Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2026

• Afees A. Salisu & Abeeb O. Olaniran, 2026, "Energy market uncertainty and economic conditions at the global and U.S. State levels," Journal of Economics and Finance, Springer;Academy of Economics and Finance, volume 50, issue 1, pages 1-51, December, DOI: 10.1007/s12197-025-09739-5.
• Elie Bouri & Rangan Gupta & Asingamaanda Liphadzi & Christian Pierdzioch, 2026, "Forecasting the volatility of stock returns in the G7 countries over centuries: the role of climate risks," Journal of Economics and Finance, Springer;Academy of Economics and Finance, volume 50, issue 1, pages 1-32, December, DOI: 10.1007/s12197-026-09751-3.
• Bilgehan Tekin, 2026, "Bitcoin as a Behavioral Bellwether: Unveiling the Bandwagon Effect and Investor Sensitivity in the NFT Landscape," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), volume 17, issue 2, pages 3714-3739, April, DOI: 10.1007/s13132-025-02788-5.
• Zhiyang Jia & Snorre Skagseth & Thor O. Thoresen & Trine E. Vattø, 2026, "Apractical framework for behavioral microsimulation using external evidence," Discussion Papers, Statistics Norway, Research Department, number 1034, Feb.
• Carlos Montes-Galdón & Joan Paredes & Elias Wolf, 2026, "A robust approach to tilting: parametric relative entropy," Working and Discussion Papers, Research Department, National Bank of Slovakia, number WP 2/2026, Feb.
• Garratt Anthony & Petrella Ivan & Zhang Yunyi, 2026, "The Predictive Content of U.S. Energy Information Administration Oil Market Forecasts," Working papers, Department of Economics, Social Studies, Applied Mathematics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino, number 104, Mar.
• Yachou Najlae & Abahman Omar & Hakimi Khalid, 2026, "Designing an LSTM-Based Model for Financial Asset Forecasting Using Machine Learning," Central European Economic Journal, Sciendo, volume 13, issue 60, pages 1-23, DOI: 10.2478/ceej-2026-0001.
• Kumar Suresh & Ali Hyder, 2026, "Liquidity risk and liquidity timing in the cross-section of Indian equity mutual fund returns," Economics and Business Review, Sciendo, volume 12, issue 1, pages 105-133, DOI: 10.18559/ebr.2026.1.2746.
• Tatarczak Anna & Humeniuk Oleksandra, 2026, "Forecasting cryptocurrencies in turbulent times: Evidence on parsimony versus model complexity," Economics and Business Review, Sciendo, volume 12, issue 1, pages 135-158, DOI: 10.18559/ebr.2026.1.2652.
• Pham Thuy Tu, 2026, "Global Information Uncertainty and Real Estate Stock Valuation in Emerging Markets: an Integrated Behavioral - Theoretical and Machine Learning Framework," Real Estate Management and Valuation, Sciendo, volume 34, issue 1, pages 63-83, DOI: 10.2478/remav-2026-0006.
• Harold Glenn A. Valera & Cymon Kayle Lubangco & Mark J. Holmes, 2026, "Does repeated cross-section data help explain consumer inflation expectations revisions?," Working Papers in Economics, University of Waikato, number 26/03, Feb.
• Evangelos E. Ioannidis & Sofia‐Eirini Nikolakakou, 2026, "Modeling and Forecasting Stochastic Seasonality: Are Seasonal Autoregressive Integrated Moving Average Models Always the Best Choice?," Journal of Forecasting, John Wiley & Sons, Ltd., volume 45, issue 1, pages 316-334, January, DOI: 10.1002/for.70034.
• Verona, Fabio, 2026, "Forecasting inflation: The sum of the cycles outperforms the whole," Bank of Finland Research Discussion Papers, Bank of Finland, number 1/2026.
• Fausch, Jürg & Frigg, Moreno & Ruenzi, Stefan & Weigert, Florian, 2026, "Machine learning mutual fund flows," CFR Working Papers, University of Cologne, Centre for Financial Research (CFR), number 26-03.
• Dallari, Pietro & Gattini, Luca, 2026, "How severe are European regulatory stress test scenarios? A probabilistic calibration for the euro area," EIB Working Papers, European Investment Bank (EIB), number 2026/01, DOI: 10.2867/0689043.
• Guest, Oliver & Steenkamp, Daan, 2026, "Is the rand at fair value?," EconStor Research Reports, ZBW - Leibniz Information Centre for Economics, number 336005.
• Heinisch, Katja & van Norden, Simon & Wildi, Marc, 2026, "Smooth and persistent forecasts of German GDP: Balancing accuracy and stability," IWH Discussion Papers, Halle Institute for Economic Research (IWH), number 1/2026, DOI: 10.18717/dp99kr-7336.
• Drygalla, Andrej & Heinisch, Katja & Holtemöller, Oliver & Lindner, Axel & Schult, Christoph & Zeddies, Götz, 2026, "Einhaltung der EU-Fiskalregeln erfordert umfangreiche Konsolidierung: Mittelfristige Projektion der gesamtwirtschaftlichen Entwicklung und der öffentlichen Finanzen in Deutschland," IWH Policy Notes, Halle Institute for Economic Research (IWH), number 1/2026, DOI: 10.18717/pnb1fm-ja32.
• Mengnan Xu & Qifa Xu & Cuixia Jiang & Xingxuan Zhuo, 2026, "High-frequency Growth-at-Risk of China: the Role of Macro-financial Environment," Computational Economics, Springer;Society for Computational Economics, volume 67, issue 3, pages 1533-1570, March, DOI: 10.1007/s10614-025-10927-y.
• 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.
• Kerim Keskin, 2026, "A game theory approach to football predictions," Public Choice, Springer, volume 206, issue 1, pages 241-261, January, DOI: 10.1007/s11127-025-01317-x.
• Ewa Dziwok & Witold Szczepaniak, 2026, "Systemic risk and climate change: a joint impact of transition and physical climate risks on the Polish banking sector," Bank i Kredyt, Narodowy Bank Polski, volume 57, issue 1, pages 105-134.
• Ming Gu & David Hirshleifer & Siew Hong Teoh & Shijia Wu, 2026, "GIFfluence: A Visual Approach to Investor Sentiment and the Stock Market," NBER Working Papers, National Bureau of Economic Research, Inc, number 34636, 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.
• Kurt G. Lunsford & Kenneth D. West, 2026, "An Empirical Evaluation of Some Long-Horizon Macroeconomic Forecasts," NBER Working Papers, National Bureau of Economic Research, Inc, number 34904, Feb.
• Lauren Cohen & Bo Li, 2026, "The Micro-Geography of Persuasion: Randomized Peer Exposure and Legislative Outcomes," NBER Working Papers, National Bureau of Economic Research, Inc, number 34925, Mar.
• Takashi Miyahara & Laura Betschka, 2026, "Can the Sahm rule indicator signal recession in OECD countries?," OECD Statistics Working Papers, OECD Publishing, number 2026/01, Mar, DOI: 10.1787/c15f9c28-en.
• 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.
• Didit B. Nugroho & Bambang Susanto & Faldy Tita & Takayuki Morimoto, 2026, "Real-time return extensions of realized GARCH models for improved risk management in asset markets," Journal of Asset Management, Palgrave Macmillan, volume 27, issue 2, pages 1-19, June, DOI: 10.1057/s41260-026-00452-4.
• Wojciech Starosta, 2026, "Calibrating credit risk parameters for climate stress testing," Risk Management, Palgrave Macmillan, volume 28, issue 1, pages 1-24, February, DOI: 10.1057/s41283-025-00189-1.
• Kazeem Ovanero Isah, 2026, "Assessing climate risk and resilience across stocks, ESG portfolios, and REITs: evidence from predictive modelling," Risk Management, Palgrave Macmillan, volume 28, issue 2, pages 1-19, May, DOI: 10.1057/s41283-026-00216-9.
• 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.
• Kahambwe, Christ & Aidini, Christian & E.Loemba, Alexandre, 2026, "Intelligence artificielle et transformation de la relation croissance –emploi : une relecture empirique de la loi d’okun
  [Artificial Intelligence and the transformation of the growth–employment nexus: an empirical reappraisal of okun’s law]," MPRA Paper, University Library of Munich, Germany, number 127930, Jan, revised 2026.
• Fantazzini, Dean & Kurbatskii, Alexey, 2026, "Nowcasting and Forecasting Russian Regional CPI: Sparse Models and the Time-Varying Value of Online Data," MPRA Paper, University Library of Munich, Germany, number 128456.
• Hardy, Nicolas & Korobilis, Dimitris, 2026, "Generalized Bayesian Composite Quantile Regression with an Application to Equity Premium Forecasting," MPRA Paper, University Library of Munich, Germany, number 128752, Apr.
• Giovanni Bonaccolto & Massimiliano Caporin & Oguzhan Cepni & Rangan Gupta, 2026, "Forecasting Realized Volatility of State-Level Stock Markets of the United States: The Role of Sentiment," Working Papers, University of Pretoria, Department of Economics, number 202603, Feb.
• Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2026, "Credit Standards: A New Predictor of U.S. Stock Market Realized Volatility," Working Papers, University of Pretoria, Department of Economics, number 202607, Mar.
• Onur Polat & Rangan Gupta & Dhanashree Somani & Sayar Karmakar, 2026, "Machine Learning Forecasting of U.S. Stock Market Volatility: The Role of Stock and Oil Bubbles," Working Papers, University of Pretoria, Department of Economics, number 202611, Apr.
• Piotr Mielus, 2026, "Volatility Modelling - What Drives Cee Currency Option Prices?," Prague Economic Papers, Prague University of Economics and Business, volume 2026, issue 1, pages 1-27, DOI: 10.18267/j.pep.906.
• Yang Yu & Yaping Gong & DooHwan Won & Atif Jahanger, 2026, "Regional Disparities in Drivers and Peaking Pathways of CO2 Emissions: Insights from Scenario Planning," Politická ekonomie, Prague University of Economics and Business, volume 2026, issue 1, pages 170-197, DOI: 10.18267/j.polek.1484.
• Luz Judith Rodríguez Esparza & Dolly Anabel Ortiz Lazcano, 2026, "A stochastic model to analyze the dynamics of poverty and social mobility in Mexico," EconoQuantum, Revista de Economia y Finanzas, Universidad de Guadalajara, Centro Universitario de Ciencias Economico Administrativas, Departamento de Metodos Cuantitativos y Maestria en Economia., volume 23, issue 1, pages 25-48, January-J.
• Winkelried, Diego & Jason Cruz & Javier Torres, 2026, "Nowcasting GDP using data revisions in an emerging economy," Working Papers, Banco Central de Reserva del Perú, number 2026-003, Apr.
• Mara Giua & Francesca Micocci & Giulia Valeria Sonzogno, 2026, "Enhancing Implementation SuccessinCohesion Policy. A Machine Learning Approach," Departmental Working Papers of Economics - University 'Roma Tre', Department of Economics - University Roma Tre, number 0289, Mar.
• Dimitris Anastasiou & Apostolos Katsafados & Christos Tzomakas, 2026, "Banks’ stock price crash risk prediction with textual analysis: a machine learning approach," Annals of Operations Research, Springer, volume 357, issue 1, pages 89-111, February, DOI: 10.1007/s10479-025-06567-y.
• Philippe Bertrand & Jean-luc Prigent, 2026, "On the performance of factor investing: an analysis based on constant mix and buy-and-hold strategies," Annals of Operations Research, Springer, volume 357, issue 1, pages 531-563, February, DOI: 10.1007/s10479-025-06644-2.
• Xiaoqing Luo, 2026, "When simplicity fails: forecasting Mainland Chinese tourist arrivals in Macao during structural breaks with a hybrid economic-search model," Asia-Pacific Journal of Regional Science, Springer, volume 10, issue 1, pages 1-31, March, DOI: 10.1007/s41685-026-00419-8.
• 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.
• 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.
• Christos Tzomakas, 2026, "Monetary policy transmission and the yield curve: the role of external market factors against the backdrop of Brexit," Empirical Economics, Springer, volume 70, issue 1, pages 1-42, January, DOI: 10.1007/s00181-025-02874-2.
• Alena Skolkova, 2026, "Model averaging with ridge regularization," Empirical Economics, Springer, volume 70, issue 2, pages 1-19, February, DOI: 10.1007/s00181-025-02866-2.
