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Report NEP-CMP-2026-03-16
This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stanley Miles issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-CMP
The following items were announced in this report:
- Manan Poddar, 2026, "Uncertainty-Aware Deep Hedging," Papers, arXiv.org, number 2603.10137, Mar.
- Chia Yean Lim, 2026, "Short-Term Stock Price Prediction Based on Single and Stacking Machine Learning Models," GATR Journals, Global Academy of Training and Research (GATR) Enterprise, number gjbssr674, Mar, DOI: https://doi.org/10.35609/gjbssr.202.
- Jing Liu & Maria Grith & Xiaowen Dong & Mihai Cucuringu, 2026, "A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting," Papers, arXiv.org, number 2603.10559, Mar, revised Apr 2026.
- Adir Saly-Kaufmann & Kieran Wood & Jan Peter-Calliess & Stefan Zohren, 2026, "Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance," Papers, arXiv.org, number 2603.01820, Mar.
- 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.
- Morteza Ghomi & Samuel Hurtado, 2026, "RAUI: Uncertainty Indicators Built With Artificial Intelligence," Working Papers, Banco de España, number 2609, Mar, DOI: https://doi.org/10.53479/42605.
- Milos Ciganovic & Federico D'Amario & Massimiliano Tancioni, 2026, "Double Machine Learning for Time Series," Papers, arXiv.org, number 2603.10999, Mar.
- Bruno Petrungaro & Anthony C. Constantinou, 2026, "Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies," Papers, arXiv.org, number 2603.00041, Feb, revised May 2026.
- Yutong Yan & Raphael Tang & Zhenyu Gao & Wenxi Jiang & Yao Lu, 2026, "DatedGPT: Preventing Lookahead Bias in Large Language Models with Time-Aware Pretraining," Papers, arXiv.org, number 2603.11838, Mar.
- Kemper, Jan & Rostam-Afschar, Davud, 2026, "Earning While Learning: How to Run Batched Bandit Experiments," IZA Discussion Papers, IZA Network @ LISER, number 18429, Mar.
- Maxime Kawawa-Beaudan & Srijan Sood & Kassiani Papasotiriou & Daniel Borrajo & Manuela Veloso, 2026, "TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure," Papers, arXiv.org, number 2602.23784, Feb.
- Dehao Dai & Ding Ma & Dou Liu & Kerui Geng & Yiqing Wang, 2026, "Beyond Polarity: Multi-Dimensional LLM Sentiment Signals for WTI Crude Oil Futures Return Prediction," Papers, arXiv.org, number 2603.11408, Mar, revised Mar 2026.
- Grenz, Sabrina & Gregory, Terry & Lehmer, Florian, 2026, "AI-Powered Skill Classification: Mapping Technology Intensity in the German Labor Market," IZA Discussion Papers, IZA Network @ LISER, number 18415, Mar.
- Giménez-Nadal, José Ignacio & Molina, José Alberto & Velilla, Jorge, 2026, "Who Shirks at Work? An Application of Machine Learning to Time Use Data," IZA Discussion Papers, IZA Network @ LISER, number 18432, Mar.
- Benjamin S. Manning & John J. Horton, 2026, "General Social Agents," NBER Working Papers, National Bureau of Economic Research, Inc, number 34937, Mar.
- Koji Takahashi & Joon Suk Park, 2026, "Generative AI for surveys on payment apps: AI views on privacy and technology," BIS Working Papers, Bank for International Settlements, number 1333, Mar.