Report NEP-CMP-2026-09-07

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.

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Other reports in NEP-CMP

The following items were announced in this report:

• Sahab Zandi & Noah Kostesku & Christophe Mues & Mar'ia 'Oskarsd'ottir & Cristi'an Bravo, 2026, "Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models," Papers, arXiv.org, number 2608.17715, Aug.
• Martin Cekal, 2026, "Reinforcement Learning for Perpetual Futures Market Making," FFA Working Papers, Prague University of Economics and Business, number 6.004, Jun, revised 15 Jun 2026.
• Marcus Buckmann & Quynh Anh Nguyen & Ed Hill, 2025, "Revealing economic facts: LLMs know more than they say," Bank of England Staff Working Paper series, Bank of England, number 1150, Oct.
• João Anderson da Silva Felix & Michel Alexandre & Cássio da Nóbrega Besarria, 2026, "Forecasting the Cost of a Basic Basket of Goods: a comparative analysis using machine learning models and online prices," Working Papers Series, Central Bank of Brazil, Research Department, number 650, Aug.
• Ashwin, Julian & Beaudry, Paul & Ellison, Martin, 2024, "Neural Network Learning for Nonlinear Economies," CEPR Discussion Papers, Centre for Economic Policy Research, number 19295, Jul.
• Jeremy Bejarano & Viren Desai & Kausthub Keshava & Arsh Kumar & Zixiao Wang & Vincent Hanyang Xu & Yangge Xu, 2026, "An Open Benchmark for Evaluating Time Series Forecasting Methods across Financial Markets," Working Papers, Office of Financial Research, US Department of the Treasury, number 26-05, Aug.
• Ayoub Louhab & Abdelhak Yaacoubi & Mohamed Azouazi, 2026, "Mapping the Evolution of Artificial Intelligence in Banking: A Systematic Bibliometric Review of Research Trends, Techniques, Applications, and Emerging Challenges," Post-Print, HAL, number hal-05694871, Jul, DOI: 10.5281/zenodo.21259423.
• Julius Dobelt, 2026, "Cross-Sectional Heterogeneity in LSTM Networks for Financial Time Series," Papers, arXiv.org, number 2608.05755, Aug, revised Aug 2026.
• Jeremy Bejarano & Viren Desai & Kausthub Keshava & Arsh Kumar & Zixiao Wang & Vincent Hanyang Xu & Yangge Xu, 2026, "Do Deep Learning Methods Improve Financial Forecasts?," The OFR Blog, Office of Financial Research, US Department of the Treasury, number 26-10, 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.
• Ryu, Jaehyeon & Yu, Jisang, 2026, "Reducing Basis Risk in Index Insurance Using Satellite-Based Vegetation Indices and Machine Learning," 2026 Annual Meeting, July 26 - 28, 2026, Kansas City, Missouri, Agricultural and Applied Economics Association, number 404414, DOI: 10.22004/ag.econ.404414.
• Joshua Elias Fred A. Suero, 2025, "Developing a Standard Strategy for Time Series Forecasting Integrating Statistical and Machine Learning Techniques Using a Meta-Model Approach and its Application in Generating External Debt Projections," BSP Discussion Paper Series, Bangko Sentral ng Pilipinas, number 202503, Apr.
• Junting Duan & Markus Pelger, 2026, "Inference with AI-Generated Covariates," NBER Working Papers, National Bureau of Economic Research, Inc, number 35481, Jul.
• Patrick Cheridito & Moritz Weiss, 2026, "Multi-Level Market Making with Reinforcement Learning," Papers, arXiv.org, number 2608.18195, Aug.
• Eray Genc{c}ay, 2026, "What survives honest evaluation? Leakage-safe, search-aware assessment of LLM-driven trading strategy discovery," Papers, arXiv.org, number 2608.27734, Aug.
• Fusheng Luo, 2026, "From Financial Sentiment Classification to Return Predictability: A QLoRA Benchmark of Large Language Models," Papers, arXiv.org, number 2608.04200, Aug.
• von Luckner, Clemens Graf & Horn, Sebastian & Kraay, Aart C. & Ramalho, Rita, 2026, "Predicting Debt Distress in Low-Income Countries," Policy Research Working Paper Series, The World Bank, number 11441, Sep.
• Penaranda, Francisco & Sentana, Enrique, 2024, "Portfolio management with big data," CEPR Discussion Papers, Centre for Economic Policy Research, number 19314, Jul.
• Max Ahrens & Dragos Gorduza & Micheal McMahon, 2025, "EcoFinBench – a natural language processing benchmark for economics and finance," Bank of England Staff Working Paper series, Bank of England, number 1163, Dec.
• Javaudin Lucas & Araldo Andrea & Coulombel Nicolas, 2026, "Accounting for intra-household joint travel in agent-based transport simulations," Papers, arXiv.org, number 2608.18657, Aug.
• Sebastian Galiani & Federico Ariel López & Raul A. Sosa, 2026, "AI Agents and Prompt Engineering in Econometric Coding," NBER Working Papers, National Bureau of Economic Research, Inc, number 35588, Aug.
• Georgios Chionas & Charalampos Kleitsikas & Stefanos Leonardos & Leandro S'anchez-Betancourt & Carmine Ventre, 2026, "Concentrated Liquidity Provision: a Reinforcement Learning Perspective," Papers, arXiv.org, number 2608.19389, Aug.
• Yupeng Zhang & Liuyuan Jiang & Hongyi Huang & Bingheng Li & Lisha Chen, 2026, "RetailAgent: Structured Adverse Timing in Self-Conditioned Multimodal LLM Trading Agents," Papers, arXiv.org, number 2608.28399, Aug, revised Oct 2026.
• Georges Sfeir & Stephane Hess & Thomas O. Hancock & Filipe Rodrigues & Jamal Amani Rad & Michiel Bliemer & Matthew Beck & Fayyaz Khan, 2025, "A Bayesian latent class reinforcement learning framework to capture adaptive, feedback-driven travel behaviour," Papers, arXiv.org, number 2512.14713, Dec.
• Joan Christine S. Allon-Pineda, 2025, "Inflation Unpacked: Breaking Down the Key Components Using a Neural Phillips Curve," BSP Discussion Paper Series, Bangko Sentral ng Pilipinas, number 202505, Apr.
• Ruichen Deng & Yichi Zhang, 2026, "Lead-Lag Relationships in Financial Markets: A Comparison of Multiple Clustering Algorithms," Papers, arXiv.org, number 2608.24703, Aug.
• Jieun Lee, 2026, "Double/Debiased Machine Learning for Functional-Form-Robust Spatial Autoregression," Papers, arXiv.org, number 2608.22706, Aug.
• Kevin A. Bryan & Joshua S. Gans, 2026, "Training AI For When Humans Will Use It," Papers, arXiv.org, number 2608.12538, Aug.
• Matthew O. Jackson & Benjamin S. Manning & Yutong Xie & Walter Yuan & Qiaozhu Mei, 2026, "Modeling Human Behavior with Type Vectors Using AI," Papers, arXiv.org, number 2608.18265, Aug, revised Sep 2026.
• Enrico Minnella & Ana Pereira & Eugen Tereanu, 2026, "The devil in the DeTail: assessing state-contingent tail effects of a releasable macroprudential capital buffer using a parsimonious agent-based framework," Bank of England Staff Working Paper series, Bank of England, number 1198, Jul.
• Bläser, Nikolaj & Magnussen, Búgvi Benjamin & Fuentes, Gabriel & Reinhardt, Line & Lindén, Anders, 2026, "A Simulation Model for Predicting Tramp Shipping Supply," Discussion Papers, Norwegian School of Economics, Department of Business and Management Science, number 2026/10, Aug.

Report NEP-CMP-2026-09-07

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: