Report NEP-FOR-2026-08-24

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.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-FOR

The following items were announced in this report:

• Furno, Francesco & Giannone, Domenico, 2024, "Nowcasting Recession Risk," CEPR Discussion Papers, Centre for Economic Policy Research, number 19483, Sep.
• Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2024, "Bayesian Neural Networks for Macroeconomic Analysis," CEPR Discussion Papers, Centre for Economic Policy Research, number 19381, Aug.
• Fan, Tony Q. & Liang, Yucheng & Peng, Cameron, 2026, "The inference-forecast gap in belief updating," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 131071, Jul.
• Bassetti, Federico & Casarin, Roberto & Del Negro, Marco, 2024, "A Bayesian Approach for Inference on Probabilistic Surveys," CEPR Discussion Papers, Centre for Economic Policy Research, number 19426, Sep.
• Runyao Yu & Yuchen Tao & Yujie Chen & Wentao Wang & Derek W. Bunn, 2026, "Crossing-Free Probabilistic K-Line Forecasts Without Retraining," Papers, arXiv.org, number 2607.26792, Jul.
• Junyi Ye & Gargi Vijay Borde, 2026, "Regime-Gated Residual Mixture-of-Experts for Cross-Sectional Volatility Forecasting," Papers, arXiv.org, number 2608.12251, Aug.
• Travis L. Johnson & Jiannan Jiang & Soumyabrata Chaudhuri & Yihao Chen & Lauren Falvey & Donal O'Cofaigh, 2026, "Long-Horizon Forecasting of Complete Financial Statements with Forma," Papers, arXiv.org, number 2608.11327, Aug.
• Esteban Sánchez-Gómez, 2026, "Machine Learning Methods for Multi-Horizon Inflation Forecasting: A Comparative Analysis for Costa Rica," Documentos de Trabajo, Banco Central de Costa Rica, number 2606, Aug.
• Muzi Chen & Difang Huang & Shouyang Wang & Xinghan Xia, 2026, "Do Carbon Price Forecasts Improve Compliance Procurement? Evidence from European Union Allowances," Papers, arXiv.org, number 2607.23426, Jul.
• Junyi Ye & Ivy Gateri Wanjiku, 2026, "Calibration Bets on the Past: Post-Training Quantization for Financial Time-Series Forecasting," Papers, arXiv.org, number 2608.12259, Aug.
• Andrea Bastianin & Elisabetta Mirto & Yan Qin & Luca Rossini, 2026, "Forecasting the Price of Carbon with Macroeconomic and Financial variables∗," Working Papers, Swiss National Bank, Study Center Gerzensee, number 26.03, Jun.
• Muhammad Abdullah Haroon, 2026, "Bitcoin Price Direction Prediction via Regime-Aware Multi-Modal Fusion of Social Sentiment and Technical Features," Papers, arXiv.org, number 2607.23370, Jul.
• Lukasz Adamski & Robert Slepaczuk, 2026, "When the Fed Speaks: Dynamics and Forecasts of the Volatility Surface," Papers, arXiv.org, number 2608.10693, Aug.
• Hongyu Lin & Yulin Chen & Yuanrong Wang & Antonio Briola & Tomaso Aste, 2026, "Dependence-Informed Sparse Neural Architecture for Stock Return Prediction," Papers, arXiv.org, number 2608.14323, Aug.
• Paudel, Susan & Ramsey, A. Ford, 2026, "Revisiting Generalized Models of Japanese Seafood Demand: A Forecast Combination Approach," 2026 Annual Meeting, July 26 - 28, 2026, Kansas City, Missouri, Agricultural and Applied Economics Association, number 404527, DOI: 10.22004/ag.econ.404527.
• Alireza Kargarzadeh & Nariman Khaledian & Navid Parvini & Arman Khaledian, 2026, "Large Language Model-Driven Small-Capitalization Trading: Integrating Financial News Sentiment, Macroeconomic Indicators, and Technical Signals," Papers, arXiv.org, number 2608.12283, Aug.
• Gerhard Hellstern & Danyal Maheshwari & Martin Zaefferer & Martin Braun & Tanja Dohler, 2026, "Variational Quantum Conditional Boltzmann Machines for Time-Series Forecasting: Architectures, Symmetric Hyperparameter Evaluation, and a Nonlinear Benchmark," Papers, arXiv.org, number 2607.24065, Jul.
• Kasun Dewage & Suranadi De Silva & Shankhadeep Mondal, 2026, "Hybrid Neural-Classical Correction for Frozen Time Series Foundation Models: A Comprehensive Ablation Study on High-Frequency Stock Prediction," Papers, arXiv.org, number 2608.08825, Aug.
• Charisios Grivas & George Kapetanios & Zacharias Psaradakis & Vasilis Sarafidis & Marian Vavra & Alexia Ventouri, 2026, "Nonlinear Boosting with Multiple Testing in High-Dimensional Generalised Linear Models with Binary Responses," Papers, arXiv.org, number 2607.22440, Jul.
• Yannik Pitcan, 2026, "Does a Structural Model Add Anything to the Closing Price? Calibrated forecasting, incremental information, and match leverage in the Italian Serie A," Papers, arXiv.org, number 2608.11505, Aug.
• Rahma Mzouri & Abdelkrim Kandrouch, 2026, "Business Failure Prediction: A Comparison of Discriminant Analysis, Logit Regression, and PLS Regression
  [Prévision de la défaillance des entreprises : comparaison de l'analyse discriminante, la régression logit et PLS Business Failure Prediction:," Post-Print, HAL, number hal-05652823, Jun, DOI: 10.5281/zenodo.20500100.
• Igor Halperin, 2026, "Are Three Matrices All You Need To Beat the Market? Observable Matrix Dynamics for Portfolio Optimization," Papers, arXiv.org, number 2607.27461, Jul.

Report NEP-FOR-2026-08-24

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.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-FOR

The following items were announced in this report: