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Report NEP-CMP-2026-09-14
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:
- Zonghao Yang, 2026, "Counterfactual Analysis via Large Language Models," Papers, arXiv.org, number 2608.05367, Aug.
- Mingli Chen & Rama Cont & Andreas Joseph & Michael Kumhof & Xinlei Pan & Wei Xiong & Xuan Zhou, 2025, "Deep reinforcement learning in a monetary model," Bank of England Staff Working Paper series, Bank of England, number 1142, Sep.
- Giovanni Dispoto & Marcello Restelli & Carmine Ventre, 2026, "Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimization," Papers, arXiv.org, number 2609.02677, Sep.
- Harris Cobb & Wenbo Hao & Yingjie Liu, 2026, "Neural Networks with Local Converging Inputs for Efficient Options Pricing Models," Papers, arXiv.org, number 2608.02778, Aug.
- Pawel Struski & Jakub Swistak & Inez Okulska & Przemyslaw Biecek, 2026, "Competitive Market Behavior of LLMs," Papers, arXiv.org, number 2609.02580, Sep.
- Rawend Brahem, 2026, "Forecasting Inflation in Tunisia Using Machine Learning Methods," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 25-2026, Sep, revised 17 Sep 2026.
- Davood Wadi & Yu Ma, 2026, "Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf," Papers, arXiv.org, number 2608.22697, Aug, revised Oct 2026.
- Marcus Buckmann & Galina Potjagailo, 2025, "Infusing economically motivated structure into machine learning methods," Bank of England Staff Working Paper series, Bank of England, number 1144, Sep.
- Marcus Buckmann & Galina Potjagailo & Philip Schnattinger, 2025, "Blockwise Boosted Inflation: Non-linear determinants of inflation using machine learning," Bank of England Staff Working Paper series, Bank of England, number 1143, Sep.
- Nathan Canen & Ted Enamorado, 2026, "When Predictions Become Regressors: A Split-Sample Correction for Biases in Downstream Inference," Papers, arXiv.org, number 2608.02909, Aug.
- Ahmed Asaad & Amr Mohamed & Yang Zhang & Omneya Abdelsalam, 2026, "The Analyst in the Prompt: Role, Retrieval, and Memory Biases in LLM Financial Analysis," Papers, arXiv.org, number 2609.03218, Sep.
- Raphaël Teixeira, 2026, "Impact of Human-AI Teaming on Creativity," Post-Print, HAL, number hal-05723566, Feb.
- Jermyn Zhen Yong Bek & Zhuang Qiang Bok & Zhongtian Sun, 2026, "FinSkillBench: Evaluating AI Agents and Domain Skills for Investment Management," Papers, arXiv.org, number 2608.18099, Jun.
- Pattaraphon Kenny Wongchamcharoen & Kris Gulati & Min Min Fong & Abhishek Nagaraj, 2026, "CentaurBench: Benchmarking LLM Capabilities on Augmenting vs. Automating Real-World Work Tasks," NBER Working Papers, National Bureau of Economic Research, Inc, number 35663, Aug.
- Yingjian Pan & Xiaowei Ding & Kay Giesecke, 2026, "Agentic Empirical Asset Pricing: Methodological Foundations," Papers, arXiv.org, number 2609.00731, Sep.
- André Luduvice & Roberto Pinheiro, 2026, "AI-Augmented Capital-Skill Complementarity," Working Papers, Federal Reserve Bank of Cleveland, number 26-22, Sep, DOI: 10.26509/frbc-wp-202622.
- Andreas Bauer, 2026, "The Fallback as Signal: Preserved Human Skill, Liability, and Competence Signaling in Credence-Good Markets under Improving AI," Papers, arXiv.org, number 2608.04276, Aug, revised Oct 2026.
- Tetsuya Takaishi, 2026, "Quantum Circuit Learning for Volatility Modeling: Multifractal Analysis of Realized Volatility Time Series," Papers, arXiv.org, number 2609.04569, Sep.