Report NEP-FOR-2026-05-25

This is the archive for NEP-FOR, a report on new working papers in the area of Forecasting. Rob J Hyndman issued this report. It is usually issued weekly.

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Other reports in NEP-FOR

The following items were announced in this report:

• Philippe Goulet Coulombe, 2026, "Quantifying the Risk-Return Tradeoff in Forecasting," Papers, arXiv.org, number 2605.09712, May.
• Philippe Goulet Coulombe, 2026, "LGB+: A Macroeconomic Forecasting Road Test," Papers, arXiv.org, number 2605.09740, May.
• Bloom, Nicholas & Kawakubo, Taka & Meng, Charlotte & Mizen, Paul & Riley, Rebecca & Senga, Tatsuro & Van Reenen, John, 2025, "Do well managed firms make better forecasts?," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 138480, Dec.
• Andrea Carriero & Florian Huber & Davide Pettenuzzo, 2026, "Double Descent and Benign Overfitting in Macroeconomic Forecasting," Papers, arXiv.org, number 2605.15358, May.
• Jonas F. Frederiksen & Muneya Matsui & Rasmus S. Pedersen, 2026, "Heavy Tails and Predictive Ability Testing," Papers, arXiv.org, number 2605.16866, May, revised May 2026.
• Patrick Woitschig & Mike West, 2026, "Bayesian Dynamic Modeling of Realized Volatility in Financial Asset Price Forecasting," Papers, arXiv.org, number 2605.12099, May.
• Sanjiv R Das & Tarang Goyal & Mohini Yadav, 2026, "Multivariate Financial Forecasting using the Chronos Time Series Foundation Models," Papers, arXiv.org, number 2605.21504, May.
• Runyao Yu & Julia Lin & Derek W. Bunn & Jochen Stiasny & Wentao Wang & Yujie Chen & Tara Esterl & Peter Palensky & Jochen L. Cremer, 2026, "A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting," Papers, arXiv.org, number 2605.09061, May.
• Marco Gregnanin & Johannes De Smedt & Giorgio Gnecco & Maurizio Parton, 2026, "The Statistical Significance of the Inclusion of Graph Neural Networks in the Financial Time Series Forecasting Problem," Papers, arXiv.org, number 2605.21192, May.
• Mathias Mesfin, 2026, "Sequential Structure in Intraday Futures Data: LSTM vs Gradient Boosting on MNQ," Papers, arXiv.org, number 2605.17724, May.
• 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.
• Obidaju, Innocent, 2026, "Time Series Forecasting Model of TETFund Allocation to Public Tertiary Institutions in Nigeria (2010–2023)," MPRA Paper, University Library of Munich, Germany, number 129024, May.
• Namhyoung Kim & Jae Wook Song, 2026, "Vector-Quantized Discrete Latent Factors Meet Financial Priors: Dynamic Cross-Sectional Stock Ranking Prediction for Portfolio Construction," Papers, arXiv.org, number 2605.13407, May.
• 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.
• Gustav Olaf Yunus Laitinen-Fredriksson Lundstrom-Imanov & Hafize Gonca Comert, 2026, "SAGA: A Sequence-Adaptive Generative Architecture for Multi-Horizon Probabilistic Forecasting with Adaptive Temporal Conformal Prediction," Papers, arXiv.org, number 2605.19014, May.
• Stanis{l}aw M. S. Halkiewicz, 2026, "Rolling-Origin Conformal Prediction under Local Stationarity and Weak Dependence," Papers, arXiv.org, number 2605.08422, May.
• Eshwar Sai Kandimalla & Sravan Chowdary Kankanala & Sumana Bhimineni & Hem Sundhar Korukunda & Vivek Yelleti, 2026, "Bi-Level Chaotic Fusion Based Graph Convolutional Network for Stock Market Prediction Interval," Papers, arXiv.org, number 2605.16324, May.
• Ujjwala Vadrevu, 2026, "A Hybrid Gaussian Process Regression Framework for Stable Volatility-Covariance Estimation: Evidence from Global Equity Indices," Papers, arXiv.org, number 2605.17275, May.
• Massimo Giannini, 2026, "Nowcasting Italian Municipal Income with Nightlights: A Deep Learning Approach," Papers, arXiv.org, number 2605.08782, May.
• 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.