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Report NEP-CMP-2026-07-27
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:
- Aquilina, Matteo & Araujo, Douglas & Gelos, Gaston & Park, Taejin & Perez-Cruz, Fernando, 2025, "Harnessing Artificial Intelligence for Monitoring Financial Markets," CEPR Discussion Papers, Centre for Economic Policy Research, number 20768, Oct.
- Veni Arakelia & Guglielmo Maria Caporale & Mirto M. Gasparinatou & Menelaos Karanasos, 2026, "Machine Learning and Liquidity Dynamics in European Stock Markets," CESifo Working Paper Series, CESifo, number 12829.
- Yang Liu & Yuhao Liu & Yunran Wei, 2026, "A Noise-Robust Elicit-to-Optimize Framework for Distortion Riskmetrics via Inverse Reinforcement Learning," Papers, arXiv.org, number 2607.14373, Jul.
- Anand, Kartik & Kazinnik, Sophia & Leonello, Agnese & Panetti, Ettore, 2025, "Ex Machina: Financial Stability in the Age of Artificial Intelligence," CEPR Discussion Papers, Centre for Economic Policy Research, number 20681, Sep.
- Xianhua Peng & Wu Guo & Songyan Wang & Jianfei Zhu, 2026, "Deep Learning for Dynamic Programming with Recursive Utility Using First-order Conditions," Papers, arXiv.org, number 2607.09461, Jul.
- Bartosz Zi'o{l}ko & Kacper Dobrzeniewski, 2026, "Augmenting Fundamental Analysis with Large Language Models: A RAG-Based System for Generating Investor Briefs," Papers, arXiv.org, number 2607.09121, Jul.
- Fengzhuo Zhang & Zhuoran Yang & Dirk Bergemann, 2026, "Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion," Papers, arXiv.org, number 2607.14371, Jul.
- Griesshaber, Niclas & Ogilvie, Sheilagh, 2025, "Transplanting Craft Guilds to Colonial Latin America: A Large Language Model Analysis," CEPR Discussion Papers, Centre for Economic Policy Research, number 20556, Aug.
- Yufei Wu & Daniel Schmierer & Dan Zylberglejd, 2026, "Estimating Supply Incrementality in Two-sided Marketplaces: A Causal Machine Learning Approach," Papers, arXiv.org, number 2606.30999, Jun.
- Xianhua Peng & Wu Guo, 2026, "Deep Learning for Dynamic Programming with Recursive Utility," Papers, arXiv.org, number 2607.04278, Jul.
- Torben S. D. Johansen & Julius Koschnick & Christian Vedel, 2026, "How to deal with machine learning bias in economic history," Papers, arXiv.org, number 2606.28063, Jun.
- Fernández-Villaverde, Jesús, 2025, "Deep Learning for Solving Economic Models," CEPR Discussion Papers, Centre for Economic Policy Research, number 20669, Sep.
- Anastasis Kratsios & Giulia Livieri & Philipp Schmocker, 2026, "NeuralChaos: Optimal Adapted Approximation of Square Integrable Predictable Processes," Papers, arXiv.org, number 2607.14361, Jul.
- Aldasoro, Inaki & Hördahl, Peter & Schrimpf, Andreas & Zhu, Sonya, 2025, "Predicting Financial Market Stress with Machine Learning," CEPR Discussion Papers, Centre for Economic Policy Research, number 20439, Jul.
- Garau, Alessio, 2026, "How economics classifies itself: text-based JEL codes and their consistency," MPRA Paper, University Library of Munich, Germany, number 130163, Jul.
- Corrado Di Guilmi & Takashi Kamihigashi, 2026, "Introducing Forward-Looking Intertemporal Optimization in an Agent-Based Model," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-53, Jul.
- 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.
- Ralph S. J. Koijen & Bradford Levy, 2026, "Assessing the Benefits of Optimized Agentic AI Systems for Asset Pricing," NBER Working Papers, National Bureau of Economic Research, Inc, number 35431, Jul.
- 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.
- Chi, Ta-Chung & Fan, Ting-Han & Ghigliazza, Raffaele & Giannone, Domenico & Wang, Zixuan (Kevin), 2025, "Macroeconomic Forecasting and Machine Learning," CEPR Discussion Papers, Centre for Economic Policy Research, number 20727, Oct.
- Po Han Teo, 2026, "LLM Agents as Static Level-k Players in Behavioural Games," Papers, arXiv.org, number 2606.27845, Jun, revised Sep 2026.
- Ioanna-Yvonni Tsaknaki & Andrea Macr`i & Fabrizio Lillo, 2026, "Can Reinforcement Learning Efficiently Discover Price Manipulation?," Papers, arXiv.org, number 2607.06121, Jul.
- Danial Ramezani & Mostafa Abouei Ardakan & Mohamadreza Dehghani Ahmadabad, 2026, "A novel robust mixed integer linear programming model for index tracking problem under no rebalancing: heuristic optimization approach," Papers, arXiv.org, number 2607.09556, Jul.
- Ryan Parker & Mark Stedman & Luca Capriotti, 2026, "Semi-Analytical Pricing for General Default Intensity Models," Papers, arXiv.org, number 2606.21800, Jun.
- Rui Yao & Kenan Zhang, 2026, "Perturbed utility Markovian traffic equilibrium: theory and computation," Papers, arXiv.org, number 2607.09568, Jul.
- Piguillem, Facundo & Shi, Liyan, 2025, "Hyperbolic Discounting with Random Gratification," CEPR Discussion Papers, Centre for Economic Policy Research, number 20579, Aug.
- Boero, Riccardo, 2026, "A Stylized Computable General Equilibrium Model for Circular Economy Strategy Analysis," SocArXiv, Center for Open Science, number 69gcx_v1, Jun, DOI: 10.31235/osf.io/69gcx_v1.
- Alexandros Gelastopoulos, 2026, "Feedback dynamics in matching networks drive behavioral differentiation despite overlapping objectives," Papers, arXiv.org, number 2606.31802, Jun.