Report NEP-CMP-2026-05-11

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:

• Olivia Zhang & Zhilin Zhang, 2026, "A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective," Papers, arXiv.org, number 2605.05211, Apr.
• Jan Rovirosa & Jesse Schmolze, 2026, "Learning Time-Inhomogeneous Markov Dynamics in Financial Time Series via Neural Parameterization," Papers, arXiv.org, number 2605.04690, May.
• Ryuji Hashimoto & Ryosuke Takata & Masahiro Suzuki & Yuki Tanaka & Kiyoshi Izumi, 2026, "Financial Market as a Self-Organized Ecosystem: Simulation via Learning with Heterogeneous Preferences," Papers, arXiv.org, number 2604.23975, Apr.
• Mohammad Al Ridhawi & Mahtab Haj Ali & Hussein Al Osman, 2026, "Multi-Dimensional Behavioral Evaluation of Agentic Stock Prediction Systems Using Large Language Model Judges with Closed-Loop Reinforcement Learning Feedback," Papers, arXiv.org, number 2605.05739, May, revised May 2026.
• Yusuke Oh & Mototsugu Shintani, 2026, "Forecasting Recessions Using Machine Learning on Text Data and Mixed-Frequency Predictors," IMES Discussion Paper Series, Institute for Monetary and Economic Studies, Bank of Japan, number 26-E-07, Mar.
• Jean-Loup Dupret & Davide Gallon & Patrick Cheridito, 2026, "INEUS: Iterative Neural Solver for High-Dimensional PIDEs," Papers, arXiv.org, number 2605.06281, May.
• Adil Reghai & Lama Tarsissi & G'erard Biau & Alex Lipton, 2026, "A Geometry-Aware Residual Correction of Hagan's SABR Implied Volatility Formula," Papers, arXiv.org, number 2605.06604, May.
• Dmitri Goloubentsev & Natalija Karpichina, 2026, "SNAPO: Smooth Neural Adjoint Policy Optimization for Optimal Control via Differentiable Simulation," Papers, arXiv.org, number 2605.06570, May.
• Alexis Lazanas & Spyridon Karpouzis, 2026, "Beyond Sequential Prediction: Learning Financial Market Dynamics in Volatile and Non-Stationary Environments through Sentiment-Conditioned Generative Modelling," Papers, arXiv.org, number 2604.22801, Apr, revised May 2026.
• Filip Blaha & Jan Botka & Josef Sveda & Ales Michl, 2026, "AI-Based Forecasting of Czech Inflation: Quantile Regression Forests with Dynamic Weights," Working Papers, Czech National Bank, Research and Statistics Department, number 2026/09, Apr.
• Thomas Conlon & John Cotter & Iason Kynigakis, 2026, "Machine Learning Forecasts of Asymmetric Betas Using Firm-Specific Information," Papers, arXiv.org, number 2604.22933, Apr.
• Sicco Kooiker & Janneke van Brummelen & Julia Schaumburg & Marcin Zamojski, 2026, "Self-driving neural networks for term structure modeling," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 26-007/III, Feb.
• Satoko Kojima & Toshiyuki Sakiyama, 2026, "Determinants of Liquidity in the Japanese Government Bond Market: An Interpretable Machine Learning Approach," IMES Discussion Paper Series, Institute for Monetary and Economic Studies, Bank of Japan, number 26-E-03, Mar.
• Yikuan Huang & Zheqi Fan & Kaiqi Hu & Yifan Ye, 2026, "From Hypotheses to Factors: Constrained LLM Agents in Cryptocurrency Markets," Papers, arXiv.org, number 2604.26747, Apr.
• Andrey Fradkin & Rohit Krishnan, 2026, "MarketBench: Evaluating AI Agents as Market Participants," Papers, arXiv.org, number 2604.23897, Apr.
• Yeqing Duan & Nils Droste & Brian Danley, 2026, "Modelling land use transition through social learning," IFRO Working Paper, University of Copenhagen, Department of Food and Resource Economics, number 2026/01, Apr.
• Maksym Nechepurenko & Pavel Shuvalov, 2026, "Foresight Arena: An On-Chain Benchmark for Evaluating AI Forecasting Agents," Papers, arXiv.org, number 2605.00420, May, revised May 2026.
• Xiaoyun Qiu & Yang Yu & Haifeng Xu, 2026, "On Benchmark Hacking in ML Contests: Modeling, Insights and Design," Papers, arXiv.org, number 2604.22230, Apr.
• Jonathan Shaki & Eden Hartman & Sarit Kraus & Yonatan Aumann, 2026, "Sustaining Cooperation in Populations Guided by AI: A Folk Theorem for LLMs," Papers, arXiv.org, number 2605.06525, May.
• Knebel-Seitz, Caroline, 2026, "Testing an investment simulation tool: Effects on knowledge, confidence, and motivation," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 26-015.
• Yuhao Fu & Nobuyuki Hanaki & Haitao Wang, 2026, "Do Humans Bargain Differently with AI? Evidence from Alternating-Offer Games," ISER Discussion Paper, Institute of Social and Economic Research, The University of Osaka, number 1311, Apr.
• Tassa Thaksakronwong & Koichi Miyamoto, 2026, "Quantum analog-encoding for correlated Gaussian vectors and their exponentiation with application to rough volatility," Papers, arXiv.org, number 2604.22463, Apr.