Report NEP-CMP-2026-01-26

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:

• Yichen Luo & Yebo Feng & Jiahua Xu & Yang Liu, 2026, "Resisting Manipulative Bots in Meme Coin Copy Trading: A Multi-Agent Approach with Chain-of-Thought Reasoning," Papers, arXiv.org, number 2601.08641, Jan, revised Feb 2026.
• Ahmed Khwaja & Sonal Srivastava, 2026, "Reinforcement Learning Based Computationally Efficient Conditional Choice Simulation Estimation of Dynamic Discrete Choice Models," Papers, arXiv.org, number 2601.02069, Jan.
• Schmidt, Lorenz & Ritter, Matthias & Mußhoff, Oliver & Odening, Martin, 2025, "Can Machine Learning Improve the Design of Set-Aside Auctions?," 2025 AAEA & WAEA Joint Annual Meeting, July 27-29, 2025, Denver, CO, Agricultural and Applied Economics Association, number 360670, DOI: 10.22004/ag.econ.360670.
• Benjamin Avanzi & Matthew Lambrianidis & Greg Taylor & Bernard Wong, 2025, "On the use of case estimate and transactional payment data in neural networks for individual loss reserving," Papers, arXiv.org, number 2601.05274, Dec.
• Chawla, Parth & Taylor, J. Edward, 2025, "Predicting Mexico-to-US Migration with Machine Learning for Counterfactual Analysis," 2025 AAEA & WAEA Joint Annual Meeting, July 27-29, 2025, Denver, CO, Agricultural and Applied Economics Association, number 361223, DOI: 10.22004/ag.econ.361223.
• Tianyu Fan & Yuhao Yang & Yangqin Jiang & Yifei Zhang & Yuxuan Chen & Chao Huang, 2025, "AI-Trader: Benchmarking Autonomous Agents in Real-Time Financial Markets," Papers, arXiv.org, number 2512.10971, Nov.
• Zhiming Lian, 2026, "Instruction Finetuning LLaMA-3-8B Model Using LoRA for Financial Named Entity Recognition," Papers, arXiv.org, number 2601.10043, Jan.
• Cen, Huang & Wanying, Liao & He, Leng & Sheetal, Abhishek, 2026, "Replication Study on “Machine Learning from a ‘Universe’ of Signals: The Role of Feature Engineering” (Li et al., 2025)," SocArXiv, Center for Open Science, number 3fh8x_v2, Jan, DOI: 10.31219/osf.io/3fh8x_v2.
• Rainer Michael Rilke & Dirk Sliwka, 2026, "When Algorithms Rate Performance: Do Large Language Models Replicate Human Evaluation Biases?," ECONtribute Discussion Papers Series, University of Bonn and University of Cologne, Germany, number 384, Jan.
• Eren Kurshan & Tucker Balch & David Byrd, 2025, "The Agentic Regulator: Risks for AI in Finance and a Proposed Agent-based Framework for Governance," Papers, arXiv.org, number 2512.11933, Dec.
• Sayed Akif Hussain & Chen Qiu-shi & Syed Amer Hussain & Syed Atif Hussain & Asma Komal & Muhammad Imran Khalid, 2026, "Improving Financial Forecasting with a Synergistic LLM-Transformer Architecture: A Hybrid Approach to Stock Price Prediction," Papers, arXiv.org, number 2601.02878, Jan.
• Agust'in M. de los Riscos & Julio E. Sandubete & Diego Carmona-Fern'andez & Le'on Bele~na, 2025, "Empirical Mode Decomposition and Graph Transformation of the MSCI World Index: A Multiscale Topological Analysis for Graph Neural Network Modeling," Papers, arXiv.org, number 2512.12526, Dec.
• Shengyu Cao & Ming Hu, 2026, "LLM Collusion," Papers, arXiv.org, number 2601.01279, Jan.
• Nikoleta Anesti & Edward Hill & Andreas Joseph, 2025, "Inflation Attitudes of Large Language Models," Papers, arXiv.org, number 2512.14306, Dec.
• Pablo Hidalgo & Julio E. Sandubete & Agust'in Garc'ia-Garc'ia, 2025, "Explainable Prediction of Economic Time Series Using IMFs and Neural Networks," Papers, arXiv.org, number 2512.12499, Dec.
• Essakkat, Kaouter & Wu, Linghui & Atallah, Shady S. & Khanna, Madhu, 2025, "Spatial-Dynamic Adoption of AI Weeding Robots: Insights from A Choice Experiment and an Agent-Based Model," 2025 AAEA & WAEA Joint Annual Meeting, July 27-29, 2025, Denver, CO, Agricultural and Applied Economics Association, number 361092, DOI: 10.22004/ag.econ.361092.
• Andrea Conti & Giacomo Morelli, 2025, "Transfer Learning (Il)liquidity," Papers, arXiv.org, number 2512.11731, Dec, revised Feb 2026.
• Zhimin Chen & Bryan T. Kelly & Semyon Malamud, 2025, "Limits To (Machine) Learning," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 25-106, Dec.
• Yu Liu & Wenwen Li & Yifan Dou & Guangnan Ye, 2025, "How AI Agents Follow the Herd of AI? Network Effects, History, and Machine Optimism," Papers, arXiv.org, number 2512.11943, Dec.
• Jaisal Patel & Yunzhe Chen & Kaiwen He & Keyi Wang & David Li & Kairong Xiao & Xiao-Yang Liu, 2025, "Reasoning Models Ace the CFA Exams," Papers, arXiv.org, number 2512.08270, Dec.
• Adler, Brian & Brown, Anne, 2026, "Predicting St. Louis Housing Prices with Machine Learning on Market and Assessor Data," SocArXiv, Center for Open Science, number s9v4u_v1, Jan, DOI: 10.31219/osf.io/s9v4u_v1.
• Kieran A. Malandain & Selim Kalici & Hakob Chakhoyan, 2025, "DeepSVM: Learning Stochastic Volatility Models with Physics-Informed Deep Operator Networks," Papers, arXiv.org, number 2512.07162, Dec.
• Anna Perekhodko & Robert 'Slepaczuk, 2025, "Stochastic Volatility Modelling with LSTM Networks: A Hybrid Approach for S&P 500 Index Volatility Forecasting," Papers, arXiv.org, number 2512.12250, Dec.
• Jakob Bjelac & Victor Chernozhukov & Phil-Adrian Klotz & Jannis Kueck & Theresa M. A. Schmitz, 2026, "Automatic debiased machine learning and sensitivity analysis for sample selection models," Papers, arXiv.org, number 2601.08643, Jan.
• Gongao Zhang & Haijiang Zeng & Lu Jiang, 2026, "Uni-FinLLM: A Unified Multimodal Large Language Model with Modular Task Heads for Micro-Level Stock Prediction and Macro-Level Systemic Risk Assessment," Papers, arXiv.org, number 2601.02677, Jan.
• Ziheng Chen & Minxuan Hu & Jiayu Yi & Wenxi Sun, 2026, "Reinforcement Learning for Option Hedging: Static Implied-Volatility Fit versus Shortfall-Aware Performance," Papers, arXiv.org, number 2601.01709, Jan.
• Kieran Wood & Stephen J. Roberts & Stefan Zohren, 2026, "DeePM: Regime-Robust Deep Learning for Systematic Macro Portfolio Management," Papers, arXiv.org, number 2601.05975, Jan.
• Gabriel Saco, 2025, "Ill-Conditioned Orthogonal Scores in Double Machine Learning," Papers, arXiv.org, number 2512.07083, Dec, revised Jan 2026.
• Sahaj Raj Malla & Shreeyash Kayastha & Rumi Suwal & Harish Chandra Bhandari & Rajendra Adhikari, 2026, "XGBoost Forecasting of NEPSE Index Log Returns with Walk Forward Validation," Papers, arXiv.org, number 2601.08896, Jan.
• Chang Liu, 2025, "Unveiling Hedge Funds: Topic Modeling and Sentiment Correlation with Fund Performance," Papers, arXiv.org, number 2512.06620, Dec.
• James Alm & Rida Belahouaoui, 2025, "Emerging Trends in Tax Fraud Detection Using Artificial Intelligence-Based Technologies," Working Papers, Tulane University, Department of Economics, number 2511, Nov.
• Shuyuan Chen & Peng Zhang & Yifan Cui, 2026, "Double Machine Learning of Continuous Treatment Effects with General Instrumental Variables," Papers, arXiv.org, number 2601.01471, Jan.
• Antoine Bouët & Lionel Fontagné & Christophe Gouel & Houssein Guimbard & Cristina Mitaritonna & Balthazar de Vaulchier & Yu Zheng, 2026, "MIRAGE Model Documentation Version 2.0," Working Papers, CEPII research center, number 2026-01, Jan.

Report NEP-CMP-2026-01-26

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: