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

• Tu DQ Le & Son H Tran & Thanh Ngo & Hung D Bui, 2026, "Forecasting Vietnam Inflation Using Machine Learning Approaches: A Comprehensive Analysis," Advances in Decision Sciences, Asia University, Taiwan, volume 30, issue 1, pages 136-185.
• Syed Imran Abbas Qazmi & Midhun Chakkaravarthy & Syed Hassan Raza & Farrah Aslam & Shahbaz Aslam & Moneeba Iftikhar, 2026, "Review Helpfulness to Support Business: Identifying Fake Reviews from User-Generated Content Using Random Forest," Advances in Decision Sciences, Asia University, Taiwan, volume 30, issue 2, pages 114-156, June.
• Pascaline Dupas & Amy Handlan & Alicia Sasser Modestino & Muriel Niederle & Mateo Seré & Haoyu Sheng & Justin Wolfers & Seminar Dynamics Collective, 2026, "Gender Differences in Economics Seminars," American Economic Review, American Economic Association, volume 116, issue 2, pages 749-789, February, DOI: 10.1257/aer.20241718.
• Gabriel Ehrlich & John Haltiwanger & Ron Jarmin & David Johnson & Ed Olivares & Luke Pardue & Matthew D. Shapiro & Laura Yi Zhao, 2026, "Quality Adjustment at Scale: Hedonic versus Exact Demand-Based Price Indices," American Economic Review, American Economic Association, volume 116, issue 6, pages 1955-1995, June, DOI: 10.1257/aer.20230766.
• Daniel Björkegren & Joshua E. Blumenstock & Samsun Knight, 2026, "Manipulation-Robust Prediction," American Economic Review, American Economic Association, volume 116, issue 9, pages 3263-3293, September, DOI: 10.1257/aer.20241087.
• Jenny C. Aker & Jennifer Burney & Alison Campion & B. Kelsey Jack & Chuan Liao, 2026, "How Many (Half) Moons? Measuring Technology Adoption from the Sky and on the Ground," AEA Papers and Proceedings, American Economic Association, volume 116, pages 173-177, May, DOI: 10.1257/pandp.20261091.
• Emily Aiken & Joshua E. Blumenstock & Sveta Milusheva & M. Merritt Smith, 2026, "Predicting Well-Being with Mobile Phone Data: Evidence from Four Countries," AEA Papers and Proceedings, American Economic Association, volume 116, pages 178-183, May, DOI: 10.1257/pandp.20261092.
• Kristina McElheran & Mu-Jeung Yang & Zachary Kroff & Erik Brynjolfsson, 2026, "The Adoption of Industrial AI in America," AEA Papers and Proceedings, American Economic Association, volume 116, pages 20-25, May, DOI: 10.1257/pandp.20261033.
• Martin Neil Baily & David M. Byrne & Aidan T. Kane & Paul E. Soto, 2026, "Generative AI: What Type of Technology?," AEA Papers and Proceedings, American Economic Association, volume 116, pages 26-30, May, DOI: 10.1257/pandp.20261034.
• Francesco Filippucci & Peter Gal & Matthias Schief, 2026, "Aggregate Productivity Gains from Artificial Intelligence: A Sectoral Perspective," AEA Papers and Proceedings, American Economic Association, volume 116, pages 31-35, May, DOI: 10.1257/pandp.20261035.
• Filippo Bontadini & Carol Corrado & Jonathan Haskel & Cecilia Jona-Lasinio, 2026, "AI as an Innovation in the Method of Innovation: Implications for Productivity Growth," AEA Papers and Proceedings, American Economic Association, volume 116, pages 36-40, May, DOI: 10.1257/pandp.20261036.
• Laura J. Ahlstrom & Carlos J. Asarta & Cynthia Harter, 2026, "The Use of Artificial Intelligence in Undergraduate Economics Courses," AEA Papers and Proceedings, American Economic Association, volume 116, pages 686-691, May, DOI: 10.1257/pandp.20261057.
• Matthew Gordon & Eliana Stone & Megan Ayers & Luke Sanford, 2026, "Debiasing Estimates of Global Forest Cover Loss," AEA Papers and Proceedings, American Economic Association, volume 116, pages 81-86, May, DOI: 10.1257/pandp.20261018.
• Haya Alsharif & Ashesh Rambachan & Rahul Singh & Davide Viviano, 2026, "Causal Inference with Satellite Imagery: A Comparison of Methods for Forest Conservation Data," AEA Papers and Proceedings, American Economic Association, volume 116, pages 87-91, May, DOI: 10.1257/pandp.20261019.
• Timothy Christensen & Stephen Hansen, 2026, "Performing Valid Inference with AI/ML-Generated Covariates: A Guide for Empirical Practice," AEA Papers and Proceedings, American Economic Association, volume 116, pages 92-97, May, DOI: 10.1257/pandp.20261020.
• Dan M. Kluger & Kerri Lu & Tijana Zrnic & Sherrie Wang & Stephen Bates, 2026, "A Preview of the Predict-Then-Debias Bootstrap," AEA Papers and Proceedings, American Economic Association, volume 116, pages 98-102, May, DOI: 10.1257/pandp.20261021.
• Kevin A. Bryan, 2026, "The Economic Impacts of Artificial Intelligence: A Multidisciplinary, Multi-book Review," Journal of Economic Literature, American Economic Association, volume 64, issue 1, pages 281-300, March, DOI: 10.1257/jel.20251799.
• Robert Novy-Marx & Mihail Velikov, 2026, "Artificial Intelligence–Powered (Finance) Scholarship," Journal of Economic Literature, American Economic Association, volume 64, issue 1, pages 5-37, March, DOI: 10.1257/jel.20251821.
• Jesús Fernández- Villaverde, 2026, "Deep Learning for Solving Economic Models," Journal of Economic Literature, American Economic Association, volume 64, issue 3, pages 829-875, September, DOI: 10.1257/jel.20261794.
• Mert Demirer & Andrey Fradkin & Nadav Tadelis, 2026, "The Emerging Market for Intelligence: How Firms Buy and Sell AI," Journal of Economic Perspectives, American Economic Association, volume 40, issue 3, pages 23-46, Summer, DOI: 10.1257/jep.20261506.
• Charles I. Jones, 2026, "AI and Our Economic Future," Journal of Economic Perspectives, American Economic Association, volume 40, issue 3, pages 3-22, Summer, DOI: 10.1257/jep.20261505.
• Allon-Pineda, Joan Christine S., 2026, "Inflation Unpacked: Breaking Down the Key Components Using a Neural Phillips Curve," Asian Journal of Applied Economics, Kasetsart University, Center for Applied Economics Research, volume 33, issue 2, July, DOI: 10.22004/ag.econ.406292.
• Katarzyna Chec & Bartosz Uniejewski & Rafal Weron, 2026, "From biased point forecasts of electricity demand to accurate predictive distributions: Using LASSO and GAMLSS," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/26/01.
• Hidayet Beyhan & Erhan Ergin & Binali Selman Eren, 2026, "Dynamic Portfolio Optimization with Deep Reinforcement Learning: Evidence from Borsa Istanbul," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 11, issue 1, pages 106-119, DOI: 10.30784/epfad.1811319.
• Ansgar Hudde & Shannon Taflinger, 2026, "A Golden Era for Open-Ended Questions? Using LLMs for Text Classification Tasks," ECONtribute Discussion Papers Series, University of Bonn and University of Cologne, Germany, number 416, Jun.
• Burke, Matt & Mohaddes, Kamiar & Raissi, Mehdi, 2026, "The Adaptation Imperative: Climate Change and Sovereign Credit Risk," INET Oxford Working Papers, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, number 2026-03, Feb.
• Adil SLAMI-AMINE & Abdessamad DINE & Boujemaa ACHCHAB, 2026, "Typologie et taxonomie des modèles prédictifs monolithiques et hybrides d'intelligence artificielle et d'apprentissage automatique en finance : une architecture générationnelle intégrée," International Journal of Accounting, Finance, Auditing, Management and Economics, Faculté des Sciences Juridiques, Économiques et Sociales, Université Ibn Tofaïl, volume 7, issue 6, pages 543-570.
• Fatima Zahra EL ALAOUI ISMAILI, 2026, "Intelligence artificielle et neurosciences au service de la décision économique et managériale : Revue systématique des approches EEG/fNIRS fondées sur l’apprentissage automatique," International Journal of Accounting, Finance, Auditing, Management and Economics, Faculté des Sciences Juridiques, Économiques et Sociales, Université Ibn Tofaïl, volume 7, issue 6, pages 61-76.
• Adil SLAMI-AMINE & Abdessamad DINE & Boujemaa ACHCHAB, 2026, "Une typologie comparative des générations de modèles d'apprentissage automatique monolithiques et hybrides pour la prédiction financière : benchmark théorique face aux modèles économétriques traditionnels," International Journal of Accounting, Finance, Auditing, Management and Economics, Faculté des Sciences Juridiques, Économiques et Sociales, Université Ibn Tofaïl, volume 7, issue 7, pages 174-200.
• Oussama ANFELOUSS & Mourad REHIOUI, 2026, "Contribution d`un contrôle de gestion intelligent dans l`optimisation des coûts économiques dans une chaîne logistique de transport : revue de littérature," International Journal of Accounting, Finance, Auditing, Management and Economics, Faculté des Sciences Juridiques, Économiques et Sociales, Université Ibn Tofaïl, volume 7, issue 8, pages 438-454.
• Joshua S. Gans, 2026, "Optimal Use of Preferences in Artificial Intelligence Algorithms," Papers, arXiv.org, number 2601.18732, Jan.
• Easton Huch & Michael Keane, 2026, "Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks," Papers, arXiv.org, number 2603.24705, Mar, revised Sep 2026.
• Torben S. D. Johansen & Julius Koschnick & Christian Vedel, 2026, "How to deal with machine learning bias in economic history," Papers, arXiv.org, number 2606.28063, Jun.
• Guanglan Zhou & Ziyi Wu, 2026, "A resilient model for trade volume forecasting under economic uncertainty: Addressing challenges in the global supply chain," E&M Economics and Management, Technical University of Liberec, Faculty of Economics, volume 29, issue 1, pages 207-224, March, DOI: 10.15240/tul/001/2026-1-013.
• Shahryar Ghorbani & Figen Yildirim & Ali Altug Bicer & Reza Rostamzadeh & Jonas Saparauskas, 2026, "Forecasting major currency exchange rates using long short-term memory networks: Evidence from multi-currency time series analysis," E&M Economics and Management, Technical University of Liberec, Faculty of Economics, volume 29, issue 2, pages 220-239, July, DOI: 10.15240/tul/001/2026-2-014.
• Luis Perez-Pulido & Angel Mena-Nieto & Jose Carlos Vides, 2026, "Application of self-organising maps to analysed bike-sharing usage in large cities: A case study of Seville," E&M Economics and Management, Technical University of Liberec, Faculty of Economics, volume 29, issue 3, pages 80-101, August, DOI: 10.15240/tul/001/2026-3-005.
• BBVA Research, 2026, "Global | Geopolitics, geoeconomics and sovereign risk: different shocks,different channels," Working Papers, BBVA Bank, Economic Research Department, number 26/04, Mar.
• James Chapman & Ajit Desai & Maryam Haghighi & James (Jim) C. MacGee, 2026, "Integrating Non-traditional Data and AI into Central Banking: A Canadian Perspective," Staff Analytical Papers, Bank of Canada, number 2026-17, May, DOI: 10.34989/sap-2026-17.
• Marlon Salazar & Andrés Salazar-Mejía & Jorge Daniel Guevara-Acevedo & Juan David Duitama-Correa, 2026, "Neural Network Equilibrium Real Exchange Rate," Borradores de Economia, Banco de la Republica de Colombia, number 1361, Aug.
• Batuhan Koyuncu & Byeungchun Kwon & Marco Jacopo Lombardi & Fernando Perez-Cruz & Hyun Song Shin, 2026, "BISTRO: a general purpose oracle for macroeconomic time series," BIS Quarterly Review, Bank for International Settlements, March.
• Koji Takahashi & Joon Suk Park, 2026, "Generative AI for surveys on payment apps: AI views on privacy and technology," BIS Working Papers, Bank for International Settlements, number 1333, Mar.
• Batuhan Koyuncu & Byeungchun Kwon & Marco Jacopo Lombardi & Fernando Perez-Cruz & Hyun Song Shin, 2026, "Introducing BISTRO: a foundational model for unconditional and conditional forecasting of macroeconomic time series," BIS Working Papers, Bank for International Settlements, number 1337, Mar.
• Nguyễn Quốc Hùng & Lê Thành Trung & Nguyễn Thị Xuân Đào & Nguyễn Quang Trường, 2026, "Hệ thống dự báo khách hàng rời bỏ dịch vụ ngân hàng trên nền tảng học máy," Tạp chí Khoa học Đại học Mở Thành phố Hồ Chí Minh - Kinh tế và Quản trị kinh doanh, Ho Chi Minh City Open University Journal of Science, Ho Chi Minh City Open University, volume 21, issue 1, pages 73-89, DOI: 10.46223/HCMCOUJS.econ.vi.21.1.3474.
• Valeria Zvereva & Anna Krupkina & Andrey Andreev & Oleg Semiturkin & Maria Kudaeva, 2026, "Identifying Turning Points in Bank of Russia Business Activity Indicators Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, volume 85, issue 2, pages 3-36, June.
• Vegard H. Larsen & Leif Anders Thorsrud, 2026, "Using Transformers and Reinforcement Learning as Narrative Filters in Macroeconomics," Working Papers, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School, number No 02/2026, Feb.
• Nikoleta Anesti & Edward Hill & Andreas Joseph, 2026, "Inflation attitudes of large language models," Bank of England Staff Working Paper series, Bank of England, number 1190, Jun.
• Benjamin Born & Nora Lamersdorf & Jana-Lynn Schuster & Sascha Steffen, 2026, "From Tweets to Transactions: High-Frequency Inflation Expectations, Consumption, and Stock Returns," CRC TR 224 Discussion Paper Series, University of Bonn and University of Mannheim, Germany, number crctr224_2025_724, Jan.
• Burke, M. & Mohaddes, K & Raissi, M., 2026, "The Adaptation Imperative: Climate Change and Sovereign Credit Risk," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2608, Feb.
• Grzeskiewicz, M., 2026, "Neural Demand Estimation with Habit Formation and Rationality Constraints," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2613, Mar.
• Greenhill, Simon & Walker, Brant J & Shapiro, Joseph S, 2026, "Deep Learning Projects Jurisdiction of New and Proposed Clean Water Act Regulation," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series, Department of Agricultural & Resource Economics, UC Berkeley, number qt6tx6m2fn, Feb.
• Vegard H. Larsen & Leif Anders Thorsrud, 2026, "Using Transformers and Reinforcement Learning as Narrative Filters in Macroeconomics," CESifo Working Paper Series, CESifo, number 12454.
• Bryan T. Kelly & Boris Kuznetsov & Semyon Malamud & Teng Andrea Xu & Yuan Zhang, 2026, "Large and Deep Factor Models," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 26-20, Feb.
• María Alejandra Saavedra Velásquez, 2026, "Reading Between the Lines: Gendered Narratives in Mexican Textbooks and Adult Labor Market Outcomes," Documentos CEDE, Universidad de los Andes, Facultad de Economía, CEDE, number 2026-42, Jul.
• Carlos Palomino Selem & Ruth Milagros Delgado Yana, 2026, "Comparative analysis between traditional momentum and machine learning (random forest): evidence from the S&P 500 (2000-2024)," Revista Tendencias, Universidad de Narino, volume 27, issue 02, pages 32-61, July, DOI: 10.22267/rtend.26272.296.
• Alam, M. Jahangir & Boyle, Shane & Li, Huiyu & Sekhposyan, Tatevik, 2026, "ChatMacro: Evaluating Inflation Forecasts of Generative AI," CEPR Discussion Papers, Centre for Economic Policy Research, number 21057, Jan.
• Gröger, André & Mueller, Hannes, 2026, "War Destruction, Commercial Relocation and Urban Recovery," CEPR Discussion Papers, Centre for Economic Policy Research, number 21059, Jan.
• Kaniel, Ron & Pelger, Markus & Van Nieuwerburgh, Stijn & Zhou, Luofeng, 2026, "Detecting Skilled Bond Fund Managers," CEPR Discussion Papers, Centre for Economic Policy Research, number 21696, Jul.
• Susan Athey & Lisa K. Simon & Oskar Nordström Skans & Johan Vikström & Yaroslav Yakymovych, 2026, "The Heterogeneous Earnings Impact of Job Loss Across Workers, Establishments, and Markets," RFBerlin Discussion Paper Series, ROCKWOOL Foundation Berlin (RFBerlin), number 26075, Mar.
• Ferrari Minesso, Massimo & Frenzel, Carla, 2026, "Sequential solution for DSGE models with deep neural networks," Working Paper Series, European Central Bank, number 3236, May.
• Ioannou, Demosthenes & Prioriello, Raffaele & Durrani, Agha, 2026, "Measuring geoeconomic tension: a large-language-model approach for the euro area," Working Paper Series, European Central Bank, number 3250, Jul.
• Nakagawa, Hironobu & Chen, Hongyi, 2026, "Real exchange rate dynamics and external balances: Econometric and artificial neural network analyses," Journal of Economic Dynamics and Control, Elsevier, volume 186, issue C, DOI: 10.1016/j.jedc.2026.105312.
• Shi, Aruhan Rui, 2026, "Can an AI agent hit a moving target?," Journal of Economic Dynamics and Control, Elsevier, volume 189, issue C, DOI: 10.1016/j.jedc.2026.105349.
• Wang, Yewen & Li, Cheng, 2026, "Firm-level climate risk exposure and corporate resilience: Evidence from textual analysis," Economic Modelling, Elsevier, volume 162, issue C, DOI: 10.1016/j.econmod.2026.107681.
• Fernández Fernández, José Alejandro & Gómez, Guillermo López & Gómez, Sonia Quiroga, 2026, "“Climatic, financial, and economic systemic risk in the Spanish stock market: An analysis based on artificial intelligence and complex networks”," The North American Journal of Economics and Finance, Elsevier, volume 84, issue C, DOI: 10.1016/j.najef.2026.102622.
• Laborda, Juan & Suárez, Cristina & Fernández, Alejandro & Wang, Haoran & Cerdá, Emilio & Ricci, Liana & Quiroga, Sonia, 2026, "Unveiling how financial markets could intensify climate change risks," Ecological Economics, Elsevier, volume 239, issue C, DOI: 10.1016/j.ecolecon.2025.108773.
• Goulet Coulombe, Philippe & Klieber, Karin, 2026, "An adaptive moving average for macroeconomic monitoring," Economics Letters, Elsevier, volume 259, issue C, DOI: 10.1016/j.econlet.2025.112773.
• Pascal, Julien, 2026, "A generalization of the Parameterized Expectations Algorithm," Economics Letters, Elsevier, volume 259, issue C, DOI: 10.1016/j.econlet.2025.112790.
• Deng, Ming-Yu & Kutlu, Levent & Mao, Xi, 2026, "Decision tree-augmented stochastic frontier analysis," Economics Letters, Elsevier, volume 264, issue C, DOI: 10.1016/j.econlet.2026.112943.
• Babiak, Mykola & Baruník, Jozef, 2026, "Deep learning, predictability, and optimal portfolio returns," Journal of Empirical Finance, Elsevier, volume 87, issue C, DOI: 10.1016/j.jempfin.2026.101705.
• Rahimikia, Eghbal & Poon, Ser-Huang, 2026, "Machine learning for realised volatility forecasting," Journal of Empirical Finance, Elsevier, volume 88, issue C, DOI: 10.1016/j.jempfin.2026.101739.
• Cepeda, Valentina & Pérez, Juan F., 2026, "Deep-learning-based optimal auction design in electricity markets," Energy Economics, Elsevier, volume 155, issue C, DOI: 10.1016/j.eneco.2026.109176.
• Das, Abhinav & Schlüter, Stephan & Schneider, Lorenz, 2026, "Regime-aware conditional neural processes with multi-criteria decision support for operational electricity price forecasting," Energy Economics, Elsevier, volume 157, issue C, DOI: 10.1016/j.eneco.2026.109233.
• Ihsane, Imane & Nait Chabane, Ahmed & Sahnoun, M’hammed, 2026, "Multi-objective optimization of artificial neural networks using Fast NSGA-II for electricity demand forecasting," Energy Economics, Elsevier, volume 160, issue C, DOI: 10.1016/j.eneco.2026.109469.
• Palao, Fernando & Pardo, Ángel & Roig, Marta, 2026, "When presidents make the news, do crude oil markets listen? An LLM-driven analysis," Energy Economics, Elsevier, volume 160, issue C, DOI: 10.1016/j.eneco.2026.109480.
• Wang, Jiarui & Liu, Junqi & Zhu, Lei & He, Gang, 2026, "Resilience planning for power systems under deep climate uncertainty," Energy Economics, Elsevier, volume 161, issue C, DOI: 10.1016/j.eneco.2026.109527.
