Research classified by Journal of Economic Literature (JEL) codes

1. Forecasting

2026

• 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.
• Rusyda, Hasna Afifah & Shi, Yanlin & Shang, Han Lin, 2026, "Forecast mortality rates with copula-based approaches: Novel evidence from integrated reconciliation," Insurance: Mathematics and Economics, Elsevier, volume 129, issue C, DOI: 10.1016/j.insmatheco.2026.103263.
• Charles, Amélie & Darné, Olivier, 2026, "Forecasting volatility and risk management in natural gas markets," International Economics, Elsevier, volume 187, issue C, DOI: 10.1016/j.inteco.2026.100714.
• 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.
• Tong, Bin & Li, Rui & Xu, Yuanrong, 2026, "Asymptotically unbiased extreme Expected Shortfall and tail risk forecasting in international financial markets," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 111, issue C, DOI: 10.1016/j.intfin.2026.102352.
• 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.
• Huang, Xiao & Liu, Xiaochun, 2026, "Real-time macroeconomic quantile factors for daily Value-at-Risk and Expected Shortfall forecasting: Statistical significance and economic value," Journal of Banking & Finance, Elsevier, volume 192, issue C, DOI: 10.1016/j.jbankfin.2026.107831.
• Xiao, Wei & Ji, Yangyang, 2026, "Heuristic macroeconomic expectations," Journal of Economic Behavior & Organization, Elsevier, volume 248, issue C, DOI: 10.1016/j.jebo.2026.107669.
• Lopez-Lira, Alejandro & Tang, Yuehua, 2026, "Can ChatGPT forecast stock price movements? Return predictability and large language models," Journal of Financial Economics, Elsevier, volume 184, issue C, DOI: 10.1016/j.jfineco.2026.104335.
• McWilliams, William & Stewart, Shamar L. & Massa, Olga Isengildina, 2026, "Food price inflation forecasting: Insights from a Macroeconomic Auto-Regressive Random Forest approach," Food Policy, Elsevier, volume 142, issue C, DOI: 10.1016/j.foodpol.2026.103115.
• Godinho, Frederico & Neugebauer, Katja, 2026, "House hunting high and low: Constructing a Housing Search Index for Portugal," Journal of Housing Economics, Elsevier, volume 73, issue C, DOI: 10.1016/j.jhe.2026.102163.
• 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.
• Farag, Markos, 2026, "Threshold effects in oil–metal volatility spillovers: Evidence from industrial and precious metals," Resources Policy, Elsevier, volume 119, issue C, DOI: 10.1016/j.resourpol.2026.105983.
• Doojav, Gan-Ochir, 2026, "Fiscal-monetary interactions and commodity prices in a commodity-exporting economy," Resources Policy, Elsevier, volume 120, issue C, DOI: 10.1016/j.resourpol.2026.106008.
• Kwon, Alexander & Maliar, Lilia, 2026, "Predicting retirement and Social Security claiming decisions using machine learning," Labour Economics, Elsevier, volume 102, issue C, DOI: 10.1016/j.labeco.2026.102915.
• Fiaschi, Davide & Tealdi, Cristina, 2026, "Let’s roll back! The challenging task of regulating temporary contracts," Labour Economics, Elsevier, volume 102, issue C, DOI: 10.1016/j.labeco.2026.102937.
• 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.
• Choi, Insu & Lim, Soyeong & Kim, Seoyeon & Choi, Yeona & Han, Subin & Kim, Woo Chang, 2026, "Metric-based technical indicators for yield forecasting," Pacific-Basin Finance Journal, Elsevier, volume 98, issue C, DOI: 10.1016/j.pacfin.2026.103169.
• 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.
• Caporin, Massimiliano & Gupta, Rangan & Subramaniam, Sowmya & Torrent, Hudson S., 2026, "Supply Constraints and Conditional Distribution Predictability of Inflation and its Volatility: A Nonparametric Mixed-Frequency Causality-in-Quantiles Approach," Research in Economics, Elsevier, volume 80, issue 2, DOI: 10.1016/j.rie.2026.101128.
• 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.
• Khadivar, Hamed & Davis, Frederick & Khadivar, Ameneh & Stetsyuk, Ivan, 2026, "Predicting takeover rumor accuracy with machine learning," International Review of Economics & Finance, Elsevier, volume 108, issue C, DOI: 10.1016/j.iref.2026.105204.
• Barasal Morales, Adriano, 2026, "Climate calm? Long-run temperature signals and volatility in EU carbon futures," International Review of Economics & Finance, Elsevier, volume 108, issue C, DOI: 10.1016/j.iref.2026.105221.
• Chen, Ying & Peng, Liang & Sheng, Jiliang, 2026, "Predictability of climate policy uncertainty index," International Review of Economics & Finance, Elsevier, volume 108, issue C, DOI: 10.1016/j.iref.2026.105293.
• Huang, Zhuo & Tan, Ying & Yang, Zi & Zhang, Xun, 2026, "The impact of economic uncertainty on household portfolio choice: Evidence from China," International Review of Economics & Finance, Elsevier, volume 109, issue C, DOI: 10.1016/j.iref.2026.105418.
• Jahangiri, Eshagh & Corazza, Marco, 2026, "Sentiment-based stock price prediction in developing countries: Evidence from Iran," International Review of Economics & Finance, Elsevier, volume 109, issue C, DOI: 10.1016/j.iref.2026.105423.
• Luo, Jiawen & Fu, Shengjie & Cepni, Oguzhan & Gupta, Rangan, 2026, "Climate risks and forecastability of US inflation: Evidence from dynamic quantile model averaging," International Review of Economics & Finance, Elsevier, volume 110, issue C, DOI: 10.1016/j.iref.2026.105576.
• Goel, Aditya, 2026, "Measuring macroeconomic surprise magnitude with prediction markets: Market-implied dispersion from Kalshi," International Review of Economics & Finance, Elsevier, volume 110, issue C, DOI: 10.1016/j.iref.2026.105577.
• Pinitjitsamut, Montchai, 2026, "Timing commodity sales under tail-risk control: State-dependent rules and global price signals in rubber markets," International Review of Economics & Finance, Elsevier, volume 111, issue C, DOI: 10.1016/j.iref.2026.105622.
• Venkatesh, Hari, 2026, "Do balance sheet vulnerabilities predict financial crises? Evidence from machine learning and Shapley decomposition," International Review of Economics & Finance, Elsevier, volume 111, issue C, DOI: 10.1016/j.iref.2026.105731.
• 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.
• Bao, Guanhao & Cui, Baisheng, 2026, "Research on the impact of U.S. monetary policy uncertainty on international oil price forecasting: A deep learning approach based on frequency domain decomposition," Research in International Business and Finance, Elsevier, volume 87, issue C, DOI: 10.1016/j.ribaf.2026.103391.
• Aslam, Adnan & Brahmana, Rayenda Khresna, 2026, "The dynamic relationship among private and public markets and its most important features," Research in International Business and Finance, Elsevier, volume 89, issue C, DOI: 10.1016/j.ribaf.2026.103490.
• Liu, Junjie & Song, Shijie, 2026, "Real-time dynamic higher-order moments of cryptocurrencies for volatility forecasting and risk measurement: New evidence from the SHARV–SK model," Research in International Business and Finance, Elsevier, volume 90, issue C, DOI: 10.1016/j.ribaf.2026.103521.
• Zhang, Ruohan, 2026, "Impact of category-specific equity market volatility on green and brown energy stocks," Research in International Business and Finance, Elsevier, volume 90, issue C, DOI: 10.1016/j.ribaf.2026.103526.
• Kim, Hyun Hak, 2026, "Measuring concentration risk with a herd sentiment index: Evidence from Korean financial markets," Research in International Business and Finance, Elsevier, volume 90, issue C, DOI: 10.1016/j.ribaf.2026.103543.
• Polat, Onur & Gupta, Rangan & Somani, Dhanashree & Karmakar, Sayar, 2026, "Machine learning forecasting of U.S. stock market volatility: The role of stock and oil bubbles," Research in International Business and Finance, Elsevier, volume 91, issue C, DOI: 10.1016/j.ribaf.2026.103580.
• Pacelli, Vincenzo & Povia, Maria Melania & Bellotti, Roberto, 2026, "An eXplainable early warning system to predict systemic financial crises," Research in International Business and Finance, Elsevier, volume 91, issue C, DOI: 10.1016/j.ribaf.2026.103591.
• 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.
• Du, Juan & Wu, Desheng, 2026, "Enhancing exchange rates forecasting: Leveraging long short-term memory with technical indicators," Structural Change and Economic Dynamics, Elsevier, volume 80, issue C, pages 136-148, DOI: 10.1016/j.strueco.2026.07.006.
• 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.
• Hilde C. Bjornland & Nicolas Hardy & Dimitris Korobilis, 2026, "Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-39, May.
• Luke Hartigan, 2026, "Estimating the Common Output Cycle in Australia," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-78, Sep.
• Rishabh Choudhary & Chetan Dave & Chetan Ghate, 2026, "Forecasting Indian Core Inflation: Simple Made Simpler," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-83, Sep.
• Gaddy, Hampton, 2026, "We are our memory: a flexible framework for quantifying the demographic imprints of the past," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 138974, Jun.
• Ahn, Tom & Aucejo, Esteban & James, Jonathan, 2026, "Revisiting test score forecasts and teacher value-added," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 141783, Sep.
• Koop, Gary & McIntyre, Stuart & Mitchell, James & Wu, Ping, 2026, "Incorporating Micro Data into Macro Models using Pseudo VARs," Discussion Papers, Economic Statistics Centre of Excellence, number escoe-dp-2026-04, Feb.
• Himaz, Rozana & Salmanidou, Dimitra & Ghaffarian, Saman, 2026, "Machine Learning for Estimating Catastrophic Health Spending in Disaster-Affected, Data-Scarce Settings," Discussion Papers, Economic Statistics Centre of Excellence, number escoe-dp-2026-05, Mar.
• Garay Rodrigo, 2026, "Predictive Credit-Risk Models as Managerial Interventions in Chilean Life Insurance Companies," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, volume 16, issue 3, pages 79-114.
• Slawomir Bukowski & Joanna Bukowska & Jacek Woloszyn & Agnieszka Molga, 2026, "Forecasting the EUR/PLN Exchange RateUsing Classical and Artificial Intelligence Methods:An Empirical Comparison of ARIMA, XGBoost, LSTMand Hybrid Models on NBP Data 2015-2026," European Research Studies Journal, European Research Studies Journal, volume 0, issue 2, pages 295-317.
• 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.
• Paul Handro, 2026, "From Oil Prices to Stock Trading: A Machine Learning Approach," European Research Studies Journal, European Research Studies Journal, volume 0, issue 3, pages 1038-1060.
• Artur Budzynski & Irena Szewczyk & Grzegorz Biesok & Asen Asenov & Velizara Pencheva, 2026, "Motorcycle Safety Management through Injury Severity Monitoring: Evidence from Poland," European Research Studies Journal, European Research Studies Journal, volume 0, issue 4, pages 61-68.
• Eduard Gracia, 2026, "Follow the median: revisiting bubbles and cycles," UB School of Economics Working Papers, University of Barcelona School of Economics, number 2026/497.
• 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.
• Patrick C. Higgins, 2026, "Nowcasting with Mixed Frequency Principal Components When Not All Months Are the Same," FRB Atlanta Working Paper, Federal Reserve Bank of Atlanta, number 2026-15, Sep, DOI: 10.29338/wp2026-15.
• 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.
• Todd E. Clark & Florian Huber & Gary Koop, 2026, "A Nonparametric Approach to Augmenting a Bayesian VAR with Nonlinear Factors," Working Papers, Federal Reserve Bank of Cleveland, number 26-14, Jun, DOI: 10.26509/frbc-wp-202614.
• Nicholas Fritsch & Edward Simpson Prescott, 2026, "Macroeconomic Parameter Instability in Auto Loan Loss Models," Working Papers, Federal Reserve Bank of Cleveland, number 26-18, Jul, DOI: 10.26509/frbc-wp-202618.
• 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.
• Hyung Joo Kim & Dong Hwan Oh, 2026, "Capturing Heterogeneity: Machine Learning Approaches to Implied Volatility Forecasting," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2026-049, Jul, DOI: 10.17016/FEDS.2026.049.
• Hie Joo Ahn & Jeremy B. Rudd, 2026, "United States of U-Star," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2026-060, Jul, DOI: 10.17016/FEDS.2026.060.
• Tobias Adrian & Domenico Giannone & Matteo Luciani & Mike West, 2026, "Risks and Uncertainty in Monetary Policy," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2026-061, Sep, DOI: 10.17016/FEDS.2026.061.
• Michele Modugno & Benjamin Roscoe & Sarah Zoi, 2026, "Beyond Financial Conditions: Measuring Structural Vulnerabilities in the U.S. Financial System," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2026-065, Sep, DOI: 10.17016/FEDS.2026.065.
• Freddy García-Albán & Manuel González-Astudillo, 2026, "Bells and Whistles of Nowcasting Models: Which Matter and When?," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2026-068, Oct, DOI: 10.17016/FEDS.2026.068.
• Scott A. Brave & Ben Henken & Ezra Karger & Aryan Safi, 2026, "The Chicago Fed Labor Market Indicators: Bridging the Gap with Alternative Labor Data," Working Paper Series, Federal Reserve Bank of Chicago, number WP 2026-09, Apr, DOI: 10.21033/wp-2026-09.
• Nathan Schor & Minchul Shin, 2026, "ForeComp: An R Package for Comparing Predictive Accuracy Using Fixed-Smoothing Asymptotics," Working Papers, Federal Reserve Bank of Philadelphia, number 26-38, Aug, DOI: 10.21799/frbp.wp.2026.38.
• Minchul Shin, 2026, "An Auditable AI Agent Loop for Empirical Economics: A Case Study in Forecast Combination," Working Papers, Federal Reserve Bank of Philadelphia, number 26-41, Aug, DOI: 10.21799/frbp.wp.2026.41.
• Tulia Gattone & Donato Romano & Luca Tiberti, 2026, "Pathways of Climate Variability, Agricultural Performance, and Conflict: A Machine Learning Approach to Complex Dependencies," Working Papers - Economics, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa, number wp2026_10.rdf.
• Vera Barinova & Margarita Gvozdeva, 2026, "The role of SMEs in the development of tourism in Russia," Published Papers, Gaidar Institute for Economic Policy, number ppaper-2026-1619, revised 2026.
• Diego Franco & Delia Ruiz & Walter Cuba, 2026, "Intraday Prediction of Operating-Rate Deviations from the Policy Rate: Evidence from Peru," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 17-2026, Jul.
• Abdukakhkhor Abdurakhmonov, 2026, "Macroeconomic Forecasting Using Machine Learning Methods: An Application to Uzbekistan," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 19-2026, Aug.
• Tamkin Nuriyev & Aygun Garayeva & Gulzar Tahirova, 2026, "Construction and Forecasting of the Imported Food Price Index in Azerbaijan," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 20-2026, Aug.
• Aaron L. Garavito-Acosta & Wilmer Martinez-Rivera & Juan J. Ospina-Tejeiro & Edgar Caicedo-Garcia, 2026, "Experimenting with Large Language Models for Inflation Forecasting in Colombia," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 23-2026, Aug, revised 01 Sep 2026.
• Rawend Brahem, 2026, "Forecasting Inflation in Tunisia Using Machine Learning Methods," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 25-2026, Sep, revised 17 Sep 2026.
• Gerald Cisneros & Marcos Cerón & Jose Apaza, 2026, "Financial Stress Indicator for Peruvian Non-Financial Corporations," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 29-2026, Oct.
• 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 Government and Public Transformation, number 31, Apr.
• Adolfo De Unánue T. & Fernanda Sobrino, 2026, "," Working Paper Series of the School of Government and Public Transformation, School of Government and Public Transformation, number 34, May.
• Jose Morales-Arilla & Rodrigo Sanchez Gavito Portilla & Hermilo Cortés & Ana Gabriela Ibarra, 2026, "Monitoreo económico de alta resolución en El Salvador: Estimación oportuna del crecimiento del PIB y su desagregación espacial," Working Paper Series of the School of Government and Public Transformation, School of Government and Public Transformation, number 35, Jul.
• 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.
• Laurent Ferrara & Aikaterini Karadimitropoulou & Athanasios Triantafyllou, 2026, "Commodity price uncertainty comovement: Does it matter for global economic growth?," Post-Print, HAL, number hal-05607366, Jul, DOI: 10.1016/j.euroecorev.2026.105339.
• Emmanouil Sofianos & Thierry Betti & Theophilos Papadimitriou & Amélie Barbier-Gauchard & Periklis Gogas, 2026, "Using DSGE and Machine Learning to Forecast Public Debt for France," Post-Print, HAL, number hal-05620169, Mar, DOI: 10.1002/for.70144.
• Julien Tekasala Nlandu, 2026, "Prediction of Bankruptcy in the Agro-Food Industry in the City of Kinshasa: Application of Edward Altman's Z-Score Model
  [Prédiction de faillite de l'industrie agroalimentaire de la ville de Kinshasa : application du modèle Z-score d'Edward Altman," Post-Print, HAL, number hal-05719834, Aug, DOI: 10.5281/Zenodo.21776202.
