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Report NEP-CMP-2026-03-02
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:
- Elliot Beck & Franziska Eckert & Linus Kühne & Helge Liebert & Rina Rosenblatt-Wisch, 2026, "Measuring economic outlook in the news," Working Papers, Swiss National Bank, number 2026-04.
- Pietro Bini & Lin William Cong & Xing Huang & Lawrence J. Jin, 2026, "Behavioral Economics of AI: LLM Biases and Corrections," Papers, arXiv.org, number 2602.09362, Feb.
- Zeping Li & Guancheng Wan & Keyang Chen & Yu Chen & Yiwen Zhao & Philip Torr & Guangnan Ye & Zhenfei Yin & Hongfeng Chai, 2026, "Behavioral Consistency Validation for LLM Agents: An Analysis of Trading-Style Switching through Stock-Market Simulation," Papers, arXiv.org, number 2602.07023, Feb, revised Mar 2026.
- Kunihiro Miyazaki & Takanobu Kawahara & Stephen Roberts & Stefan Zohren, 2026, "Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks," Papers, arXiv.org, number 2602.23330, Feb.
- Krishna Neupane & Prem Sapkota & Ujjwal Prajapati, 2026, "Beyond the Numbers: Causal Effects of Financial Report Sentiment on Bank Profitability," Papers, arXiv.org, number 2602.17851, Feb.
- Yaxuan Kong & Hoyoung Lee & Yoontae Hwang & Alejandro Lopez-Lira & Bradford Levy & Dhagash Mehta & Qingsong Wen & Chanyeol Choi & Yongjae Lee & Stefan Zohren, 2026, "Evaluating LLMs in Finance Requires Explicit Bias Consideration," Papers, arXiv.org, number 2602.14233, Feb.
- Srijan Sood & Kassiani Papasotiriou & Marius Vaiciulis & Tucker Balch, 2026, "Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization," Papers, arXiv.org, number 2602.17098, Feb.
- Sean Cao & Wei Jiang & Hui Xu, 2026, "Seeing the Goal, Missing the Truth: Human Accountability for AI Bias," Papers, arXiv.org, number 2602.09504, Feb.
- Sumin Kim & Jihoon Kwon & Yoon Kim & Nicole Kagan & Raffi Khatchadourian & Wonbin Ahn & Alejandro Lopez-Lira & Jaewon Lee & Yoontae Hwang & Oscar Levy & Yongjae Lee & Chanyeol Choi, 2026, "Forecasting Future Language: Context Design for Mention Markets," Papers, arXiv.org, number 2602.21229, Feb, revised Feb 2026.
- Omri Feldman & Amar Venugopal & Jann Spiess & Amir Feder, 2026, "Causal Effect Estimation with Latent Textual Treatments," Papers, arXiv.org, number 2602.15730, Feb.
- Hainaut, Donatien, 2025, "In-processing of actuarial and equity fairness constraints for Neural networks," LIDAM Discussion Papers ISBA, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), number 2025011, May.
- Masahiro Kato, 2026, "genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression," Papers, arXiv.org, number 2602.17543, Feb.
- Thomas R. Cook & Sophia Kazinnik & Zach Modig & Nathan M. Palmer, 2026, "What Do LLMs Want?," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2026-006, Jan, DOI: 10.17016/FEDS.2026.006.
- Lauren Cohen & Yiwen Lu & Quoc H. Nguyen, 2026, "Mimicking Finance," NBER Working Papers, National Bureau of Economic Research, Inc, number 34849, Feb.
- Gautami Parate & Arpita Choudhary, 2026, "Patent Valuation under Fragile Institutional Enforcement: A Continuous-Time Markov Approach," Working Papers, Madras School of Economics,Chennai,India, number 2026-293, Jan.
- Paolo Pellizzari & Francesca Parpinel, 2026, "Illusions and Perceived Wealth: an Agent-based model of Madoff's Ponzi scheme," Working Papers, Department of Economics, University of Venice "Ca' Foscari", number 2026: 04.
- Stefano Scoleri & Marco Bianchetti & Sergei Kucherenko, 2026, "Application of Quasi Monte Carlo and Global Sensitivity Analysis to Option Pricing and Greeks," Papers, arXiv.org, number 2602.14354, Feb.
- Caleb Maresca, 2026, "Can Interest-Bearing Positions Solve the Long-Horizon Problem in Prediction Markets?," Papers, arXiv.org, number 2602.21091, Feb.
- Yijie Wang & Hao Gao & Campbell R. Harvey & Yan Liu & Xinyuan Tao, 2026, "Machine Learning Meets Markowitz," NBER Working Papers, National Bureau of Economic Research, Inc, number 34861, Feb.
- Kemper, Jan & Rostam-Afschar, Davud, 2026, "Earning While Learning: How to Run Batched Bandit Experiments," GLO Discussion Paper Series, Global Labor Organization (GLO), number 1717.
- Stephan Ludwig & Peter J. Danaher & Xiaohao Yang & Yu-Ting Lin & Ehsan Abedin & Dhruv Grewal & Lan Du, 2026, "Extracting Consumer Insight from Text: A Large Language Model Approach to Emotion and Evaluation Measurement," Papers, arXiv.org, number 2602.15312, Feb.
- Li, Yuxuan & Zhou, Yuqin & Huang, Jun & Xie, Lin & Huang, Hancheng, 2026, "Bitcoin ETFs and structural decoupling in the cryptocurrency market: evidence from altcoin correlation dynamics," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 137306, Feb.
- Moon Duchin & Kristopher Tapp, 2026, "Metric geometry for ranking-based voting: Tools for learning electoral structure," Papers, arXiv.org, number 2602.10293, Feb.
- German Nova Orozco & Duy-Minh Dang & Peter A. Forsyth, 2026, "Money-Back Tontines for Retirement Decumulation: Neural-Network Optimization under Systematic Longevity Risk," Papers, arXiv.org, number 2602.16212, Feb.
- Yiqing Xu & Leo Yang Yang, 2026, "Scaling Reproducibility: An AI-Assisted Workflow for Large-Scale Replication and Reanalysis," Papers, arXiv.org, number 2602.16733, Feb, revised Mar 2026.