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Report NEP-CMP-2026-03-09
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:
- Vegard H. Larsen & Leif Anders Thorsrud, 2026, "Using Transformers and Reinforcement Learning as Narrative Filters in Macroeconomics," CESifo Working Paper Series, CESifo, number 12454.
- Szymon Lis & Robert 'Slepaczuk & Pawe{l} Sakowski, 2026, "Overreaction as an indicator for momentum in algorithmic trading: A Case of AAPL stocks," Papers, arXiv.org, number 2602.18912, Feb.
- Wentao Zhang & Mingxuan Zhao & Jincheng Gao & Jieshun You & Huaiyu Jia & Yilei Zhao & Bo An & Shuo Sun, 2026, "AlphaForgeBench: Benchmarking End-to-End Trading Strategy Design with Large Language Models," Papers, arXiv.org, number 2602.18481, Feb, revised May 2026.
- Fausch, Jürg & Frigg, Moreno & Ruenzi, Stefan & Weigert, Florian, 2026, "Machine learning mutual fund flows," CFR Working Papers, University of Cologne, Centre for Financial Research (CFR), number 26-03.
- Wenxi Geng & Dingyuan Liu & Liya Li & Yiqing Wang, 2026, "Could Large Language Models work as Post-hoc Explainability Tools in Credit Risk Models?," Papers, arXiv.org, number 2602.18895, Feb, revised May 2026.
- Ronald Richman & Mario V. Wuthrich, 2026, "From Chain-Ladder to Individual Claims Reserving," Papers, arXiv.org, number 2602.15385, Feb, revised Feb 2026.
- Douglas K.G. Araujo & Harald Uhlig, 2026, "How does AI distribute the pie? Large Language Models and the Ultimatum Game," Working Papers, Becker Friedman Institute for Research In Economics, number 2026-29.
- Rilke, Rainer & Sliwka, Dirk, 2026, "When Algorithms Rate Performance: Do Large Language Models Replicate Human Evaluation Biases?," IZA Discussion Papers, IZA Network @ LISER, number 18371, Feb.
- da Silva, Lucas Paulo, 2026, "Measuring Online Media Ideology with Large Language Models and "Multi-Cue Classification"," SocArXiv, Center for Open Science, number zmtqp_v1, Feb, DOI: 10.31219/osf.io/zmtqp_v1.
- Paola Ganum & Tohid Atashbar, 2026, "How Effectively Can Current LLMs Analyze Macrofinancial Issues?," IMF Working Papers, International Monetary Fund, number 2026/035, Feb.
- Aidan Vyas, 2026, "LemonadeBench: Evaluating the Economic Intuition of Large Language Models in Simple Markets," Papers, arXiv.org, number 2602.13209, Jan.
- Klimm, Johanna & Müller, Kathrin & Rößle, Felix, 2026, "Einsatzpotenziale von KI zur Lösung von Controlling-Fallstudien," Rosenheim Papers in Applied Economics and Business Sciences, Rosenheim Technical University of Applied Sciences, number 13/2026.
- Pavel Koptev & Vishnu Kumar & Konstantin Malkov & George Shapiro & Yury Vikhanov, 2026, "Predicting Invoice Dilution in Supply Chain Finance with Leakage Free Two Stage XGBoost, KAN (Kolmogorov Arnold Networks), and Ensemble Models," Papers, arXiv.org, number 2602.15248, Feb.
- Bricongne, Jean-Charles & Meunier, Baptiste & Macalos, Joao & Milis, Julia & Pical, Thomas, 2026, "Can satellites predict oil demand?," Working Paper Series, European Central Bank, number 3198, Feb.
- Ata Keskin, 2026, "Factor Engine: A Python Library for Systematic Financial Factor Computation and Analysis," Papers, arXiv.org, number 2602.14138, Feb.
- Weibels, Sebastian, 2026, "Hard to process: Atypical firms and the cross-section of expected stock returns," CFR Working Papers, University of Cologne, Centre for Financial Research (CFR), number 26-05.
- Leogrande, Angelo & Arnone, Massimo & Drago, Carlo & Costantiello, Alberto & Anobile, Fabio, 2026, "Crime, Trust, and Quality of Life: Determinants of Perceived Insecurity across Italian Regions," SocArXiv, Center for Open Science, number rd2sv_v1, Feb, DOI: 10.31219/osf.io/rd2sv_v1.
- Jiaqi Huang, 2026, "Fixed Effects as Generated Regressors," Papers, arXiv.org, number 2602.08899, Feb.
- Jeremy McEntire, 2026, "Leap+Verify: Regime-Adaptive Speculative Weight Prediction for Accelerating Neural Network Training," Papers, arXiv.org, number 2602.19580, Feb.