Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2026

• 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.
• Jan Willem Van den End, 2026, "Does uncertainty raise the probability of a recession?," Empirical Economics, Springer, volume 70, issue 6, pages 1-32, June, DOI: 10.1007/s00181-026-02914-5.
• Seho Park & Kahyun Lee, 2026, "Predicting brand share after LOE in chronic disease market using machine learning," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), volume 27, issue 3, pages 699-714, April, DOI: 10.1007/s10198-025-01845-9.
• 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.
• 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.
• Bibiana Lanzilotta & Gabriela Mordecki & Pablo Tapie & Joaquín Torres Pérez, 2026, "Structural Breaks in Uncertainty and the Business Cycle in a Small and Open Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), volume 22, issue 1, pages 1-28, April, DOI: 10.1007/s41549-025-00117-y.
• 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.
• David J. C. Dinis, 2026, "Low Default Portfolios – From the Usefulness of Pluriannual Data to the Inconsistency of Multi period Estimation," Journal of Applied Finance & Banking, SCIENPRESS Ltd, volume 16, issue 4, pages 1-2.
• 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.
• Sarthak S. Behera & Hyeongwoo Kim & Soohyon Kim, 2026, "Asymmetric Roles of Macroeconomic Variables in the Real Exchange Rate: Insights from U.S.-Korea Data," International Economic Journal, Taylor & Francis Journals, volume 40, issue 1, pages 84-113, January, DOI: 10.1080/10168737.2026.2613859.
• 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.
• Boysen-Hogrefe Jens, 2026, "Leitartikel: Fiskalregeln und die Grenzen der Politikberatung," Wirtschaftsdienst, Sciendo, volume 106, issue 4, pages 230-231, DOI: 10.2478/wd-2026-0056.
• 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.
• Kostiantyn Okhrimenko, 2026, "Painting Price: A Machine Learning Approach to Art Valuation. Proof of Concept and Market Structure Diagnosis," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2026-18.
• 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.
• Jabeur Salhi & Ichrak Dridi & Oussama Gafrej, 2026, "Unlocking success in tech reward crowdfunding: A hybrid probit-machine learning approach with SHAP-driven feature analysis," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., volume 13, issue 01, pages 1-43, March, DOI: 10.1142/S2424786326500027.
• Ichrak Dridi & Mohamed Malek Belhoula, 2026, "The moderating role of inflation targeting in stock market volatility drivers: Machine learning insights into macro-financial channels," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., volume 13, issue 02, pages 1-36, June, DOI: 10.1142/S2424786326500180.
• Anshul Agrawal & Sanjeev Kadam & Mohd Afjal, 2026, "Evaluating Predictive Robustness of Machine Learning Models During Black Swan Crises: Insights from Bitcoin Price Forecasting," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., volume 17, issue 02, pages 1-22, June, DOI: 10.1142/S1793993325500267.
• 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.
• Gondauri, Davit & Batiashvili, Mikheil, 2026, "Agentic Capital as a Productive Asset in the Agentic Economy: A Panel Econometric Analysis of Productivity Dynamics," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 341306.
• 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.
• Knüppel, Malte & Pavlova, Lora, 2026, "Survey design and professional forecasters: The case of uncertainty in the US SPF," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 26-017.
• 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.
• Mengxi He & Daxiang Jin & Yaojie Zhang, 2026, "The Role of Lead-lag Effect in Predicting Crude Oil Futures Volatility: Empirical Evidence from China," Computational Economics, Springer;Society for Computational Economics, volume 67, issue 6, pages 5115-5137, June, DOI: 10.1007/s10614-025-11041-9.
• Alok Arun & Sibanjan Mishra & Bibhuti Bhusan Mishra, 2026, "Cross impact of technology and finance on economic growth: evidence across globe," Economic Change and Restructuring, Springer, volume 59, issue 3, pages 1-35, June, DOI: 10.1007/s10644-026-10021-9.
• 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.
• Jiti Gao & Fei Liu & Bin Peng, 2026, "Inference for High-Dimensional Local Projection," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 1/26.
• Nam Ho-Nguyen & Hossein Alipour & Anastasios Panagiotelis & George Athanasopoulos, 2026, "Optimal Forecast Reconciliation for Quantiles," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 4/26.
• 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.
• Lin William Cong & Guanhao Feng & Jingyu He & Yuanzhi Wang, 2026, "Mosaics of Predictability," NBER Working Papers, National Bureau of Economic Research, Inc, number 35158, Apr.
• 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.
• 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.
• Gabriel Rodriguez & Fiorela Liza & Miguel Ataurima Arellano, 2026, "Forecasting Value at Risk and Expected Shortfall in Equity Markets of High-Income and Latin American Countries," Documentos de Trabajo / Working Papers, Departamento de Economía - Pontificia Universidad Católica del Perú, number 2026-554, DOI: 10.18800/2079-8474.0554.
• 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.
• Larsen, Harry, 2026, "A Markov Model of the Learning Curve," MPRA Paper, University Library of Munich, Germany, number 128435, Mar.
• 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.
• 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.
• Nugawela, N.P. Gayan, 2026, "THE YIELD EQUILIBRIUM PROTOCOL: Architecting Revenue Governance and NOI Protection," MPRA Paper, University Library of Munich, Germany, number 129202, 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.
• Talita Greyling & Rangan Gupta & Christian Pierdzioch, 2026, "Supply Bottlenecks and Sentiment in Europe: Some Evidence using Machine Learning," Working Papers, University of Pretoria, Department of Economics, number 202616, May.
• Onur Polat & Oguzhan Cepni & Riza Demirer & Rangan Gupta, 2026, "AI Revolution and Crash Risks in Technology Stocks," Working Papers, University of Pretoria, Department of Economics, number 202617, Jun.
• 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.
• Valdemar J. Undji & Johannes P.S. Sheefeni, 2026, "Determinants of Non-Performing Loans in Namibia," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, volume 79, issue 2, pages 199-244, May, DOI: 10.65644/EIIE.079.02.0199.
• Julián Ramajo & Alejandro Ricci-Risquete & Geoffrey J.D Hewings, 2026, "Dinámica espaciotemporal en el crecimiento económico regional: un modelo empírico para las comunidades autónomas españolas
  [Spatio-temporal dynamics of regional economic growth: An empirical model for the Spanish Autonomous Communities]," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, volume 65, issue 65, pages 143-160, June, DOI: 10.38191/iirr-jorr.24.049.
• Hwee Kwan Chow & Jordan Lee, 2026, "Projecting Inflation Tail Risks in a Small Open Economy: Some Evidence from Singapore," Economics and Statistics Working Papers, Singapore Management University, School of Economics, number 04-2026, Feb.
• 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.
• Magdalena Cornejo & Walter Sosa Escudero, 2026, "Machine Learning and Shrinkage in Dynamic Panel Forecasting," Working Papers, Universidad de San Andres, Departamento de Economia, number 183, May, revised May 2026.
• 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.
• 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.
• 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.
• 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, June.
• Zourkata Meriem & Damene Ouahiba, 2026, "The Use of the Z-Score Indicator to Measure Financial Soundness and Stability in Islamic Banks (The Case of Al Salam Bank Algeria during the Period 2015–2024)," Finance, Accounting and Business Analysis, Academic Publishing UNWE, volume 8, issue 1, pages 117-134, June.
• 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.
• Denis Kuster & Bojana Vukovic, 2026, "Enhancing Resilience of Small and Medium-Sized Enterprises in an Emerging Economy: Neural Network-Based Bankruptcy Prediction," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, volume 28, issue 72, pages 712-712, April.
• 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.
• Joshua S. Gans, 2026, "Optimal Use of Preferences in Artificial Intelligence Algorithms," Papers, arXiv.org, number 2601.18732, Jan.
• Hilde C. Bjornland & Nicolas Hardy & Dimitris Korobilis, 2026, "Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach," Papers, arXiv.org, number 2604.12927, Apr.
• Latif Zeynalli & Ramil Huseyn & Agil Asadov & Abdulrahim Dadashov, 2026, "Exploring The Nexus Between Emissions, Economic Growth, And Employment: Evidence From Azerbaijan," Economic Thought and Practice, Department of Economics and Business, University of Dubrovnik, volume 35, issue 1, pages 277-298, june, DOI: 10.17818/EMIP/2025/44.
• 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.
• 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.
• Luca Bacco & Tiziana Laureti & Juri Marcucci & Luigi Palumbo & Daniele Sasso & Luca Vollero, 2026, "Nowcasting the Italian consumer price index using online prices and machine learning," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 1026, Jun.
• Andrea Del Monaco & Luigi Longo & Juri Marcucci & Irene Tafani, 2026, "Reddit's 'pulse' on US inflation: forecasting with large language models," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 1028, Jun.
• 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.
• Hilde C. Bjørnland & Nicolás Hardy & Dimitris Korobilis, 2026, "Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach," Working Papers, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School, number No 03/2026, Apr.
• Nicolas Hardy & Dimitris Korobilis, 2026, "Generalized Bayesian Composite Quantile Regression with an Application to Equity Premium Forecasting," Working Papers, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School, number No 04/2026, Apr.
• 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.
• Dimitrios P. Louzis, 2026, "Trend inflation and inflation expectations in high dimensional vector autoregressions," Working Papers, Bank of Greece, number 360, Mar, DOI: 10.52903/wp2026360.