• Bohórquez Correa, Santiago & Mosquera-López, Stephanía & Uribe, Jorge M., 2026, "Time-varying systemic risk in electricity markets using generative adversarial networks: Market resilience and policy," Energy Policy, Elsevier, volume 210, issue C, DOI: 10.1016/j.enpol.2025.115034.
• Bigerna, Simona & Gattone, Tulia & Magazzino, Cosimo, 2026, "Fossil lock-in, resource dependence, and energy transition policy in the Global South," Energy Policy, Elsevier, volume 216, issue C, DOI: 10.1016/j.enpol.2026.115281.
• Kwon, Yein & Kim, Hongjoong & Moon, Kyoung-Sook, 2026, "Cluster-based Adaptive Generation for imbalanced financial data," Finance Research Letters, Elsevier, volume 106, issue C, DOI: 10.1016/j.frl.2026.110288.
• Zhou, Fan & Guo, Wenjing, 2026, "Time-varying network structure and volatility prediction in the cryptocurrency market," Finance Research Letters, Elsevier, volume 87, issue C, DOI: 10.1016/j.frl.2025.109028.
• Li, Boyan & Wu, Chongfeng, 2026, "Beyond delta neutrality: Confidence-scaled hedging with machine learning forecasts," Finance Research Letters, Elsevier, volume 87, issue C, DOI: 10.1016/j.frl.2025.109098.
• Dai, Yuehao & Shi, Chao & Zhang, Ruixun, 2026, "Estimating market liquidity from daily data: Marrying microstructure models and machine learning," Journal of Financial Markets, Elsevier, volume 79, issue C, DOI: 10.1016/j.finmar.2025.101019.
• Wang, Yicheng & Lera, Sandro Claudio, 2026, "Meta-learning for return prediction in shifting market regimes," Journal of Financial Markets, Elsevier, volume 79, issue C, DOI: 10.1016/j.finmar.2025.101042.
• Guo, Norman (Xuxi), 2026, "Decoding mutual fund performance: Dynamic return patterns via deep learning," Journal of Financial Stability, Elsevier, volume 84, issue C, DOI: 10.1016/j.jfs.2026.101532.
• Fazekas, Mihály & Tóth, Bence & Wachs, Johannes & Abdou, Aly, 2026, "Public procurement cartels: A large-sample testing of screens using machine learning," International Journal of Industrial Organization, Elsevier, volume 104, issue C, DOI: 10.1016/j.ijindorg.2025.103228.
• Thalagoda, Gayani & Hanewald, Katja & Villegas, Andrés M. & Ziveyi, Jonathan, 2026, "Variable annuity portfolio valuation with SHapley Additive exPlanations," Insurance: Mathematics and Economics, Elsevier, volume 129, issue C, DOI: 10.1016/j.insmatheco.2026.103252.
• Moreno-Pérez, Carlos & Minozzo, Marco, 2026, "Monetary policy uncertainty in Mexico: An unsupervised approach," International Economics, Elsevier, volume 186, issue C, DOI: 10.1016/j.inteco.2026.100683.
• Hilscher, Jens & Nabors, Kyle & Raviv, Alon, 2026, "Information in central bank sentiment: An analysis of Fed and ECB communication," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 110, issue C, DOI: 10.1016/j.intfin.2026.102335.
• Turetken, Aysun Can & Leippold, Markus, 2026, "Battle of transformers: Adversarial attacks on financial sentiment models," Journal of Banking & Finance, Elsevier, volume 188, issue C, DOI: 10.1016/j.jbankfin.2026.107698.
• Sawant, Rajwardhan & Kang, Sang Baum, 2026, "Explaining and predicting conditional volatility in lithium markets: Climate policy uncertainty, supply chain stress, and hybrid modeling," Resources Policy, Elsevier, volume 117, issue C, DOI: 10.1016/j.resourpol.2026.105924.
• Wang, Ziyang & Li, Yunpeng & Cui, Zhihao & Zheng, Weinan & Wang, Ting, 2026, "A machine learning-based study of credit risk in supply chain finance of listed service-oriented enterprises in China," Pacific-Basin Finance Journal, Elsevier, volume 96, issue C, DOI: 10.1016/j.pacfin.2025.103043.
• Choi, Insu & Lim, Soyeong & Kim, Seoyeon & Choi, Yeona & Han, Subin & Kim, Woo Chang, 2026, "Metric-based technical indicators for yield forecasting," Pacific-Basin Finance Journal, Elsevier, volume 98, issue C, DOI: 10.1016/j.pacfin.2026.103169.
• Mati, Sagiru & Usman, Abdullahi G. & Ismael, Goran Yousif & Babuga, Umar Tijjani & Nadarajah, Saralees & Masoud, Serag & Uzun Ozsahin, Dilber & Abba, Sani I., 2026, "Explainable support vector regression coupled with quantum firefly optimisation algorithm for carbon emission prediction in West Africa: The role of socioeconomic, energy, and environmental factors," Renewable Energy, Elsevier, volume 256, issue PE, DOI: 10.1016/j.renene.2025.124298.
• Xing, Xiaochao & Hong, Yanran & Wang, Lu, 2026, "A novel LSTM-based Granger-causality approach: A case study on traditional energy and stock markets," Renewable Energy, Elsevier, volume 256, issue PG, DOI: 10.1016/j.renene.2025.124519.
• Jiang, Yifu & Liu, Jine, 2026, "Robust investment portfolio management for dynamic financial markets using Bayesian neural networks," International Review of Economics & Finance, Elsevier, volume 108, issue C, DOI: 10.1016/j.iref.2026.105244.
• Jahangiri, Eshagh & Corazza, Marco, 2026, "Sentiment-based stock price prediction in developing countries: Evidence from Iran," International Review of Economics & Finance, Elsevier, volume 109, issue C, DOI: 10.1016/j.iref.2026.105423.
• Bao, Guanhao & Cui, Baisheng, 2026, "Research on the impact of U.S. monetary policy uncertainty on international oil price forecasting: A deep learning approach based on frequency domain decomposition," Research in International Business and Finance, Elsevier, volume 87, issue C, DOI: 10.1016/j.ribaf.2026.103391.
• Wang, Yuan, 2026, "Deconstructing the green productivity paradox: How AI-enabled green innovation shapes efficiency and technological change," Technological Forecasting and Social Change, Elsevier, volume 230, issue C, DOI: 10.1016/j.techfore.2026.124757.
• Matt Burke & Kamiar Mohaddes & Mehdi Raissi, 2026, "The Adaptation Imperative: Climate Change and Sovereign Credit Risk," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-11, Feb.
• James Morley & Jing Tian & Ben Zhe Wang, 2026, "Disagreement over the Nature of Macroeconomic Shocks," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-21, Mar.
• Jonathan Benchimol & Sathya Mellina, 2026, "Narratives and the Term Structure of Inflation Expectations," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-29, May.
• Himaz, Rozana & Salmanidou, Dimitra & Ghaffarian, Saman, 2026, "Machine Learning for Estimating Catastrophic Health Spending in Disaster-Affected, Data-Scarce Settings," Discussion Papers, Economic Statistics Centre of Excellence, number escoe-dp-2026-05, Mar.
• Marx Davidson Nkonyam Ando & Bartosz Zakrzewski & Irena Dul, 2026, "Artificial Intelligence in the Management of Transportation Companies," European Research Studies Journal, European Research Studies Journal, volume 0, issue 1, pages 432-447.
• Slawomir Bukowski & Joanna Bukowska & Jacek Woloszyn & Agnieszka Molga, 2026, "Forecasting the EUR/PLN Exchange RateUsing Classical and Artificial Intelligence Methods:An Empirical Comparison of ARIMA, XGBoost, LSTMand Hybrid Models on NBP Data 2015-2026," European Research Studies Journal, European Research Studies Journal, volume 0, issue 2, pages 295-317.
• Hassan Raza & Syeda Hina Zaidi & Eleftherios Thalassinos, 2026, "Do ESG Scores and Controversies Improve Corporate Financial Distress Prediction? Evidence from Machine Learning in Asia-Pacific Markets," European Research Studies Journal, European Research Studies Journal, volume 0, issue 2, pages 485-512.
• Daniel Coll Sol & Mario Cuenda Garcia & Bathusi Gabanatlhong & Miroslav Palansky & Tijmen Tuinsma, 2026, "Estimating the Scale of Illicit Financial Flows: The Abnormality Method," Working Papers IES, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, number 2026/08, May, revised May 2026.
• Julie Pernaudet & John List & Arnoldo Müller-Molina & Majid Ahmadi & Imrul Huda & Ajay Sailopal & Dana Suskind, 2026, "Leveraging Artificial Intelligence and Field Experiments to Explore Novel Features of Parental Speech and Foster Child Development," Framed Field Experiments, The Field Experiments Website, number 00836.
• M.Jahangir Alam & Shane Boyle & Huiyu Li & Tatevik Sekhposyan, 2026, "ChatMacro: Evaluating Inflation Forecasts of Generative AI," Working Paper Series, Federal Reserve Bank of San Francisco, number 2026-04, Feb, DOI: 10.24148/wp2026-04.
• Anne Lundgaard Hansen, 2026, "Validating Large Language Model Annotations," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2026-020, Mar, DOI: 10.17016/FEDS.2026.020.
• Tulia Gattone & Donato Romano & Luca Tiberti, 2026, "Pathways of Climate Variability, Agricultural Performance, and Conflict: A Machine Learning Approach to Complex Dependencies," Working Papers - Economics, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa, number wp2026_10.rdf.
• Abdukakhkhor Abdurakhmonov, 2026, "Macroeconomic Forecasting Using Machine Learning Methods: An Application to Uzbekistan," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 19-2026, Aug.
• Adolfo De Unánue & Fernanda Sobrino, 2026, "Machine Learning as Performative Materialist Practice: Thirteen Theses on the Epistemology, Methodology, and Politics of Applied ML," Working Paper Series of the School of Government and Public Transformation, School of Governement and Public Transformation, number 34, May.
• José Morales-Arilla & Rodrigo Sánchez Gavito Portilla & Hermilo Cortés & Ana Gabriela Ibarra, 2026, "Monitoreo económico de alta resolución en El Salvador: Estimación oportuna del crecimiento del PIB y su desagregación espacial," Working Paper Series of the School of Government and Public Transformation, School of Governement and Public Transformation, number 35, Jul.
• Massimo Arnone & Carlo Drago & Alberto Costantiello & Fabio Anobile & Angelo Leogrande, 2026, "From Security to Sustainability: The BES Determinants of Italian Regional GDP," Working Papers, HAL, number hal-05455875, Jan.
• Angelo Leogrande & Fabio Anobile & Alberto Costantiello & Carlo Drago & Massimo Arnone, 2026, "Who Finances the Carbon Transition? Financial Structure, Institutional Quality, and Emissions in OECD Economies," Working Papers, HAL, number hal-05526715, Feb.
• Torben S. D. Johansen & Julius Koschnick & Christian Vedel, 2026, "How to deal with machine learning bias in economic history," Working Papers, European Historical Economics Society (EHES), number 0306, Jul.
• Almgren, Pelle, 2026, "Text-Based Identification of Monetary Policy Shocks," Working Papers, Lund University, Department of Economics, number 2026:10, Sep.
• Yasin Buyukkor, 2026, "Deep Learning in Financial Time Series: A Comparative Analysis of RNN, GRU, LSTM, and Hybrid Models," Croatian Economic Survey, The Institute of Economics, Zagreb, volume 28, issue 1, pages 5-38, June.
• Bogdan Mirea & Giani-Ionel Gradinaru, 2026, "Ethics and bias in AI: a potential challenge to fair economic progress," Romanian Journal of Economics, Institute of National Economy, volume 62, issue 1(71), pages 99-110, June.
• Mikhail Stolbov & Maria Shchepeleva, 2026, "Measuring global financial stress: is there any role for large language models?," Annals of Finance, Springer, volume 22, issue 1, pages 1-22, June, DOI: 10.1007/s10436-026-00481-4.
• Bhanu Pratap & Amit Pawar & Shovon Sengupta, 2026, "Non-linear Phillips Curve for India: Evidence from Explainable Machine Learning," Computational Economics, Springer;Society for Computational Economics, volume 67, issue 3, pages 2301-2344, March, DOI: 10.1007/s10614-025-10942-z.
• Wael Dammak & Ali Ben Mrad & Christian de Peretti & Salah Ben Hamad, 2026, "Enhancing Currency Option Pricing Models: Incorporating Dynamic Information Costs and Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, volume 67, issue 4, pages 2603-2642, April, DOI: 10.1007/s10614-025-10939-8.
• Jiaxiang Huang & Renxiang Wang, 2026, "Prediction of Bank Systemic Risk Based on LSTM Model," Computational Economics, Springer;Society for Computational Economics, volume 67, issue 4, pages 3067-3086, April, DOI: 10.1007/s10614-025-10960-x.
• Aparna Gupta & Cheng Lu & Majeed Simaan & Mohammed J. Zaki, 2026, "When Positive Sentiment is not so Positive: Textual Analytics and Bank Failures," Computational Economics, Springer;Society for Computational Economics, volume 68, issue 1, pages 621-661, July, DOI: 10.1007/s10614-025-10969-2.
• David Alaminos & M. Belén Salas-Compás & Estefanía Alaminos, 2026, "High-Frequency Trading, Short Squeeze and ARMA-GARCH-Fractal Neural Networks," Computational Economics, Springer;Society for Computational Economics, volume 68, issue 2, pages 1097-1154, August, DOI: 10.1007/s10614-025-11026-8.
• Dalia ATIF, 2026, "VAE-INN: Variational Autoencoder with Integrated Neural Network Classifier for Imbalanced Credit Scoring, Utilizing Weighted Loss for Improved Accuracy," Computational Economics, Springer;Society for Computational Economics, volume 68, issue 2, pages 1213-1243, August, DOI: 10.1007/s10614-025-11094-w.
• Jin Hyung Lee, 2026, "Competitive Bidding Strategy in an Auction with Random Cutoff - Randomness is Always Unpredictable?," Computational Economics, Springer;Society for Computational Economics, volume 68, issue 3, pages 2199-2228, September, DOI: 10.1007/s10614-025-11087-9.
