Elsevier, volume 221, issue C, pages 17-34, DOI: 10.1016/j.jebo.2024.03.001.
• Moreno-Pérez, Carlos & Minozzo, Marco, 2024, "‘Making text talk’: The minutes of the Central Bank of Brazil and the real economy," Journal of International Money and Finance, Elsevier, volume 147, issue C, DOI: 10.1016/j.jimonfin.2024.103133.
• Christensen, Peter & Francisco, Paul & Myers, Erica & Shao, Hansen & Souza, Mateus, 2024, "Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting," Journal of Public Economics, Elsevier, volume 234, issue C, DOI: 10.1016/j.jpubeco.2024.105098.
• Giraldo, Carlos & Giraldo, Iader & Gomez-Gonzalez, Jose E. & Uribe, Jorge M., 2024, "High frequency monitoring of credit creation: A new tool for central banks in emerging market economies," The Quarterly Review of Economics and Finance, Elsevier, volume 97, issue C, DOI: 10.1016/j.qref.2024.101893.
• Mati, Sagiru & Baita, Abubakar Jamilu & Ismael, Goran Yousif & Abdullahi, Salisu Garba & Samour, Ahmed & Ozsahin, Dilber Uzun, 2024, "Enhancing CO2 emissions prediction in Africa: A novel approach integrating enviroeconomic factors and nature-inspired neural network in the presence of unit root," Renewable Energy, Elsevier, volume 237, issue PA, DOI: 10.1016/j.renene.2024.121561.
• Carbó, José Manuel & Gorjón, Sergio, 2024, "Determinants of the price of bitcoin: An analysis with machine learning and interpretability techniques," International Review of Economics & Finance, Elsevier, volume 92, issue C, pages 123-140, DOI: 10.1016/j.iref.2024.01.070.
• de França Carvalho, João Vinícius & Guimarães, Acássio Silva, 2024, "Systemic risk assessment using complex networks approach: Evidence from the Brazilian (re)insurance market," Research in International Business and Finance, Elsevier, volume 67, issue PA, DOI: 10.1016/j.ribaf.2023.102065.
• Ghosh, Indranil & Jana, Rabin K., 2024, "Clean energy stock price forecasting and response to macroeconomic variables: A novel framework using Facebook's Prophet, NeuralProphet and explainable AI," Technological Forecasting and Social Change, Elsevier, volume 200, issue C, DOI: 10.1016/j.techfore.2023.123148.
• Saâdaoui, Foued & Rabbouch, Hana, 2024, "Financial forecasting improvement with LSTM-ARFIMA hybrid models and non-Gaussian distributions," Technological Forecasting and Social Change, Elsevier, volume 206, issue C, DOI: 10.1016/j.techfore.2024.123539.
• Manuel Hidalgo Pérez, 2024, "Percepción y disrupción: Impacto laboral de la Inteligencia Artificial Generativa en Euskadi," EKONOMIAZ. Revista vasca de Economía, Gobierno Vasco / Eusko Jaurlaritza / Basque Government, volume 105, issue 01, pages 140-171.
• Stiven Agusta & Fuad Rakhman & Jogiyanto Hartono Mustakini & Singgih Wijayana, 2024, "Enhancing the accuracy of stock return movement prediction in Indonesia through recent fundamental value incorporation in multilayer perceptron," Asian Journal of Accounting Research, Emerald Group Publishing Limited, volume 9, issue 4, pages 358-377, July, DOI: 10.1108/AJAR-01-2024-0006.
• Luan Thanh Le & Trang Xuan-Thi-Thu, 2024, "Discovering supply chain operation towards sustainability using machine learning and DES techniques: a case study in Vietnam seafood," Maritime Business Review, Emerald Group Publishing Limited, volume 9, issue 3, pages 243-262, July, DOI: 10.1108/MABR-10-2023-0074.
• Leonardo E. Torre & Eva E. González & Luis R. Casillas & Jorge A. Alvarado, 2024, "à ndices de sentimiento regionales y su asociación con indicadores oportunos de actividad económica en México, 2016-2021/Regional sentiment indexes and their association with timely indicators of e," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, volume 39, issue 2, pages 349–419-3.
• Daria Wotzka & Pawel Fracz & Jolanta Staszewska & Joachim Foltys & Malgorzata Smolarek & Krzysztof Orzechowski, 2024, "The Application of Selected Supervised Machine Learning Methods in the Classification of Family Businesses in the Context of Cluster Formation," European Research Studies Journal, European Research Studies Journal, volume 0, issue 4, pages 248-272.
• Sebastian Zupok, 2024, "Contemporary Tools for Creating Customer Value," European Research Studies Journal, European Research Studies Journal, volume 0, issue 4, pages 545-559.
• Jacek Rudewicz, 2024, "Theoretical Insights into Artificial Intelligence Applications in Human Geography and Spatial Management (GeoAI)," European Research Studies Journal, European Research Studies Journal, volume 0, issue 4, pages 973-984.
• Pawel Rymarczyk & Tomasz Smutek & Daria Stefanczak & Wiktor Cwynar & Sebastian Zupok, 2024, "Self-learning Recommendation System Using Reinforcement Learning," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 137-149.
• Mariusz Mazurek & Konrad Gauda & Sylwia Skrzypek-Ahmed & Artem Artiukov & Piotr Stalinski, 2024, "Collection Life Cycle Analysis: Enhancing Market Strategies through Advanced Data Analytics," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 14-24.
• Bartosz Przysucha & Pawel Kaleta & Artur Dmowski & Jacek Piwkowski & Piotr Czarnecki & Tomasz Cieplak, 2024, "Product Knowledge Graphs - Creating a Knowledge System for Customer Support," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 150-159.
• Tomasz Smutek & Marcin Marczuk & Michal Jarmul & Ewelina Jurczak & Damian Pliszczuk, 2024, "Chatbot to Support the Customer Service Process," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 160-168.
• Michal Maj & Artur Dmowski & Ryszard Nowak & Tadeusz Medzelowski & Tomasz Cieplak, 2024, "Enhancing Customer Service in Shopping Malls with an Advanced Chatbot-Integrated Concierge Device," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 169-178.
• Pawel Olszewski & Leszek Gil & Natalia Rak & Tomasz Wolowiec & Michal Jasienski, 2024, "Construction of Regression Models Predicting Lead Times and Classification Models," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 179-189.
• Michal Maj & Jacek Korzeniak & Jozef Stoklosa & Pawel Barwiak & Bartlomiej Bartnik & Lukasz Maciura, 2024, "A Real-Time Autonomous Machine Learning System for Face Recognition Using Pre-Trained Convolutional Neural Networks," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 3-13.
• Krzysztof Krol & Michal Oleszek & Grzegorz Bartnik & Olena Ivashko & Marek Rutkowski & Adam Hernas, 2024, "Developing an IoT and Machine Learning-Based Monitoring System for Discrete Production Processes," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 38-48.
• Mariusz Mazurek & Magdalena Halas & Jan Sikora & Anna Wisniewska - Vistula & Diana Wroblewska & Sebastian Zupok, 2024, "Analysis of Consumer Behavior Using an Intelligent Multi-Source System," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 49-58.
• Tomasz Smutek & Jan Sikora & Sylwester Bogacki & Marek Rutkowski & Dariusz Wozniak, 2024, "Use of Autoencoder and One-Hot Encoding for Customer Segmentation," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 72-82.
• Tomasz Smutek & Marcin Kowalski & Olena Ivashko & Robert Chmura & Justyna Sokolowska-Wozniak, 2024, "A Graph-Based Recommendation System Leveraging Cosine Similarity for Enhanced Marketing Decisions," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 83-93.
• Bartosz Przysucha & Magdalena Halas & Cezary Figura & Natalia Rak & Pawel Barwiak & Adam Hernas, 2024, "Exploring and Analyzing YouTube Communities through Data Mining and Knowledge Graphs," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 94-102.
• Tomasz Strak, 2024, "Generative AI as Tax Attorneys: Exploring Legal Understanding Through Experiments," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special B, pages 1057-1094.
• Damian Pliszczuk & Michal Maj & Patryk Marek & Weronika Wilczewska & Tomasz Cieplak & Tomasz Rymarczyk, 2024, "The Effect of the Context Length of Large Language Models on the Quality of Extracted Information in Polish in the Process of Project Management," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special B, pages 414-425.
• Michal Maj & Damian Pliszczuk & Patryk Marek & Weronika Wilczewska & Bartosz Przysucha & Tomasz Rymarczyk, 2024, "Optimizing Customer Support Using Text2SQL to Query Natural Language Databases," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special B, pages 426-438.
• Mikhail Vyacheslavovich Shatunov & Natalia Anatolyevna Navrotskaya, 2024, "Spatial Organization of Global Value Chains in the Optical and Electronic Equipment Industry," Spatial Economics=Prostranstvennaya Ekonomika, Economic Research Institute, Far Eastern Branch, Russian Academy of Sciences (Khabarovsk, Russia), issue 4, pages 108-134, DOI: https://dx.doi.org/10.14530/se.2024.
• Karel Janda & Mathieu Petit, 2024, "Analyzing Decision-Making in Deep-Q Reinforcement Learning for Trading: A Case Study on Tesla Company and its Supply Chain," Working Papers IES, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, number 2024/40, Nov, revised Nov 2024.