• Hippolyte D’albis & Mélika Ben Salem & Ekrame Boubtane, 2026, "Extending working lives in Japan: Evidence and lessons from an outlier," Post-Print, HAL, number halshs-05666846, Jun, DOI: 10.1016/j.jeoa.2026.100643.
• Hippolyte D’albis & Mélika Ben Salem & Ekrame Boubtane, 2026, "Extending working lives in Japan: Evidence and lessons from an outlier," PSE-Ecole d'économie de Paris (Postprint), HAL, number halshs-05666846, Jun, DOI: 10.1016/j.jeoa.2026.100643.
• Hippolyte D’albis & Mélika Ben Salem & Ekrame Boubtane, 2026, "Extending Working Lives in Japan: Evidence and Lessons from an Outlier," PSE Working Papers, HAL, number halshs-05666786, Jun.
• 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.
• Valérie Mignon & Marc Joëts & Christophe Hurlin, 2026, "ZICO: A Credit Scoring Approach to Detecting Zombie Papers," Working Papers, HAL, number hal-05699398.
• Matthieu Bordenave, 2026, "Reforestation Policy under Carbon Taxation: Climate Efficacy, Biodiversity, and Macro-Financial Risks," Working Papers, HAL, number hal-05754509, Sep.
• Charles Boullon & Edouard Richard & Louis-Amand Gérard, 2026, "Optimizing Tail-Risk Hedging via Model Hybridization: Integrating Strategic Asset Allocation with Regime Detection," Working Papers, HAL, number hal-05781709, Aug.
• Hippolyte D’albis & Mélika Ben Salem & Ekrame Boubtane, 2026, "Extending Working Lives in Japan: Evidence and Lessons from an Outlier," Working Papers, HAL, number halshs-05666786, Jun.
• Safira, Dinda Ayu & Kuswanto, Heri & Ahsan, Muhammad & Sibbertsen, Philipp, 2026, "A Majorization-Minimization gLASSO Framework for SETAR Models: Theory, Simulation, and Application to PM2.5 Data," Hannover Economic Papers (HEP), Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, number dp-746, May.
• Özer, Yeliz & del Barrio Castro, Tomás & Escribano, Álvaro & Sibbertsen, Philipp, 2026, "Modeling Long Memory in 67 Million Years of Cyclical Climate Trends: Anticipating Future Cycles," Hannover Economic Papers (HEP), Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, number dp-751, Aug.
• Ewald, Anika & Puhani, Patrick A., 2026, "Does Pension Coverage Predict Fertility? International Evidence from OLS, Fixed Effects and Machine Learning Random Forests," Hannover Economic Papers (HEP), Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, number dp-752, Nov.
• Almgren, Pelle, 2026, "Revisiting US Herding – Accounting for the Dynamics," Working Papers, Lund University, Department of Economics, number 2026:9, Sep.
• 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.
• Saboin, José Luis & Guerrero, Diego & Mazzocca, Angelo, 2026, "Nowcasting Real GDP Growth in The Bahamas," IDB Publications (Working Papers), Inter-American Development Bank, number 14635, May, DOI: http://dx.doi.org/10.18235/0014052.
• Yasin Buyukkor, 2026, "Deep Learning in Financial Time Series: A Comparative Analysis of RNN, GRU, LSTM, and Hybrid Models," Croatian Economic Survey, The Institute of Economics, Zagreb, volume 28, issue 1, pages 5-38, June.
• 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.

2025

• Gonçalves, Sílvia & McCracken, Michael W. & Yao, Yongxu, 2025, "Bootstrapping out-of-sample predictability tests with real-time data," Journal of Econometrics, Elsevier, volume 247, issue C, DOI: 10.1016/j.jeconom.2024.105916.
• Paap, Richard & Franses, Philip Hans, 2025, "Shrinkage estimators for periodic autoregressions," Journal of Econometrics, Elsevier, volume 247, issue C, DOI: 10.1016/j.jeconom.2024.105937.
• Czellar, Veronika & Garcia, René & Le Grand, François, 2025, "Uncovering asset market participation from household consumption and income," Journal of Econometrics, Elsevier, volume 248, issue C, DOI: 10.1016/j.jeconom.2024.105867.