• Levent Kutlu & Xi Mao & Xuelei Sherry Ni, 2026, "A machine learning approach to stochastic frontier modeling," Journal of Productivity Analysis, Springer, volume 65, issue 2, pages 1-12, June, DOI: 10.1007/s11123-026-00800-x.
• Simon Fritzsch & Felix Irresberger & Gregor Weiß, 2026, "Predicting option prices from their price history via machine learning," Review of Derivatives Research, Springer, volume 29, issue 1, pages 1-38, December, DOI: 10.1007/s11147-026-09228-9.
• Mohd Raagib Shakeel & Satyam Yadav & Musheer Ahmad, 2026, "Option pricing under regime-switching jump-diffusion dynamics with transaction costs: a neural SDE approach," Review of Derivatives Research, Springer, volume 29, issue 1, pages 1-70, December, DOI: 10.1007/s11147-026-09238-7.
• Keorapetse Leballo & Jules Clement Mba, 2026, "Stress-consistent macroprudential overlay for derivative pricing," Review of Derivatives Research, Springer, volume 29, issue 1, pages 1-31, December, DOI: 10.1007/s11147-026-09241-y.
• Ahmet Umur Özsoy, 2026, "Selective forgetting in option calibration: an operator-theoretic Gauss–Newton framework," Review of Derivatives Research, Springer, volume 29, issue 1, pages 1-27, December, DOI: 10.1007/s11147-026-09243-w.
• Alexander Arimond & Damian S. Borth & Sergio Garcia-Vega & Maretno Harjoto & Andreas G. F. Hoepner & Michael Klawunn & Stefan Weisheit, 2026, "Neural Networks and Value at Risk in Asset Management," Review of Quantitative Finance and Accounting, Springer, volume 67, issue 1, pages 277-316, July, DOI: 10.1007/s11156-025-01460-y.
• Gautami Parate & Arpita Choudhary, 2026, "Patent Valuation under Fragile Institutional Enforcement: A Continuous-Time Markov Approach," Working Papers, Madras School of Economics,Chennai,India, number 2026-293, Jan.
• Purbita Jana, 2026, "Explainable Decision Support in Multi-Agent AI Systems Using L-Valued Information Flow and Shapley Aggregation," Working Papers, Madras School of Economics,Chennai,India, number 2026-299, May.
• Ramit Das & Purbita Jana, 2026, "Generalised Geometric Logic: A Logic for Expressing Neural Network Architectures," Working Papers, Madras School of Economics,Chennai,India, number 2026-300, May.
• Alice Treesa M & Dr. Arpita Choudhary, 2026, "Comparative Study of Machine Learning and Deep Learning Models for Short-Term Energy Consumption Prediction," Working Papers, Madras School of Economics,Chennai,India, number 2026-301, May.
• Tri Gunarsih & Rodhiyah Mardhiyah & Fran Sayekti, 2026, "Bibliometric Analysis of Bankruptcy Prediction in Financial Institutions: Themes, Evidence, and A Future Research Agenda," Capital Markets Review, Malaysian Finance Association, volume 34, issue 1, pages 89-105.
• Csanad Temesvari & Beata Horvath & Livia Reka Onozo, 2026, "Natural Language Processing-Driven Use-Cases for Economic Analysis Using Unstructured Data," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), volume 25, issue 1, pages 27-52.
• Christian S. de Leon, 2026, "A Meta-Learning Model of Philippine Bank Lending Behaviour," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), volume 25, issue 2, pages 119-158.
• Nicole Czaplicki & Colin J. Shevlin & Hector R. Ferronato & Aidan D. Smith & Dwarakh R. Nayam & Lei Peng & Scott W. Springer & Doren Walker, 2026, "A Blended Data Approach to Measuring Monthly Housing Starts: Satellite Imagery, Survey Data and More!," NBER Chapters, National Bureau of Economic Research, Inc, "Measurement of Housing and the Housing Sector".
• Hui Chen & Yuhan Cheng & Yanchu Liu & Ke Tang, 2026, "Teaching Economics to the Machines," NBER Working Papers, National Bureau of Economic Research, Inc, number 34713, Jan.
• Joshua S. Gans, 2026, "Optimal Use of Preferences in Artificial Intelligence Algorithms," NBER Working Papers, National Bureau of Economic Research, Inc, number 34780, Jan.
• Lauren Cohen & Yiwen Lu & Quoc H. Nguyen, 2026, "Mimicking Finance," NBER Working Papers, National Bureau of Economic Research, Inc, number 34849, Feb.
• Yijie Wang & Hao Gao & Campbell R. Harvey & Yan Liu & Xinyuan Tao, 2026, "Machine Learning Meets Markowitz," NBER Working Papers, National Bureau of Economic Research, Inc, number 34861, Feb.
• Susan Athey & Lisa K. Simon & Oskar Nordström Skans & Johan Vikstrom & Yaroslav Yakymovych, 2026, "The Heterogeneous Earnings Impact of Job Loss Across Workers, Establishments, and Markets," NBER Working Papers, National Bureau of Economic Research, Inc, number 34946, Mar.
• Simon Greenhill & Brant J. Walker & Joseph S. Shapiro, 2026, "Deep Learning Projects Jurisdiction of New and Proposed Clean Water Act Regulation," NBER Working Papers, National Bureau of Economic Research, Inc, number 34947, Mar.
• Hui Chen & Antoine Didisheim & Luciano A. Somoza, 2026, "Out of the Black Box: Uncertainty Quantification for LLMs via Conditional Probabilities," NBER Working Papers, National Bureau of Economic Research, Inc, number 34965, Mar.
• Easton K. Huch & Michael P. Keane, 2026, "Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks," NBER Working Papers, National Bureau of Economic Research, Inc, number 35037, Apr.
• Antoine Didisheim & Bryan T. Kelly & Mohammad Pourmohammadi & Hanqing Tian, 2026, "The Inefficient Pricing of News," NBER Working Papers, National Bureau of Economic Research, Inc, number 35093, Apr.
• Nicole Czaplicki & Colin J. Shevlin & Hector R. Ferronato & Aidan D. Smith & Dwarakh V. Nayam & Lei Peng & Scott W. Springer & Doren Walker, 2026, "A Blended Data Approach to Measuring Monthly Housing Starts: Satellite Imagery, Survey Data and More!," NBER Working Papers, National Bureau of Economic Research, Inc, number 35113, Apr.
• Sebastian Bell & Ali Kakhbod & Martin Lettau & Abdolreza Nazemi, 2026, "AlphaGlass: Interpretable Characteristic-Based Portfolio Choice," NBER Working Papers, National Bureau of Economic Research, Inc, number 35186, May.
• Bryan T. Kelly & Semyon Malamud & Johannes Schwab & Teng Andrea Xu, 2026, "Scaling Point-in-Time Language Models," NBER Working Papers, National Bureau of Economic Research, Inc, number 35247, May.
• Victor Duarte & Julia Fonseca, 2026, "AI for Structural Estimation," NBER Working Papers, National Bureau of Economic Research, Inc, number 35283, May.
• Julie Pernaudet & John A. List & Arnoldo Müller-Molina & Majid Ahmadi & Imrul Huda & Ajay Sailopal & Dana Suskind, 2026, "Leveraging Artificial Intelligence and Field Experiments to Explore Novel Features of Parental Speech and Foster Child Development," NBER Working Papers, National Bureau of Economic Research, Inc, number 35302, Jun.
• Melissa Dell & Ashesh Rambachan, 2026, "The Measurement Revolution? Credible Measurement and Inference in the Age of AI," NBER Working Papers, National Bureau of Economic Research, Inc, number 35744, Sep.
• Teplova, T. & Sokolova, T. & Kissa, D. & Gurov, S., 2026, "ESG indicators as determinants of the risk of a decline in Russian stock prices in different periods: A view of Explainable AI," Journal of the New Economic Association, New Economic Association, volume 70, issue 1, pages 157-190, DOI: 10.31737/22212264_2026_1_157-190.
• Matevosova, A., 2026, "Sentiment analysis for monetary policy research," Journal of the New Economic Association, New Economic Association, volume 71, issue 2, pages 314-323, DOI: 10.31737/22212264_2026_2_314-323.
• Samrajya Raj Acharya & Aayush Man Regmi & Kanhaiya Jha, 2026, "Exploring Trajectories of Government Bonds for Debt Planning Using Machine Learning Models," NRB Economic Review, Nepal Rastra Bank, Economic Research Department, volume 37, issue 1, pages 1-27, April.
• Liu Jieni, 2026, "A Search-Then-Forecast Transformer Framework for Mid-Term Stock Price Prediction: An Empirical Case Study on the Chinese A-Share Market," Discussion Papers in Economics and Business, Osaka University, Graduate School of Economics, number 26-06, Apr.
• Anna Denkowska & Krystian Szczȩsny & Stanisław Wanat, 2026, "Nonlinear dependencies in Solvency II: risk aggregation with deep neural networks," Risk Management, Palgrave Macmillan, volume 28, issue 2, pages 1-31, May, DOI: 10.1057/s41283-026-00191-1.
• Shujie Li, 2026, "Comparing the estimation of Value at Risk and Expected Shortfall with LSTM and EGARCH family members," Working Papers CIE, Paderborn University, CIE Center for International Economics, number 173, Mar.
• Bahaa Aly, Tarek, 2026, "Global Economic Cycles Unveiled: A Hybrid TCN-HMM Approach for Regime Dynamics Across Eight Nations," MPRA Paper, University Library of Munich, Germany, number 127574, Jan.
• Arnone, Massimo & Drago, Carlo & Costantiello, Alberto & Anobile, Fabio & Leogrande, Angelo, 2026, "From Security to Sustainability: The BES Determinants of Italian Regional GDP," MPRA Paper, University Library of Munich, Germany, number 127706, Jan.
• Leogrande, Angelo & Anobile, Fabio & Costantiello, Alberto & Drago, Carlo & Arnone, Massimo, 2026, "Who Finances the Carbon Transition? Financial Structure, Institutional Quality, and Emissions in OECD Economies," MPRA Paper, University Library of Munich, Germany, number 128168, Feb.
• Ji, Zihao & Wang, Guan & Hu, Chenxi & Zhang, Hongru, 2026, "Non-linear Spillover of External EPU on Macau Gaming Stock Volatility: Micro-foundations using TVP-VAR and ML Attribution," MPRA Paper, University Library of Munich, Germany, number 128532, Jan.
• Labastidas, Esteban, 2026, "A Hybrid Early-Warning System for Inflation in an Emerging Market: Combining Econometric Models, an Agent-Based Decomposition with Heterogeneous Expectations, a Large Language Model, and a Multi-Output Agent Architecture," MPRA Paper, University Library of Munich, Germany, number 128779, Apr.
• boughabi, houssam, 2026, "Fiscal Regimes and Wage Formation: Learning Distributive Conflict in a Kaleckian Economy," MPRA Paper, University Library of Munich, Germany, number 128993, May.
• Sanchez, Paulo, 2026, "Nowcasting with Novel High-Frequency Data: A Cross-Method Comparison for Colombia’s ISE," MPRA Paper, University Library of Munich, Germany, number 129072, May.
• Yagufarov, Ruslan, 2026, "Two-scale topological momentum and persistence of stress regimes in correlation networks: evidence from equity markets," MPRA Paper, University Library of Munich, Germany, number 129341, May.
• Garau, Alessio, 2026, "How economics classifies itself: text-based JEL codes and their consistency," MPRA Paper, University Library of Munich, Germany, number 130163, Jul.
• Sphiwe B. Skhosana & Abeeb O. Olaniran & Najmeh Nakhaei Rad & Rangan Gupta, 2026, "Economic Complexity and Environmental Impact using a Neural-Network Embedded Semiparametric Mixture of Experts Model," Working Papers, University of Pretoria, Department of Economics, number 202618, Jun.
• Steffen Jahn & Tobias Klinke & Daniel Guhl & Maja Murr, 2026, "Re-fielding Replicability of Silicon Sample-based Marketing Research," Rationality and Competition Discussion Paper Series, CRC TRR 190 Rationality and Competition, number 583, Aug.
• Joan Christine S. Allon-Pineda, 2026, "Inflation Unpacked: Breaking Down the Key Components Using a Neural Phillips Curve," Asian Journal of Applied Economics/ Applied Economics Journal, Kasetsart University, Faculty of Economics, Center for Applied Economic Research, volume 33, issue 2, July.
• Andre Mouton, 2026, "Measuring Task-Level Technological Exposure: A Language Model Approach," Working Papers, Wake Forest University, Economics Department, number 132, Feb.
• Elliot Beck & Franziska Eckert & Linus Kühne & Helge Liebert & Rina Rosenblatt-Wisch, 2026, "Measuring economic outlook in the news," Working Papers, Swiss National Bank, number 2026-04.
• Arvind Ashta, 2026, "Artificial Intelligence in Microfinance and Financial Inclusion: Applications, Issues, and Future Directions," Working Papers CEB, ULB -- Universite Libre de Bruxelles, number 26-008, Jun.
• Cosimo Magazzino & Benedetta Coluccia & Donatella Porrini & Tulia Gattone, 2026, "Agents of digitalization: gendered employment patterns and broadband access across Asian economies," The Annals of Regional Science, Springer;Western Regional Science Association, volume 75, issue 1, pages 1-28, March, DOI: 10.1007/s00168-025-01432-z.
• Sergio Scicchitano & Marco Biagetti & Tulia Gattone & Cosimo Magazzino, 2026, "Digital transformation and remote work: gender, family size, and education in shaping work-from-home perceptions during COVID-19," The Annals of Regional Science, Springer;Western Regional Science Association, volume 75, issue 3, pages 1-39, September, DOI: 10.1007/s00168-026-01469-8.
• Yuanfang Chen & Wee-Yeap Lau & Lim-Thye Goh, 2026, "Spatio-temporal heterogeneity effect of digital talent mobility on economic growth: evidence from urban agglomerations in China," Asia-Pacific Journal of Regional Science, Springer, volume 10, issue 2, pages 1-37, June, DOI: 10.1007/s41685-026-00430-z.