• Elliot Beck & Michael Wolf, 2026, "Forecasting inflation with the hedged random forest," Empirical Economics, Springer, volume 70, issue 2, pages 1-36, February, DOI: 10.1007/s00181-025-02879-x.
• Visa Kuntze & Henri Nyberg & Samuel Rauhala, 2026, "Similarity-based path forecasting of US recession periods," Empirical Economics, Springer, volume 70, issue 3, pages 1-18, March, DOI: 10.1007/s00181-026-02893-7.
• Arthur Jonath & Fred Khorasani & John O’Connell, 2026, "The consumer-to-producer temperature gradient predicts leading indicators: a new economic measurement based on physical principles and cause and effect," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 12, issue 1, pages 1-24, December, DOI: 10.1186/s40854-025-00805-9.
• 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.
• 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.
• Jose Rizal & Nur Afandi & Gusman Juliadi & Indah Wahyuliani & Cinta Rizki Oktarina, 2026, "Forecasting the Appearance Frequency of Rafflesia arnoldii in Bengkulu, Indonesia, Using Discrete-valued Time Series Modeling," Advances in Decision Sciences, Asia University, Taiwan, volume 30, issue 2, pages 39-67, March.
• Sarthak Behera & Hyeongwoo Kim & Soohyon Kim, 2026, "Asymmetric Roles of Macroeconomic Variables in the Real Exchange Rate: Insights from U.S.-Korea Data," Auburn Economics Working Paper Series, Department of Economics, Auburn University, number auwp2026-01, Jan.
• Casoli, Chiara & Lucchetti, Riccardo, 2026, "A rotated Dynamic Factor Model for the yield curve: squeezing out information when it matters," FEEM Working Papers, Fondazione Eni Enrico Mattei (FEEM), number 388985, Jan, DOI: 10.22004/ag.econ.388985.
• 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.
• Cansu Çilingir Kara, 2026, "The Impact of R&D Intensity and Financial Slack on Company Performance: An Analysis of Companies with the Highest R&D Expenditure in Türkiye," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 10, issue 4, pages 1366-1381, DOI: 10.30784/epfad.1666691.
• Chiara Casoli & Riccardo Lucchetti, 2026, "A rotated Dynamic Factor Model for the yield curve: squeezing out information when it matters," Working Papers, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali, number 503, Jan.
• Ахмет Алишер // Alisher Akhmet, 2026, "Прогнозирование ВВП Казахстана на основе динамической факторной модели с регуляризацией // Forecasting Kazakhstan’s GDP Based on a Dynamic Factor Model with Regularization," Working Papers, National Bank of Kazakhstan, number #2026-1.
• Niko Hauzenberger Massimiliano Marcellino Michael Pfarrhofer Anna Stelzer, 2026, "Direct Gaussian Process Predictive Regressions with Mixed Frequency Data," BAFFI CAREFIN Working Papers, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy, number 26265.
• Dalibor Stevanovic, 2026, "Who Saw It Coming? Historical Experienceand the 2021 Inflation Forecast Failure," Working Papers, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, number 26-02, Apr.
• 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.
• Filippo Natoli & Sharath Sonti, 2026, "Overconfident forecasters and the impact of inflation information: evidence from a randomized survey experiment," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1532, Apr.
• Rocío Clara A. Mora-Quiñones & Antonio José Orozco-Gallo & Dora Alicia Mora-Pérez, 2026, "Sentiment and Uncertainty Indices from economic news in Colombia," Borradores de Economia, Banco de la Republica de Colombia, number 1340, Jan, DOI: 10.32468/be.1340.
• Renato Vassallo & Margherita Philipp & Christopher Rauh & Hannes Mueller & Laura Mayoral, 2026, "Semantic Similarity Measures in Newspaper Text for Detecting and Predicting Disruptive Institutional Events," Working Papers, Barcelona School of Economics, number 1555, Jan.
• Ramón Talvi Robledo & Christopher Rauh & Ben Seimon & Hannes Mueller & Laura Mayoral, 2026, "Forecasting Forced Displacement Flows Using Machine Learning with Text Data," Working Papers, Barcelona School of Economics, number 1573, Apr.
• Doan Gia Bao Ngoc & Luu Minh Quan & Truong Thi Thanh Ha & Nguyen Duc Minh Tan & Phan Thi Minh Huyen & Tran Duy Thanh, 2026, "Building a new hybrid machine learning model for improvement insurance cross-sell prediction," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ECONOMICS AND BUSINESS ADMINISTRATION, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, volume 16, issue 1, pages 93-111, DOI: 10.46223/HCMCOUJS.econ.en.16.1.4306.
• Danila Ovechkin, 2026, "Estimation and forecasting with a Nonlinear Phillips Curve based on heterogeneous sensitivity between economic activity and CPI components," Bank of Russia Working Paper Series, Bank of Russia, number wps161, Jan.
• Alexander Eliseev & Sergei Seleznev, 2026, "Fake Date Tests: Can We Trust In-sample Accuracy of LLMs in Macroeconomic Forecasting?," Bank of Russia Working Paper Series, Bank of Russia, number wps167, Mar.
• Oguzhan Cepni & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2026, "Political Geography and Stock Market Volatility: The Role of Political Alignment Across Sentiment Regimes," Scottish Journal of Political Economy, Scottish Economic Society, volume 73, issue 1, February, DOI: 10.1111/sjpe.70028.
• Tom Doan, 2026, "MATHESONSTAVREVEL2013: RATS programs to replicate Matheson-Stavrev(2013) non-linear state-space model," Statistical Software Components, Boston College Department of Economics, number RTJ00054, revised .
• Panagiotis Delis & Georgios Kontogeorgos, 2026, "Outlier-robust evaluation of fixed-event macroeconomic survey expectations," Working Papers, Bank of Greece, number 356, Jan, DOI: 10.52903/wp2026356.
• Dimitris Louzis, 2026, "Trend inflation and inflation expectations in high dimensional vector autoregressions," Working Papers, Bank of Greece, number 360, Mar, DOI: 10.52903/wp2026360.
• Nonejad Nima, 2026, "Out-of-Sample Density Prediction of the End-of-Month Price of Crude Oil and the U.S. Economic Policy Uncertainty Index," Journal of Time Series Econometrics, De Gruyter, volume 18, issue 1, pages 1-47, DOI: 10.1515/jtse-2025-0007.