• Ding, Yi & Engle, Robert & Li, Yingying & Zheng, Xinghua, 2025, "Multiplicative factor model for volatility," Journal of Econometrics, Elsevier, volume 249, issue PB, DOI: 10.1016/j.jeconom.2025.105959.
• Lin, Tzu-Chi & Liu, Chu-An, 2025, "Model averaging prediction for possibly nonstationary autoregressions," Journal of Econometrics, Elsevier, volume 249, issue PB, DOI: 10.1016/j.jeconom.2025.105994.
• Tu, Yundong & Wang, Siwei, 2025, "Quantile prediction with factor-augmented regression: Structural instability and model uncertainty," Journal of Econometrics, Elsevier, volume 249, issue PB, DOI: 10.1016/j.jeconom.2025.105999.
• Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025, "Bayesian neural networks for macroeconomic analysis," Journal of Econometrics, Elsevier, volume 249, issue PC, DOI: 10.1016/j.jeconom.2024.105843.
• Ahrens, Maximilian & Erdemlioglu, Deniz & McMahon, Michael & Neely, Christopher J. & Yang, Xiye, 2025, "Mind your language: Market responses to central bank speeches," Journal of Econometrics, Elsevier, volume 249, issue PC, DOI: 10.1016/j.jeconom.2024.105921.
• Haghighi, Maryam & Joseph, Andreas & Kapetanios, George & Kurz, Christopher & Lenza, Michele & Marcucci, Juri, 2025, "Machine Learning for Economic Policy," Journal of Econometrics, Elsevier, volume 249, issue PC, DOI: 10.1016/j.jeconom.2025.105970.
• Chen, Han & Fei, Yijie & Yu, Jun, 2025, "Multivariate stochastic volatility models based on generalized Fisher transformation," Journal of Econometrics, Elsevier, volume 251, issue C, DOI: 10.1016/j.jeconom.2025.106041.
• Chen, Yi-Ting & Liu, Chu-An & Su, Jiun-Hua, 2025, "Bregman model averaging for forecast combination," Journal of Econometrics, Elsevier, volume 251, issue C, DOI: 10.1016/j.jeconom.2025.106076.
• Sun, Yixiao, 2025, "Support vector decision making," Journal of Econometrics, Elsevier, volume 251, issue C, DOI: 10.1016/j.jeconom.2025.106087.
• Wróblewska, Justyna, 2025, "Bayesian analysis of seasonally cointegrated VAR models," Econometrics and Statistics, Elsevier, volume 35, issue C, pages 55-70, DOI: 10.1016/j.ecosta.2023.02.002.
• Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2025, "Density forecasts of inflation: A quantile regression forest approach," European Economic Review, Elsevier, volume 178, issue C, DOI: 10.1016/j.euroecorev.2025.105079.
• Ellington, Michael & Kalli, Maria, 2025, "Predictive distributions and the market return: The role of market illiquidity," European Journal of Operational Research, Elsevier, volume 323, issue 1, pages 309-322, DOI: 10.1016/j.ejor.2025.01.006.
• 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.
• Bårdsen, Gunnar & Nymoen, Ragnar, 2025, "Dynamic time series modelling and forecasting of COVID-19 in Norway," International Journal of Forecasting, Elsevier, volume 41, issue 1, pages 251-269, DOI: 10.1016/j.ijforecast.2024.05.004.
• 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.
• Degiannakis, Stavros & Kafousaki, Eleftheria, 2025, "Disaggregating VIX," International Journal of Forecasting, Elsevier, volume 41, issue 4, pages 1559-1588, DOI: 10.1016/j.ijforecast.2025.01.007.
• Binz, Oliver & Schipper, Katherine & Standridge, Kevin R., 2025, "Estimating profitability decomposition frameworks via machine learning: Implications for earnings forecasting and financial statement analysis," Journal of Accounting and Economics, Elsevier, volume 80, issue 2, DOI: 10.1016/j.jacceco.2025.101805.
• Feng, Guanhao & He, Xin & Wang, Yanchu & Wu, Chunchi, 2025, "Predicting individual corporate bond returns," Journal of Banking & Finance, Elsevier, volume 171, issue C, DOI: 10.1016/j.jbankfin.2024.107372.
• Amendola, Marco & Pereira, Marcelo C., 2025, "State-dependent impulse responses in agent-based models: A new methodology and an economic application," Journal of Economic Behavior & Organization, Elsevier, volume 229, issue C, DOI: 10.1016/j.jebo.2024.106811.