• Ayben Koy & Semra Demir & Andaç Batur Çolak, 2026, "Google trend index as an investor sentiment proxy in cryptomarket: nonlinear relationships with cryptomarket and predicting bitcoin returns with machine learning approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, volume 34, issue 2, pages 575-595, June, DOI: 10.1007/s10100-025-01012-8.
• Soheil Salahshour & Mehdi Salimi & Kian Tehranian & Niloufar Erfanibehrouz & Massimiliano Ferrara & Ali Ahmadian, 2026, "Deep prediction on financial market sequence for enhancing economic policies," Decisions in Economics and Finance, Springer;Associazione per la Matematica, volume 49, issue 1, pages 5-24, June, DOI: 10.1007/s10203-024-00488-4.
• Rita Pimentel & Morten Risstad & Sondre Rogde & Erlend S. Rygg & Jacob Vinje & Sjur Westgaard & Cassandra Wu, 2026, "Option pricing with deep learning: a long short-term memory approach," Decisions in Economics and Finance, Springer;Associazione per la Matematica, volume 49, issue 1, pages 155-186, June, DOI: 10.1007/s10203-025-00518-9.
• Vaibhav Gagneja & Mayank Gupta & Sanjay Batish & Poonam Saini & Sudesh Rani, 2026, "ES-LSTM: a hybrid model for accurate time series forecasting in financial markets," Digital Finance, Springer, volume 8, issue 1, pages 1-21, March, DOI: 10.1007/s42521-025-00173-0.
• Qizhao Chen & Hiroaki Kawashima, 2026, "Sentiment-aware stock price prediction with transformer and LLM-generated formulaic alpha," Digital Finance, Springer, volume 8, issue 2, pages 1-28, June, DOI: 10.1007/s42521-026-00176-5.
• Hoang Anh Nguyen & Nhat Hoang Bach, 2026, "QI-HRNN: a quantum-inspired hybrid framework for resilient currency forecasting under extreme market conditions," Digital Finance, Springer, volume 8, issue 2, pages 1-40, June, DOI: 10.1007/s42521-026-00189-0.
• Huyen Giang Thi Thu & Thang Viet Doan & Ha-Bang Ban & Tai Le Quy, 2026, "An experimental study on fairness-aware machine learning for credit scoring problems," Digital Finance, Springer, volume 8, issue 3, pages 1-26, September, DOI: 10.1007/s42521-026-00202-6.
• Alexandra Ioana Conda & Ștefan Găman & Raul Cristian Bâg & Miruna Mazurencu-Marinescu-Pele & Daniel Traian Pele & Wolfgang Karl Härdle, 2026, "BitMood: AI analysis of Bitcoin trends via Facebook emotions," Digital Finance, Springer, volume 8, issue 3, pages 1-25, September, DOI: 10.1007/s42521-026-00205-3.
• Jinwon Kim & Seongsoo Jang & Ulrike Gretzel & Chulmo Koo, 2026, "Special issue on “Smart tourism 2.0: Perspectives with geospatial data and AI”," Electronic Markets, Springer;IIM University of St. Gallen, volume 36, issue 1, pages 1-5, December, DOI: 10.1007/s12525-025-00866-9.
• Radmir Mishelevich Leushuis & Nicolai Petkov, 2026, "Advances in forecasting realized volatility: a review of methodologies," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 12, issue 1, pages 1-29, December, DOI: 10.1186/s40854-025-00809-5.
• Hugo Gobato Souto & Amir Moradi, 2026, "Enhancing financial risk management: a novel multivariate neural network approach for realized covariance matrix prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 12, issue 1, pages 1-26, December, DOI: 10.1186/s40854-025-00816-6.
• Heng Xiong & Yuxuan Guo & Ričardas Zitikis, 2026, "Beyond no-claims discount: a learning-embedded telematics-driven pricing system for dynamic premium adjustments," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 12, issue 1, pages 1-38, December, DOI: 10.1186/s40854-026-00928-7.
• Boyu Wang & Xuefeng Gao & Lingfei Li, 2026, "Reinforcement learning for continuous-time optimal execution: actor–critic algorithm and error analysis," Finance and Stochastics, Springer, volume 30, issue 2, pages 597-655, April, DOI: 10.1007/s00780-026-00589-5.
• Hien Thu Bui & Thu Anh Trinh & Nhung Ha Phuong Vu & Linh Thi Thuy Pham & Hang Nguyet Trieu, 2026, "ESG, firm systematic risk, and economic policy uncertainty: analysis using deep learning-based ESG scores," Future Business Journal, Springer, volume 12, issue 1, pages 1-15, December, DOI: 10.1186/s43093-026-00830-9.
• Tetsuya Kaji & Elena Manresa, 2026, "Why do the elderly save? using health shocks to uncover bequest motives," The Japanese Economic Review, Springer, volume 77, issue 2, pages 355-378, April, DOI: 10.1007/s42973-026-00249-5.
• Konstantinos Gkillas & Constantinos Halkiopoulos, 2026, "AI-Driven Smart Tourism: Leveraging Deep Learning and Financial Econometrics for Predictive Risk Management and Dynamic Destination Optimization," Springer Proceedings in Business and Economics, Springer, in: Vicky Katsoni, "Synergizing Management, Culture, and Arts for Tourism Development - Vol. 1", DOI: 10.1007/978-3-032-17545-8_17.
• Nikolina Christou & Vassiliki Karioti, 2026, "Forecasting Tourist Indicators in Greece: A Comparative Evaluation of Statistical and Deep Learning Models," Springer Proceedings in Business and Economics, Springer, in: Vicky Katsoni, "Synergizing Management, Culture, and Arts for Tourism Development - Vol. 1", DOI: 10.1007/978-3-032-17545-8_22.
• Gideon Mazuruse & Brighton Nyagadza, 2026, "Barriers to adopting climate change awareness and education in Zimbabwe: a hybrid structural equation modelling and extreme gradient boosting approach," Quality & Quantity: International Journal of Methodology, Springer, volume 60, issue 1, pages 2883-2912, February, DOI: 10.1007/s11135-025-02384-4.
• Mohd Redzuan Ahmad & Mohd Herwan Sulaiman, 2026, "Machine learning-based gold price forecasting: a bibliometric review of trends, methods, and future directions," SN Business & Economics, Springer, volume 6, issue 8, pages 1-38, August, DOI: 10.1007/s43546-026-01299-y.
• Elliott Ash & Stephen Hansen & Yabra Muvdi & Claudia Marangon, 2026, "Large Language Models in Economics," Springer Books, Springer, chapter 0, in: Shlomo Weber & Victor Ginsburgh, "The Palgrave Handbook of Economics and Language", DOI: 10.1007/978-3-031-88240-1_8.
• Ian Staley, 2026, "Quantum-Inspired Counterfactual Explainable AI with Blockchain-Based Provenance for Governed Automated Decision-Making: An Empirical Evaluation on Credit Underwriting," Journal of Applied Finance & Banking, SCIENPRESS Ltd, volume 16, issue 3, pages 1-5.