Report NEP-CMP-2025-02-24

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:

• Zhouyu Shen & Dacheng Xiu, 2025, "Can Machines Learn Weak Signals?," NBER Working Papers, National Bureau of Economic Research, Inc, number 33421, Jan.
• Yoontae Hwang & Yaxuan Kong & Stefan Zohren & Yongjae Lee, 2025, "Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization," Papers, arXiv.org, number 2502.00828, Feb.
• Di Zhang, 2025, "Efficient Triangular Arbitrage Detection via Graph Neural Networks," Papers, arXiv.org, number 2502.03194, Feb, revised Oct 2025.
• Fernando Perez-Cruz & Hyun Song Shin, 2025, "Putting AI agents through their paces on general tasks," BIS Working Papers, Bank for International Settlements, number 1245, Feb.
• Kubitza, Dennis Oliver & Weßling, Katarina, 2025, "Whole Lotta Training - Studying School-to-Training Transitions by Training Artificial Neural Networks," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 310974.
• Pataranutaporn, Pat & Powdthavee, Nattavudh & Maes, Pattie, 2025, "Can AI Solve the Peer Review Crisis? A Large-Scale Experiment on LLM's Performance and Biases in Evaluating Economics Papers," IZA Discussion Papers, Institute of Labor Economics (IZA), number 17659, Jan.
• Item repec:osf:africa:vpuac_v1 is not listed on IDEAS anymore

• Joshua Rosaler & Luca Candelori & Vahagn Kirakosyan & Kharen Musaelian & Ryan Samson & Martin T. Wells & Dhagash Mehta & Stefano Pasquali, 2025, "Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning," Papers, arXiv.org, number 2502.01495, Feb.
• Marco Bardoscia & Adrian Carro & Marc Hinterschweiger & Mauro Napoletano & Lilit Popoyan & Andrea Roventini & Arzu Uluc, 2024, "The impact of prudential regulations on the UK housing market and economy: insights from an agent-based model," Working Papers, Banco de España, number 2502, Jan, DOI: https://doi.org/10.53479/38913.
• Matthew Caron & Oliver Müller & Johannes Kriebel, 2025, "Detecting and Mitigating Shortcut Learning Bias in Machine Learning: A Pathway to More Generalizable ML-based (IS) Research," Working Papers Dissertations, Paderborn University, Faculty of Business Administration and Economics, number 129, Feb.
• Daniil Karzanov & Rub'en Garz'on & Mikhail Terekhov & Caglar Gulcehre & Thomas Raffinot & Marcin Detyniecki, 2025, "Regret-Optimized Portfolio Enhancement through Deep Reinforcement Learning and Future Looking Rewards," Papers, arXiv.org, number 2502.02619, Feb.
• George Fatouros & Kostas Metaxas & John Soldatos & Manos Karathanassis, 2025, "MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents," Papers, arXiv.org, number 2502.00415, Feb, revised Oct 2025.
• Chowdhury, Koushik, 2023, "Towards a Deep Learning approach to regularise discourse of collaborative learner," Thesis Commons, Center for Open Science, number hjk4b_v1, May, DOI: 10.31219/osf.io/hjk4b_v1.
• Alnajjar, Khalid & Hämäläinen, Mika, 2024, "MLPESTEL: The New Era of Forecasting Change in the Operational Environment of Businesses Using LLMs," Thesis Commons, Center for Open Science, number qz8hk_v1, Oct, DOI: 10.31219/osf.io/qz8hk_v1.
• Felix Drinkall & Janet B. Pierrehumbert & Stefan Zohren, 2025, "When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks," Papers, arXiv.org, number 2502.02199, Feb.
• Chen Ziyi & Gu Jia-wen, 2025, "Exploratory Utility Maximization Problem with Tsallis Entropy," Papers, arXiv.org, number 2502.01269, Feb.
• Iuliia Alekseenko & Dmitry Dagaev & Sofia Paklina & Petr Parshakov, 2025, "Strategizing with AI: Insights from a Beauty Contest Experiment," Papers, arXiv.org, number 2502.03158, Feb, revised Oct 2025.
• Lionel Fontagné & Francesca Micocci & Armando Rungi, 2025, "The heterogeneous impact of the EU-Canada agreement with causal machine learning," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers), HAL, number halshs-04913313, Jan.
• Robin Jarry & Marc Chaumont & Laure Berti-Equille & Gérard Subsol, 2024, "Predicting Socio-economic Indicator Variations with Satellite Image Time Series and Transformer," Post-Print, HAL, number lirmm-04895134, Nov.
• de Avila, Rogerio, 2024, "Utilizing Big Administrative Data in Evaluation Research: Integrating Causal Modeling, Program Theory, and Machine Learning," Thesis Commons, Center for Open Science, number z7der_v1, Nov, DOI: 10.31219/osf.io/z7der_v1.
• Li-Chun Huang, 2024, "NEAT Algorithm-based Stock Trading Strategy with Multiple Technical Indicators Resonance," Papers, arXiv.org, number 2501.14736, Dec.
• Victor Chernozhukov & Mert Demirer & Esther Duflo & Iv'an Fern'andez-Val, 2025, "Comment on "Sequential validation of treatment heterogeneity" and "Comment on generic machine learning inference on heterogeneous treatment effects in randomized experiments"," Papers, arXiv.org, number 2502.01548, Feb, revised Feb 2025.
• Item repec:osf:metaar:npvwr_v1 is not listed on IDEAS anymore

Report NEP-CMP-2025-02-24

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: