Report NEP-CMP-2026-04-13

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:

• Anne Lundgaard Hansen, 2026, "Validating Large Language Model Annotations," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2026-020, Mar, DOI: 10.17016/FEDS.2026.020.
• Helal, Al Mansor & Hiraki, Ryotaro & Patrinos, Harry Anthony, 2026, "Returns to Education in the United States: A Comparison of OLS and Double Machine Learning Methods," GLO Discussion Paper Series, Global Labor Organization (GLO), number 1733.
• Sylvain K. Assienin & Auguste K. Kouakou & Christian K. Nda & Loukou L. E. Yobouet, 2025, "School Governance and Learner Performance in Sub-Saharan Africa: A Neural Networks Approach," Post-Print, HAL, number hal-05547822, Aug, DOI: 10.5539/ijef.v17n9p90.
• Abdelfatah, Omar Sharafeldin Mohamed, 2026, "Machine Learning Approaches for Improving Demand Forecasting Accuracy in Retail Supply Chains," SocArXiv, Center for Open Science, number 4z9be_v1, Apr, DOI: 10.31219/osf.io/4z9be_v1.
• Zhenyu Gao & Wenxi Jiang & Yutong Yan, 2026, "Debiasing LLMs by Fine-tuning," Papers, arXiv.org, number 2604.02921, Apr, revised May 2026.
• van Loon, Austin & Kanopka, Klint, 2026, "Using large language models as a source of human behavioral data in social science experiments," SocArXiv, Center for Open Science, number y74mu_v1, Apr, DOI: 10.31219/osf.io/y74mu_v1.
• Easton K. Huch & Michael P. Keane, 2026, "Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks," NBER Working Papers, National Bureau of Economic Research, Inc, number 35037, Apr.
• Hui Gong & Akash Sedai & Thomas Schroeder & Francesca Medda, 2026, "Quantum Computing for Financial Transformation: A Review of Optimisation, Pricing, Risk, Machine Learning, and Post-Quantum Security," Papers, arXiv.org, number 2604.08180, Apr.
• Rajat M. Barot & Arjun S. Borkhatariya, 2026, "PolySwarm: A Multi-Agent Large Language Model Framework for Prediction Market Trading and Latency Arbitrage," Papers, arXiv.org, number 2604.03888, Apr.
• Daniel Bloch, 2026, "Anticipatory Reinforcement Learning: From Generative Path-Laws to Distributional Value Functions," Papers, arXiv.org, number 2604.04662, Apr.
• Daron Acemoglu & Tianyi Lin & Asuman Ozdaglar & James Siderius, 2026, "How AI Aggregation Affects Knowledge," Papers, arXiv.org, number 2604.04906, Apr.
• Alexandre Alouadi & Gr'egoire Loeper & C'elian Marsala & Othmane Mazhar & Huy^en Pham, 2026, "SBBTS: A Unified Schr\"odinger-Bass Framework for Synthetic Financial Time Series," Papers, arXiv.org, number 2604.07159, Apr.
• Karmanpartap Singh Sidhu & Pranshi Saxena, 2026, "Beyond Black-Scholes: A Computational Framework for Option Pricing Using Heston, GARCH, and Jump Diffusion Models," Papers, arXiv.org, number 2604.06068, Apr.
• Daron Acemoglu & Tianyi Lin & Asuman Ozdaglar & James Siderius, 2026, "How AI Aggregation Affects Knowledge," NBER Working Papers, National Bureau of Economic Research, Inc, number 35036, Apr.
• Abhinav Das & Stephan Schlüter & Lorenz Schneider, 2026, "Regime-aware conditional neural processes with multi-criteria decision support for operational electricity price forecasting," Post-Print, HAL, number hal-05562231, May, DOI: 10.1016/j.eneco.2026.109233.
• Luigi Caputi & Nicholas Meadows, 2026, "Financial Anomaly Detection for the Canadian Market," Papers, arXiv.org, number 2604.02549, Apr.
• Jimez, Bruno Oliveira Costa, 2026, "Reproducibility: an open-source tool for computational hypothesis testing in natural language," MetaArXiv, Center for Open Science, number 7enxu_v1, Mar, DOI: 10.31219/osf.io/7enxu_v1.
• Tashreef Muhammad & Tahsin Ahmed & Meherun Farzana & Md. Mahmudul Hasan & Abrar Eyasir & Md. Emon Khan & Mahafuzul Islam Shawon & Ferdous Mondol & Mahmudul Hasan & Muhammad Ibrahim, 2026, "A Benchmark of Classical and Deep Learning Models for Agricultural Commodity Price Forecasting on A Novel Bangladeshi Market Price Dataset," Papers, arXiv.org, number 2604.06227, Mar.
• Kazim, Zeeshan, 2026, "Intraday Decision Support for Traders: Explainable CNN-Based Directional Price Forecasting from Candlestick Chart Images," SocArXiv, Center for Open Science, number btnz8_v1, Mar, DOI: 10.31219/osf.io/btnz8_v1.
• Gaskin, Thomas & Demirel, Guven & Wolfram, Marie-Therese & Duncan, Andrew, 2026, "Modelling global trade with optimal transport," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 137330, Feb.