• Mayoral, L. & Mueller, H. & Philipp, M. & Rauh, C. & Vassallo, R., 2026, "Semantic Similarity Measures in Newspaper Text for Detecting and Predicting Disruptive Institutional Events," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2609, Jan.
• Congressional Budget Office, 2026, "The Accuracy of CBO's Budget Projections for Fiscal Year 2025," Reports, Congressional Budget Office, number 61916, Jan.
• Uluc Aysun & Melanie Guldi, 2026, "Revisiting exchange rate predictability: Can machine learning with theoretical filtering outperform canonical models?," Working Papers, University of Central Florida, Department of Economics, number 2026-01, Jan.
• Dalibor Stevanovic, 2026, "Who Saw It Coming? Historical Experience and the 2021 Inflation Forecast Failure," CIRANO Working Papers, CIRANO, number 2026s-06, Apr.
• Filip Blaha & Jan Botka & Josef Sveda & Ales Michl, 2026, "AI-Based Forecasting of Czech Inflation: Quantile Regression Forests with Dynamic Weights," Working Papers, Czech National Bank, Research and Statistics Department, number 2026/09, Apr.
• Guo, Hongfei & Marín Díazaraque, Juan Miguel & Veiga, Helena, 2026, "Target-Driven Bayesian Stacking of Realized and Implied Volatility Forecasts," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de Estadística, number 49851, Apr.
• Eraslan, Sercan & Fabbri, Andrea & Saiz, Lorena, 2026, "Short-term forecasting of euro area economic activity in an uncertain world," Economic Bulletin Articles, European Central Bank, volume 8.
• Montes-Galdón, Carlos & Paredes, Joan & Wolf, Elias, 2026, "A robust approach to tilting: parametric relative entropy," Working Paper Series, European Central Bank, number 3200, Mar.
• Jiang, Min & Shi, Jichuan & Zheng, Yukai & Zhou, Wei, 2026, "The role of alternative data in micro-enterprises’ credit risk assessment in China — Empirical evidence based on machine learning," Journal of Behavioral and Experimental Finance, Elsevier, volume 49, issue C, DOI: 10.1016/j.jbef.2026.101154.
• Mahler, Daniel Gerszon & Schoch, Marta & Lakner, Christoph & Nguyen, Minh Cong, 2026, "A parsimonious approach to predicting income distributions," Journal of Development Economics, Elsevier, volume 180, issue C, DOI: 10.1016/j.jdeveco.2025.103695.
• Yang, Zheng & Wu, Haocheng & Kuo, Biing-Shen & Ma, Yongkai, 2026, "Forecasting Chinese equity premium: A dimensionality reduction combination approach," Journal of Economic Dynamics and Control, Elsevier, volume 186, issue C, DOI: 10.1016/j.jedc.2026.105308.
• Chironna, Gianpiero & Orlando, Giuseppe, 2026, "Predicting bank defaults in Italy: A comparative analysis of conventional and machine learning approaches," Economic Analysis and Policy, Elsevier, volume 89, issue C, pages 788-833, DOI: 10.1016/j.eap.2025.12.002.
• De Rosa, Mauricio & Vilá, Joan, 2026, "Taxing the rich in Latin America: Effects of a wealth tax on revenue and distribution," Economic Analysis and Policy, Elsevier, volume 90, issue C, pages 1440-1466, DOI: 10.1016/j.eap.2026.02.016.
• Koppolu, Sarath Chandra & Hoeschle, Lisa & Maruejols, Lucie, 2026, "Potential for energy poverty reduction by error decomposition with machine learning," Economic Analysis and Policy, Elsevier, volume 90, issue C, pages 417-435, DOI: 10.1016/j.eap.2026.01.032.
• Shah, Sayar Ahmad & Garg, Bhavesh, 2026, "Dynamics of exchange rate pass-through: The role of pricing strategies and economic shocks," Economic Modelling, Elsevier, volume 154, issue C, DOI: 10.1016/j.econmod.2025.107353.
• Guerzoni, Marco & Riso, Luigi & Zoia, M. Grazia, 2026, "Extreme weather events as the main driver of electricity price volatility in Italy: A GARCH-MIDAS approach with machine learning-based variable selection," The North American Journal of Economics and Finance, Elsevier, volume 81, issue C, DOI: 10.1016/j.najef.2025.102512.
• Aslam, Adnan & Brahmana, Rayenda Khresna, 2026, "Systemic spillovers in high-growth private market sectors: determinants and portfolio implications," The North American Journal of Economics and Finance, Elsevier, volume 82, issue C, DOI: 10.1016/j.najef.2025.102579.
• Caldeira, João F. & Cordeiro, Werley C., 2026, "Decomposing nominal and real yield curves and inflation forecasting: Evidence from Brazil," Economics Letters, Elsevier, volume 258, issue C, DOI: 10.1016/j.econlet.2025.112712.
• 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.
• Quinlan, Rory & Pinheiro, Roberto, 2026, "BLS payroll revisions: Forecasting recessions," Economics Letters, Elsevier, volume 261, issue C, DOI: 10.1016/j.econlet.2026.112859.
• Morita, Hiroshi, 2026, "Forecasting GDP growth with stock returns: Time-series or cross-sectional information?," Economics Letters, Elsevier, volume 263, issue C, DOI: 10.1016/j.econlet.2026.112946.
• Patton, Andrew J. & Zhang, Haozhe, 2026, "Bespoke realized volatility: Tailored measures of risk for volatility prediction," Journal of Econometrics, Elsevier, volume 254, issue PA, DOI: 10.1016/j.jeconom.2025.106122.
• Garcia, Pablo & Jacquinot, Pascal & Lenarčič, Črt & Mavromatis, Kostas & Papadopoulou, Niki & Silgado-Gómez, Edgar, 2026, "Green transition in the euro area: Domestic and global factors," European Economic Review, Elsevier, volume 182, issue C, DOI: 10.1016/j.euroecorev.2025.105206.
• 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.
• Cheng, Mingmian, 2026, "Sparse heterogeneous auto-regressive model for volatility forecasting," Journal of Empirical Finance, Elsevier, volume 87, issue C, DOI: 10.1016/j.jempfin.2026.101708.
• Li, Gang & Wang, Shuqi & Wei, K.C. John, 2026, "What drives retail investors’ overconfidence? The role of information acquisition costs," Journal of Empirical Finance, Elsevier, volume 87, issue C, DOI: 10.1016/j.jempfin.2026.101709.
• Jiao, Lei & Zhou, Qing (Clara), 2026, "Economic conditions and portfolio tail risk: A probability-weighted simulation approach," Journal of Empirical Finance, Elsevier, volume 87, issue C, DOI: 10.1016/j.jempfin.2026.101715.
• Nam, Kyungsik & Seo, Won-Ki, 2026, "Nonlinear temperature sensitivity of residential electricity demand: Evidence from a distributional regression approach," Energy Economics, Elsevier, volume 153, issue C, DOI: 10.1016/j.eneco.2025.109076.
• Ghelasi, Paul & Ziel, Florian, 2026, "A data-driven merit order: Learning a fundamental electricity price model," Energy Economics, Elsevier, volume 154, issue C, DOI: 10.1016/j.eneco.2025.109114.
• Garratt, Anthony & Petrella, Ivan & Zhang, Yunyi, 2026, "The predictive content of U.S. Energy Information Administration oil market forecasts," Energy Economics, Elsevier, volume 156, issue C, DOI: 10.1016/j.eneco.2026.109214.
• 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.
• Yu, Deshui & Tang, Jiachen & Zhou, Mingtao, 2026, "Trade policy uncertainty and stock returns: A tale of two periods," International Review of Financial Analysis, Elsevier, volume 109, issue C, DOI: 10.1016/j.irfa.2025.104789.
• Klinkowska, Olga & Zadorozhna, Olha, 2026, "The yield curve strikes back: New evidence of its predictive power for economic activity and inflation," International Review of Financial Analysis, Elsevier, volume 113, issue C, DOI: 10.1016/j.irfa.2026.105128.
• 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.
• Papíková, Lenka & Papík, Mário, 2026, "Similarity failure proximity score: A network-based metric for bankruptcy prediction," Finance Research Letters, Elsevier, volume 94, issue C, DOI: 10.1016/j.frl.2026.109631.
• Hibbeln, Martin T. & Kopp, Raphael M. & Urban, Noah, 2026, "Predictive multiplicity, procedural multiplicity, and heterogeneous machine learning ensembles in recovery rate forecasting," Journal of Financial Stability, Elsevier, volume 83, issue C, DOI: 10.1016/j.jfs.2026.101510.
• Mei, Ziwei & Sheng, Liugang & Shi, Zhentao, 2026, "Nickell bias in panel local projection: Financial crises are worse than you think," Journal of International Economics, Elsevier, volume 160, issue C, DOI: 10.1016/j.jinteco.2025.104210.
• Benmoussa, Amor Aniss & Ellwanger, Reinhard & Snudden, Stephen, 2026, "Carpe diem: Can daily oil prices improve model-based forecasts of the real price of crude oil?," International Journal of Forecasting, Elsevier, volume 42, issue 1, pages 281-295, DOI: 10.1016/j.ijforecast.2025.02.009.
• Mitchell, James & Shiroff, Taylor & Braitsch, Hana, 2026, "Practice makes perfect: Learning effects with household point and density forecasts of inflation," International Journal of Forecasting, Elsevier, volume 42, issue 2, pages 315-329, DOI: 10.1016/j.ijforecast.2025.06.002.
• Bürgi, Constantin & Ortiz, Julio L., 2026, "Overreaction through anchoring," International Journal of Forecasting, Elsevier, volume 42, issue 2, pages 512-526, DOI: 10.1016/j.ijforecast.2025.08.002.
• Alsayed, Ahmed R.M. & Cameletti, Michela, 2026, "Air demand forecasting for passengers and freight in Italy: A comparison of two statistical models," Journal of Air Transport Management, Elsevier, volume 134, issue C, DOI: 10.1016/j.jairtraman.2026.102975.
• Bolivar, Osmar, 2026, "High-frequency inflation forecasting: A two-step machine learning methodology," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 7, issue 1, DOI: 10.1016/j.latcb.2025.100172.
• Gemmi, Luca & Valchev, Rosen, 2026, "Biased surveys," Journal of Monetary Economics, Elsevier, volume 157, issue C, DOI: 10.1016/j.jmoneco.2025.103868.
• Hubrich, Kirstin & Schüler, Yves & Waggoner, Daniel, 2026, "Financial shocks and leverage of financial institutions: When do they matter?," Journal of Monetary Economics, Elsevier, volume 158, issue C, DOI: 10.1016/j.jmoneco.2026.103900.
• Bonato, Matteo & Demirer, Riza & Gupta, Rangan & Olaniran, Abeeb, 2026, "Does mining activity drive crash risks in bitcoin?," The Quarterly Review of Economics and Finance, Elsevier, volume 105, issue C, DOI: 10.1016/j.qref.2025.102082.
• Salisu, Afees A. & Gupta, Rangan & Cepni, Oguzhan, 2026, "Housing market variables and predictability of state-level stock market volatility of the United States: Fundamentals versus sentiments in a mixed-frequency framework," The Quarterly Review of Economics and Finance, Elsevier, volume 105, issue C, DOI: 10.1016/j.qref.2025.102087.
• 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.
• Bargman, Daniil, 2026, "Latent variable modelling by supervised diffusion," International Review of Economics & Finance, Elsevier, volume 106, issue C, DOI: 10.1016/j.iref.2026.104972.
• Fasanya, Ismail O. & Oyewole, Oluwatomisin J. & Saleh Al-Faryan, Mamdouh Abdulaziz, 2026, "The inflation-energy nexus in OPEC: A nonlinear forecasting perspective," International Review of Economics & Finance, Elsevier, volume 107, issue C, DOI: 10.1016/j.iref.2026.105096.
• Migliavacca, Milena & Anwer, Zaheer & Fandella, Paola, 2026, "Geopolitical risk and stock market volatility: The case of US weapon and non-weapon firms," Research in International Business and Finance, Elsevier, volume 81, issue C, DOI: 10.1016/j.ribaf.2025.103195.
• Mei, Dexiang & Li, Xiaotao, 2026, "Forecasting of Chinese stock price using a hybrid neural network model," Research in International Business and Finance, Elsevier, volume 82, issue C, DOI: 10.1016/j.ribaf.2025.103232.
• Hamida, Amal Ben & de Peretti, Christian & Belkacem, Lotfi, 2026, "Benford’s law and intraday microstructure anomalies: Forecasting market movements with high-frequency data," Research in International Business and Finance, Elsevier, volume 84, issue C, DOI: 10.1016/j.ribaf.2026.103302.
• Sun, Hao & Zhu, Xiaoqian & Li, Jianping, 2026, "Revealing corporate accounting fraud: From the perspective of individual investors," Research in International Business and Finance, Elsevier, volume 86, issue C, DOI: 10.1016/j.ribaf.2026.103385.
• Fontana, Stefania & Guccio, Calogero & Pignataro, Giacomo & Vidoli, Francesco, 2026, "Better politicians, fewer deaths? Local resilience in overcoming the pandemic crisis in Italy," Social Science & Medicine, Elsevier, volume 398, issue C, DOI: 10.1016/j.socscimed.2026.119198.
• Yilin Xiao & Jamie L. Cross, 2026, "Regularized Random Subspace Regressions," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-13, Feb.
• Anna Gembalska-Kwiecien, 2026, "Attempted Development of a Methodology to Support Project Implementation Risk Management in a Manufacturing Enterprise," European Research Studies Journal, European Research Studies Journal, volume 0, issue 2, pages 57-66.
• Chiara Casoli & Riccardo Lucchetti, 2026, "A rotated Dynamic Factor Model for the yield curve: squeezing out information when it matters," Working Papers, Fondazione Eni Enrico Mattei, number 2026.03, Jan.
• Gary Koop & Stuart McIntyre & James Mitchell & Ping Wu, 2026, "Incorporating Micro Data into Macro Models Using Pseudo VARs," Working Papers, Federal Reserve Bank of Cleveland, number 26-04, Feb, DOI: 10.26509/frbc-wp-202604.
• Kevin J. Lansing & Adam Hale Shapiro, 2026, "Measuring Inflation Shock Momentum," Working Paper Series, Federal Reserve Bank of San Francisco, number 2026-10, Apr, DOI: 10.24148/wp2026-10.
• Elena Villalobos & Adolfo de Unánue T. & Fernanda Sobrino & David Aké & Stephany Cisneros & Jorge Lecona & Alejandra Matadamaz, 2026, "Toward Reducing Unproductive Container Moves: Predicting Service Requirements and Dwell Times," Working Paper Series of the School of Government and Public Transformation, School of Governement and Public Transformation, number 31, Apr.
• Dobrislav Dobrev & Pawel J. Szerszen, 2026, "Missing Data Substitution for Enhanced Robust Filtering and Forecasting in State-Space Models," Working Papers, The George Washington University, The Center for Economic Research, number 2026-004, Mar.
• Rahma Mzouri & Abdelkrim Kandrouch, 2026, "Business failure: a literature review
  [Défaillance des entreprises : revue de littérature]," Post-Print, HAL, number hal-05527901, Feb, DOI: 10.5281/zenodo.18614550.
• G Barone-Adesi & M Bonollo & V Damato & F Luce, 2026, "Risk Governance Through Long-Term Risk Modelling: An Enhanced Filtered Historical Simulation Approach for Financial Institutions," Working Papers, HAL, number hal-05487195, Jan.
• Drin, Svitlana & Zhuravlova, Anastasiia, 2026, "Real-Time Nowcasting of Kyiv’s Regional GRP Using Google Trends and Mixed-Frequency Data," Working Papers, Örebro University, School of Business, number 2026:1, Jan.
• Andrew B. Martinez & Alexander D. Schibuola & David Beckworth, 2026, "The Reliability of the Nominal GDP Expectations Gap," International Journal of Central Banking, International Journal of Central Banking, volume 22, issue 2, pages 525-557, April.
• Yusuke Oh & Mototsugu Shintani, 2026, "Forecasting Recessions Using Machine Learning on Text Data and Mixed-Frequency Predictors," IMES Discussion Paper Series, Institute for Monetary and Economic Studies, Bank of Japan, number 26-E-07, Mar.
• 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.
• Giménez-Nadal, José Ignacio & Molina, José Alberto & Velilla, Jorge, 2026, "Who Shirks at Work? An Application of Machine Learning to Time Use Data," IZA Discussion Papers, IZA Network @ LISER, number 18432, Mar.

2025

• Luo, Jiawen & Chen, Zhenbiao & Cheng, Mingmian, 2025, "Forecasting realized betas using predictors indicating structural breaks and asymmetric risk effects," Journal of Empirical Finance, Elsevier, volume 80, issue C, DOI: 10.1016/j.jempfin.2024.101575.
• Luo, Jiawen & Cepni, Oguzhan & Demirer, Riza & Gupta, Rangan, 2025, "Forecasting multivariate volatilities with exogenous predictors: An application to industry diversification strategies," Journal of Empirical Finance, Elsevier, volume 81, issue C, DOI: 10.1016/j.jempfin.2025.101595.
• Zhang, Tao & Tang, Ke & Liu, Taoxiong & Jiang, Tingfeng, 2025, "High frequency online inflation and term structure of interest rates: Evidence from China," Journal of Empirical Finance, Elsevier, volume 83, issue C, DOI: 10.1016/j.jempfin.2025.101626.
• Hsu, Po-Hsuan & Taylor, Mark P. & Wang, Zigan & Li, Yan, 2025, "On the profitability of influential carry-trade strategies: Data-snooping bias and post-publication performance," Journal of Empirical Finance, Elsevier, volume 83, issue C, DOI: 10.1016/j.jempfin.2025.101640.
• Yuan, Ying & Qu, Yong & Wang, Tianyang, 2025, "Predicting risk premiums: A constraint-based model," Journal of Empirical Finance, Elsevier, volume 83, issue C, DOI: 10.1016/j.jempfin.2025.101647.
• Zhang, Han & Xiong, Xiong & Guo, Bin, 2025, "The stock return predictability of treasury bond yield in China," Journal of Empirical Finance, Elsevier, volume 84, issue C, DOI: 10.1016/j.jempfin.2025.101654.
• Agakishiev, Ilyas & Härdle, Wolfgang Karl & Kopa, Milos & Kozmik, Karel & Petukhina, Alla, 2025, "Multivariate probabilistic forecasting of electricity prices with trading applications," Energy Economics, Elsevier, volume 141, issue C, DOI: 10.1016/j.eneco.2024.108008.
• Ellwanger, Reinhard, 2025, "The tail risk premium in the oil market," Energy Economics, Elsevier, volume 141, issue C, DOI: 10.1016/j.eneco.2024.108041.
• Wang, Zhengzhong & Wei, Yunjie & Wang, Shouyang, 2025, "Forecasting the carbon price of China's national carbon market: A novel dynamic interval-valued framework," Energy Economics, Elsevier, volume 141, issue C, DOI: 10.1016/j.eneco.2024.108107.
• Motegi, Kaiji & Hamori, Shigeyuki, 2025, "Conditional threshold effects of stock market volatility on crude oil market volatility," Energy Economics, Elsevier, volume 143, issue C, DOI: 10.1016/j.eneco.2025.108189.
• Forgetta, Anthony & Godin, Frédéric & Augustyniak, Maciej, 2025, "Distributional forecasting of electricity DART spreads with a covariate-dependent mixture model," Energy Economics, Elsevier, volume 144, issue C, DOI: 10.1016/j.eneco.2025.108332.
• Castro, Tomas del Barrio & Escribano, Alvaro & Sibbertsen, Philipp, 2025, "Modeling and forecasting the long memory of Cyclical Trends in paleoclimate data," Energy Economics, Elsevier, volume 147, issue C, DOI: 10.1016/j.eneco.2025.108520.
• Wu, Bangzheng, 2025, "The global supply pressure and oil supply–demand shocks: A time-scale and quantile analysis," Energy Economics, Elsevier, volume 147, issue C, DOI: 10.1016/j.eneco.2025.108555.
• Delis, Panagiotis & Degiannakis, Stavros & Filis, George, 2025, "Navigating crude oil volatility forecasts: Assessing the contribution of geopolitical risk," Energy Economics, Elsevier, volume 148, issue C, DOI: 10.1016/j.eneco.2025.108594.
• Serafin, Tomasz & Weron, Rafał, 2025, "Loss functions in regression models: Impact on profits and risk in day-ahead electricity trading," Energy Economics, Elsevier, volume 148, issue C, DOI: 10.1016/j.eneco.2025.108596.
• Candila, Vincenzo & Petrella, Lea & Andreani, Mila, 2025, "Mixed-frequency Quantile Regression Forests for Value-at-Risk forecasting," Energy Economics, Elsevier, volume 149, issue C, DOI: 10.1016/j.eneco.2025.108706.
• Koechlin, Guillaume & Bovera, Filippo & Secchi, Piercesare, 2025, "Strategic bidding in pay-as-bid power reserve markets: A machine learning approach," Energy Economics, Elsevier, volume 150, issue C, DOI: 10.1016/j.eneco.2025.108780.
• Das, Debojyoti & Saurav, Sumit & Dutta, Anupam, 2025, "Modelling for insight: Does oil price uncertainty have directional predictability for travel and leisure firms?," Energy Economics, Elsevier, volume 151, issue C, DOI: 10.1016/j.eneco.2025.108887.
• Hanus, Luboš & Baruník, Jozef, 2025, "Learning the probability distributions of day-ahead electricity prices," Energy Economics, Elsevier, volume 152, issue C, DOI: 10.1016/j.eneco.2025.108988.
• Wen, Danyan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2025, "Forecasting gasoline prices using oil prices: New evidence based on the rocket and feather hypothesis," Energy, Elsevier, volume 335, issue C, DOI: 10.1016/j.energy.2025.138115.
• Yan, Lili & Kellard, Neil M. & Lambercy, Lyudmyla, 2025, "Multivariate range-based EGARCH models," International Review of Financial Analysis, Elsevier, volume 100, issue C, DOI: 10.1016/j.irfa.2025.103983.
• Chen, Sihan & Ming, Lei & Yang, Haoxi & Yang, Shenggang, 2025, "Iterated Dynamic Model Averaging and application to inflation forecasting," International Review of Financial Analysis, Elsevier, volume 102, issue C, DOI: 10.1016/j.irfa.2025.104095.
• Liu, Yanchen & Yi, Siyu & Li, Sitong & Chen, Gengxuan, 2025, "Asymmetric impacts of energy market-related uncertainty on clean energy stock volatility: The role of extreme shocks," International Review of Financial Analysis, Elsevier, volume 103, issue C, DOI: 10.1016/j.irfa.2025.104206.
• Wang, Jiqian & Chen, Chuang & Dai, Xingyu, 2025, "News topic attention and crude oil price predictability," International Review of Financial Analysis, Elsevier, volume 108, issue PA, DOI: 10.1016/j.irfa.2025.104696.
• Zhao, Dongshuai & Wang, Zhongli & Schweizer-Gamborino, Florian & Sornette, Didier, 2025, "Polytope Fraud Theory," International Review of Financial Analysis, Elsevier, volume 97, issue C, DOI: 10.1016/j.irfa.2024.103734.
• Zhang, Yaojie & He, Mengxi & Wang, Yudong & Wen, Danyan, 2025, "Model specification for volatility forecasting benchmark," International Review of Financial Analysis, Elsevier, volume 97, issue C, DOI: 10.1016/j.irfa.2024.103850.
• Hu, Nan & Yin, Xuebao & Yao, Yuhang, 2025, "A novel HAR-type realized volatility forecasting model using graph neural network," International Review of Financial Analysis, Elsevier, volume 98, issue C, DOI: 10.1016/j.irfa.2024.103881.
• Blazsek, Szabolcs & Kong, Dejun & Shadoff, Samantha R., 2025, "Within-regime volatility dynamics for observable- and Markov-switching score-driven models," Finance Research Letters, Elsevier, volume 73, issue C, DOI: 10.1016/j.frl.2024.106631.
• Li, Sitong & Chen, Huangen & Chen, Gengxuan, 2025, "The US-China tension and fossil fuel energy price volatility relationship," Finance Research Letters, Elsevier, volume 74, issue C, DOI: 10.1016/j.frl.2024.106707.
• Hu, Wendi & Shao, Chujian & Zhang, Wenyu, 2025, "Predicting U.S. bank failures and stress testing with machine learning algorithms," Finance Research Letters, Elsevier, volume 75, issue C, DOI: 10.1016/j.frl.2025.106802.
• Liu, Zhenya & You, Rongyu & Zhan, Yaosong, 2025, "Modeling GDP with a continuous-time finance approach," Finance Research Letters, Elsevier, volume 76, issue C, DOI: 10.1016/j.frl.2025.106971.
• Ellwanger, Reinhard & Snudden, Stephen, 2025, "Putting VAR forecasts of the real price of crude oil to the test," Finance Research Letters, Elsevier, volume 77, issue C, DOI: 10.1016/j.frl.2025.106940.
• Polat, Onur & Somani, Dhanashree & Gupta, Rangan & Karmakar, Sayar, 2025, "Shortages and machine-learning forecasting of oil returns volatility: 1900–2024," Finance Research Letters, Elsevier, volume 79, issue C, DOI: 10.1016/j.frl.2025.107334.
• Li, Chenxing & Yang, Qiao, 2025, "An infinite hidden Markov model with GARCH for short-term interest rates," Finance Research Letters, Elsevier, volume 80, issue C, DOI: 10.1016/j.frl.2025.107294.
• Wu, Bangzheng, 2025, "Sino-American relations and gold market volatility," Finance Research Letters, Elsevier, volume 80, issue C, DOI: 10.1016/j.frl.2025.107379.
• Awartani, Basel & Maghyereh, Aktham, 2025, "The value of cross market volatility in improving the forecast accuracy of risk in the gold, the dollar and the oil futures markets," Finance Research Letters, Elsevier, volume 83, issue C, DOI: 10.1016/j.frl.2025.107668.
• Lu, Zhichao & Xu, Yuhong & Zhang, Yue & Zhao, Xinyao, 2025, "Is it difficult to predict the price movements of high-volatility assets," Finance Research Letters, Elsevier, volume 85, issue PB, DOI: 10.1016/j.frl.2025.107980.
• Koutmos, Dimitrios & Gunay, Samet & Payne, James E., 2025, "Market expectations and the holding behaviors of bitcoin whales, dolphins, and minnows," Finance Research Letters, Elsevier, volume 86, issue PE, DOI: 10.1016/j.frl.2025.108590.
• Somani, Dhanashree & Gupta, Rangan & Karmakar, Sayar & Plakandaras, Vasilios, 2025, "Supply bottlenecks and machine learning forecasting of international stock market volatility," Finance Research Letters, Elsevier, volume 86, issue PG, DOI: 10.1016/j.frl.2025.108931.
• Oliveira, Lucas M. & Alencar, Airlane P., 2025, "When timing matters: Regime-dependent delays in exchange rate fundamentals," Finance Research Letters, Elsevier, volume 86, issue PG, DOI: 10.1016/j.frl.2025.108941.
• Liu, Dan, 2025, "Seeing is believing: Forecasting oil market returns with artificial intelligence-powered visual climate change perception," Global Finance Journal, Elsevier, volume 68, issue C, DOI: 10.1016/j.gfj.2025.101174.
• Yuan, Ying & Qu, Yong & Qiao, Sijia, 2025, "Equity premium prediction: A constraint-based predictor decomposition approach," Global Finance Journal, Elsevier, volume 68, issue C, DOI: 10.1016/j.gfj.2025.101199.
• Levich, Sergej & Knust, Lucas, 2025, "Discriminative meets generative: Automated information retrieval from unstructured corporate documents via (large) language models," International Journal of Accounting Information Systems, Elsevier, volume 56, issue C, DOI: 10.1016/j.accinf.2025.100750.
• Barone, Guglielmo & Letta, Marco, 2025, "Unlevel playing field? Machine learning meets state aid regulation," International Journal of Industrial Organization, Elsevier, volume 101, issue C, DOI: 10.1016/j.ijindorg.2025.103175.
• Baumgärtner, Martin & Zahner, Johannes, 2025, "Whatever it takes to understand a central banker — Embedding their words using neural networks," Journal of International Economics, Elsevier, volume 157, issue C, DOI: 10.1016/j.jinteco.2025.104101.
• Chen, Ze & Li, Hong & Mao, Yu & Zhou, Kenneth Q., 2025, "Learning from COVID-19: A catastrophe mortality bond solution in the post-pandemic era," Insurance: Mathematics and Economics, Elsevier, volume 123, issue C, DOI: 10.1016/j.insmatheco.2025.103113.
• Koike, Takaaki & Chen, Cathy W.S. & Lin, Edward M.H., 2025, "Forecasting and backtesting gradient allocations of expected shortfall," Insurance: Mathematics and Economics, Elsevier, volume 124, issue C, DOI: 10.1016/j.insmatheco.2025.103130.
• Ahn, Jae Youn & Jeong, Himchan & Lu, Yang & Wüthrich, Mario V., 2025, "An observation-driven state-space count model for experience rating," Insurance: Mathematics and Economics, Elsevier, volume 125, issue C, DOI: 10.1016/j.insmatheco.2025.103149.
• Caporin, Massimiliano & Caraiani, Petre & Cepni, Oguzhan & Gupta, Rangan, 2025, "Predicting the conditional distribution of US stock market systemic Stress: The role of climate risks," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 101, issue C, DOI: 10.1016/j.intfin.2025.102156.
• M’bakob, Gilles Brice & Mandeng ma Ntamack, Jules & Mfouapon, Georges Kriyoss, 2025, "Anticipated psychological spreads: Cryptocurrencies’ hidden short-term monitors and implications for price forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 104, issue C, DOI: 10.1016/j.intfin.2025.102224.
• Coroneo, Laura & Iacone, Fabrizio, 2025, "Testing for equal predictive accuracy with strong dependence," International Journal of Forecasting, Elsevier, volume 41, issue 3, pages 1073-1092, DOI: 10.1016/j.ijforecast.2024.11.003.
• Sokol, Andrej, 2025, "Fan charts 2.0: Flexible forecast distributions with expert judgement," International Journal of Forecasting, Elsevier, volume 41, issue 3, pages 1148-1164, DOI: 10.1016/j.ijforecast.2024.11.009.
• Samartzis, Panagiotis, 2025, "Predicting the relative performance among financial assets: A comparative analysis of different approaches," International Journal of Forecasting, Elsevier, volume 41, issue 4, pages 1428-1449, DOI: 10.1016/j.ijforecast.2024.12.008.