• Zacharias Bragoudakis & Alexandros Karakitsios & Evangelia Kasimati, 2026, "Short-term inflation projections: Τhe new BOG’STIP model," Working Papers, Bank of Greece, number 363, Jun, DOI: 10.52903/wp2026363.
• 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.
• Rouven Beiner & Bernd Süssmuth, 2026, "Monotonic Polynomial GARCH Models for Conditional Higher Moments," CESifo Working Paper Series, CESifo, number 12734.
• 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.
• Ghezzi, Fabrizio & Timmermann, Allan & Yang, Max, 2026, "Gauging Hourly Economic Activity in Your Neighborhood," CEPR Discussion Papers, Centre for Economic Policy Research, number 21098, Jan.
• Hauzenberger, Niko & Marcellino, Massimiliano & Pfarrhofer, Michael & Stelzer, Anna, 2026, "Direct Gaussian Process Predictive Regressions with Mixed Frequency Data," CEPR Discussion Papers, Centre for Economic Policy Research, number 21214, Feb.
• 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.
• Çiğdem LAZOĞLU & Uğur KARABEY, 2026, "Evaluating the impact of stochastic interest rates and COVID-19 on financial performance under IFRS 17," JODE - Journal of Demographic Economics, Cambridge University Press, volume 92, issue 2, pages 277-296, June, DOI: 10.1017/dem.2024.25.
• 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.
• Delgado-Téllez, Mar & Ceglar, Andrej & Spiteri, Sarah & Lebouteiller, Léonore & Vorderobermeier, Nicole, 2026, "Beat the heat, the role of heat waves and droughts in regional EU economies," Working Paper Series, European Central Bank, number 3248, Jun.
• 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.
• Labonne, Paul & Thorsrud, Leif Anders, 2026, "Risky news and credit market sentiment," Journal of Economic Dynamics and Control, Elsevier, volume 187, issue C, DOI: 10.1016/j.jedc.2026.105331.
• 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.
• Raj, Prakash & Selvaraju, N., 2026, "Bitcoin volatility modeling with realized measures and jump dynamics," Economic Modelling, Elsevier, volume 160, issue C, DOI: 10.1016/j.econmod.2026.107615.
• Zeng, Tao & Wang, Kaixin & Fan, Yanjing & Liu, Xiaobin, 2026, "Systemic default probability and return predictability: Evidence from China," Economic Modelling, Elsevier, volume 160, issue C, DOI: 10.1016/j.econmod.2026.107617.
• Prüser, Jan & Blagov, Boris, 2026, "Improving inference and forecasting in VAR models using cross-sectional information," Economic Modelling, Elsevier, volume 160, issue C, DOI: 10.1016/j.econmod.2026.107618.
• 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.
• Zevallos, Mauricio & Rubesam, Alexandre, 2026, "Finite-sample properties of the Campbell and Thompson out-of-sample R2," Economics Letters, Elsevier, volume 265, issue C, DOI: 10.1016/j.econlet.2026.113011.
• Guidolin, Massimo & Ionta, Serena, 2026, "Predicting commodity returns with climate variables: Statistical loss functions vs. economic value," Economics Letters, Elsevier, volume 265, issue C, DOI: 10.1016/j.econlet.2026.113028.
• 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.
• Lunsford, Kurt G. & West, Kenneth D., 2026, "An empirical evaluation of some long-horizon macroeconomic forecasts," Journal of Econometrics, Elsevier, volume 255, issue C, DOI: 10.1016/j.jeconom.2026.106221.
• Gao, Zhan & Lee, Ji Hyung & Mei, Ziwei & Shi, Zhentao, 2026, "LASSO inference for high dimensional predictive regressions," Journal of Econometrics, Elsevier, volume 255, issue C, DOI: 10.1016/j.jeconom.2026.106240.
• Menzel, Konrad, 2026, "Transfer estimates for causal effects across heterogeneous sites," Journal of Econometrics, Elsevier, volume 255, issue C, DOI: 10.1016/j.jeconom.2026.106250.
• Maddock, Luke & Nelson, Frank & Altringer, Levi & McKee, Sophie C., 2026, "Estimating the value of ecosystem services: A machine learning approach for Missouri wetlands," Ecosystem Services, Elsevier, volume 79, issue C, DOI: 10.1016/j.ecoser.2026.101849.
• 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.
• Li, Mengheng & Mendieta-Muñoz, Ivan, 2026, "Unpacking trend inflation: Evidence from a factor correlated unobserved components model of sticky and flexible prices," European Economic Review, Elsevier, volume 187, issue C, DOI: 10.1016/j.euroecorev.2026.105377.
• Bonato, Matteo & Gupta, Rangan & Pierdzioch, Christian, 2026, "Do shortages forecast aggregate and sectoral U.S. stock market realized variance? Evidence from a century of data," Journal of Empirical Finance, Elsevier, volume 86, issue C, DOI: 10.1016/j.jempfin.2026.101726.
• 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.
• Díaz-Díaz, Raimundo & Galiano, Aida & Martín-Álvarez, Juan Manuel & Barrientos-Marín, Jorge, 2026, "From policy to reality: Forecasting Spain's vehicle fleet trajectory toward 2030 climate targets," Energy Policy, Elsevier, volume 215, issue C, DOI: 10.1016/j.enpol.2026.115298.
• 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.
• Li, Jupeng & Hou, Weijie & Zhang, Zongxin, 2026, "A coupled autoregressive extreme-value model for dynamic tail risk with risk spirals," Finance Research Letters, Elsevier, volume 105, issue C, DOI: 10.1016/j.frl.2026.110187.
• 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.
• Chen, Qitong & Chen, Xingyi & Chen, Zhenrui, 2026, "Avoiding weak-factor selection in sPCA-based factor-augmented regression: An all subset-averaging perspective," Finance Research Letters, Elsevier, volume 98, issue C, DOI: 10.1016/j.frl.2026.109870.
• Polakow, Daniel Adam & Flint, Emlyn James & Turro, Isabella Cristina Josephine & van Rooyen, Joané, 2026, "Prediction reconditioned: Revisiting relevance," Finance Research Letters, Elsevier, volume 99, issue C, DOI: 10.1016/j.frl.2026.109854.
• Ghosh, Indranil & Alfaro-Cortés, Esteban & Gámez, Matías & García-Rubio, Noelia, 2026, "Are defense stocks sensitive to Reddit sentiments on Middle East conflicts? Insights from Google’s TabNet and Wavelet Quantile correlation," Finance Research Letters, Elsevier, volume 99, issue C, DOI: 10.1016/j.frl.2026.109887.
• Bie, Siyu & Feng, Guanhao & Guo, Naixin & He, Jingyu, 2026, "Can news predict firm bankruptcy?," Journal of Financial Markets, Elsevier, volume 79, issue C, DOI: 10.1016/j.finmar.2025.101002.
• Zhang, Qunzi, 2026, "Commodity sentiment in predicting index futures returns," Journal of Financial Markets, Elsevier, volume 79, issue C, DOI: 10.1016/j.finmar.2025.101025.
• 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.
• Ross, Landon J. & Horn, Jim & Pilanci, Mert & Luo, Kaihong & Zhou, Guofu, 2026, "Bottom up vs. top down: What does firm 10-K tell us?," Journal of Financial Markets, Elsevier, volume 79, issue C, DOI: 10.1016/j.finmar.2026.101070.
• 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.
• Li, Yan & Qian, Zongxin, 2026, "Systemic risk measures and macroeconomic shocks: An update of empirical evidence," Journal of Financial Stability, Elsevier, volume 84, issue C, DOI: 10.1016/j.jfs.2026.101520.
• Crawley, Andrew & Daigneault, Adam & Gendron, Jonathan, 2026, "Where the trees fall: Macroeconomic forecasts for forest-reliant states," Forest Policy and Economics, Elsevier, volume 186, issue C, DOI: 10.1016/j.forpol.2026.103739.
• 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.
• Avanzi, Benjamin & Dong, Eric T. & Laub, Patrick J. & Wong, Bernard, 2026, "Distributional refinement network: Distributional forecasting via deep learning," Insurance: Mathematics and Economics, Elsevier, volume 128, issue C, DOI: 10.1016/j.insmatheco.2026.103246.
• Chen, Kairan & Granville, Brigitte & Matousek, Roman, 2026, "Decoding central bank communications with large language models," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 109, issue C, DOI: 10.1016/j.intfin.2026.102325.
• 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.
• Boug, Pål & Hungnes, Håvard & Kurita, Takamitsu, 2026, "Getting back on track: Forecasting after extreme observations," International Journal of Forecasting, Elsevier, volume 42, issue 2, pages 548-569, DOI: 10.1016/j.ijforecast.2025.08.005.
• 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.
• d’Albis, Hippolyte & Salem, Mélika Ben & Boubtane, Ekrame, 2026, "Extending working lives in Japan: Evidence and lessons from an outlier," The Journal of the Economics of Ageing, Elsevier, volume 34, issue C, DOI: 10.1016/j.jeoa.2026.100643.
• 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.
• Yu, Bo & Peng, Weijia & Yao, Chun & Lan, Wei, 2026, "Forecasting realized volatility of Shanghai oil futures with mix-frequency uncertainty factors," Pacific-Basin Finance Journal, Elsevier, volume 98, issue C, DOI: 10.1016/j.pacfin.2026.103150.