Report NEP-CMP-2026-10-12

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:

• Kelvin J. L. Koa & Filip Orestav & Shengqiong Wu & Michael J. Wooldridge & Ke-Wei Huang, 2026, "Self-Evolving Multi-Agent Symbolic Discovery for Financial Fundamental Analysis," Papers, arXiv.org, number 2609.32746, Sep.
• Wee Ling Tan & Stephen Roberts & Stefan Zohren, 2026, "Taming the Greeks: Option Portfolios with Inductive Biases," Papers, arXiv.org, number 2609.33767, Sep.
• Anna B. Schmidt, 2026, "Bayesian Machine Learning Methods For Large Scale Demand Estimation," Papers, arXiv.org, number 2610.08409, Oct.
• Maya Kodeih & Aliaa Alnaggar & Mucahit Cevik, 2026, "Forecasting Intraday USD/CAD Exchange Rate with News-Derived Monetary-Policy Signals," Papers, arXiv.org, number 2609.32773, Sep.
• Haochen Luo & Yifan Li & Binh Minh An & Xiaolong Luo & Zhengzhao Lai & Yuan Zhang & Chen Liu, 2026, "LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs," Papers, arXiv.org, number 2609.33470, Sep.
• Siyu Wang & Yifan Wang & Yuecheng He, 2026, "MintEval: Do LLMs Implement the Trading Strategy You Asked For? A Behavioural-Equivalence Benchmark for Natural-Language-to-Strategy Code," Papers, arXiv.org, number 2610.03080, Oct.
• Junyi Ye, 2026, "Can You Delete a Year of Market Data? Machine Unlearning Against Exact Retraining Oracles," Papers, arXiv.org, number 2609.26242, Aug.
• Xiangyu Li & Fengbin Zhu & Xuan Yao & Siyu Liu & Xiaoluan Liu & Chao Wang & Huanbo Luan & Xiaofen Xing & Xiangmin Xu & Ke-Wei Huang & Richang Hong & Tat-Seng Chua, 2026, "FinNextAssist: Towards Professional Financial Deep Research Assistant," Papers, arXiv.org, number 2610.03174, Oct.
• Kriesch, Lukas & Losacker, Sebastian, 2026, "A geolocated dataset of German news articles," Papers in Innovation Studies, Lund University, CIRCLE - Centre for Innovation Research, number 2025/4, Sep.
• Jean-Loup Dupret & Donatien Hainaut & Edouard Motte, 2026, "Deep kernel hedging," Papers, arXiv.org, number 2609.34474, Sep, revised Oct 2026.
• Enang, Imowo & Omeihe, Kingsley Obi, 2025, "Real-time, adaptive AI-driven business simulation: design science research on a dynamic learning platform," Greenwich Papers in Political Economy, University of Greenwich, Greenwich Political Economy Research Centre, number 54468, Mar.
• Virginie Terraza & Aslı Boru İpek, 2026, "Regime-Dependent Dependence: Cryptocurrencies and Traditional Assets Under Structural Breaks," Post-Print, HAL, number hal-05752791, Sep, DOI: 10.3390/math14183340.
• Kenneth L. Judd & Karl Schmedders, 2026, "Deep Learning as a Projection Method for Solving Economic Models," NBER Working Papers, National Bureau of Economic Research, Inc, number 35806, Sep.
• Lenza, Michele & Marchini, Claudia, 2026, "Forecasting wages with local linear forests," Working Paper Series, European Central Bank, number 3291, Oct.