Report NEP-CMP-2025-09-01

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:

• David Imhof & Emanuel W Viklund & Martin Huber, 2025, "Catching Bid-rigging Cartels with Graph Attention Neural Networks," Papers, arXiv.org, number 2507.12369, Jul, revised Jul 2025.
• Jieyu Chen & Sebastian Lerch & Melanie Schienle & Tomasz Serafin & Rafal Weron, 2025, "Probabilistic intraday electricity price forecasting using generative machine learning," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/25/05.
• Daniele Ballinari & Jessica Maly, 2025, "FX sentiment analysis with large language models," Working Papers, Swiss National Bank, number 2025-11.
• Rehim Kılıç, 2025, "Linear and nonlinear econometric models against machine learning models: realized volatility prediction," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2025-061, Aug, DOI: 10.17016/FEDS.2025.061.
• Tianjiao Zhao & Jingrao Lyu & Stokes Jones & Harrison Garber & Stefano Pasquali & Dhagash Mehta, 2025, "AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions," Papers, arXiv.org, number 2508.11152, Aug.
• Gambara, Matteo & Livieri, Giulia & Pallavicini, Andrea, 2025, "Machine-learning regression methods for American-style path-dependent contracts," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 128600, Jun.
• Dimitrios Emmanoulopoulos & Ollie Olby & Justin Lyon & Namid R. Stillman, 2025, "To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions," Papers, arXiv.org, number 2507.08584, Jul.
• Diego Vallarino, 2025, "Adaptive Market Intelligence: A Mixture of Experts Framework for Volatility-Sensitive Stock Forecasting," Papers, arXiv.org, number 2508.02686, Jul.
• Orhan Erdem & Ragavi Pobbathi Ashok, 2025, "Artificial Finance: How AI Thinks About Money," Papers, arXiv.org, number 2507.10933, Jul.
• Fabio Baschetti & Giacomo Bormetti & Pietro Rossi, 2025, "Joint deep calibration of the 4-factor PDV model," Papers, arXiv.org, number 2507.09412, Jul.
• Edmunds, Scott C & Nogoy, Nicole & Lan, Qing & Zhang, Hongfang & Fan, Yannan & Zhou, Hongling & Armit, Chris J, 2025, "Integrating Machine Learning Standards in Disseminating Machine Learning Research," MetaArXiv, Center for Open Science, number y6jh2_v1, Aug, DOI: 10.31219/osf.io/y6jh2_v1.
• Yueyi Wang & Qiyao Wei, 2025, "Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading," Papers, arXiv.org, number 2508.07408, Aug.
• Jeremy Proz & Martin Huber, 2025, "Machine Learning for Detecting Collusion and Capacity Withholding in Wholesale Electricity Markets," Papers, arXiv.org, number 2508.09885, Aug, revised Dec 2025.
• Vidya Sagar G & Shifat Ali & Siddhartha P. Chakrabarty, 2025, "Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios," Papers, arXiv.org, number 2507.02011, Jul.
• Aryan Varshney & Venkat Ram Reddy Ganuthula, 2025, "Signal or Noise? Evaluating Large Language Models in Resume Screening Across Contextual Variations and Human Expert Benchmarks," Papers, arXiv.org, number 2507.08019, Jul.
• Igor Halperin, 2025, "Prompt-Response Semantic Divergence Metrics for Faithfulness Hallucination and Misalignment Detection in Large Language Models," Papers, arXiv.org, number 2508.10192, Aug.
• Nishan Ranabhat & Behnam Javanparast & David Goerz & Estelle Inack, 2025, "Large-scale portfolio optimization with variational neural annealing," Papers, arXiv.org, number 2507.07159, Jul.
• Jinbo Cai & Wenze Li & Wenjie Wang, 2025, "Electricity Market Predictability: Virtues of Machine Learning and Links to the Macroeconomy," Papers, arXiv.org, number 2507.07477, Jul.
• Arkadiusz Lipiecki & Kaja Bilinska & Nikolaos Kourentzes & Rafal Weron, 2025, "Stealing accuracy: Predicting day-ahead electricity prices with Temporal Hierarchy Forecasting (THieF)," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/25/06.
• John Rust & Tianshi Mu & Pranjal Rawat & Chengjun Zhang & Qixuan Zhong, 2025, "Who is More Bayesian: Humans or ChatGPT?," Working Papers, Georgetown University, Department of Economics, number gueconwpa~25-25-02, Jul.
• Ivan Letteri, 2025, "A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books," Papers, arXiv.org, number 2507.14960, Jul.
• Öztürk Kösenciğ, Kamile & Okuyucu, Elif Bahar & Balaban, Özgün, 2024, "Structural Plan Schema Generation Through Generative Adversarial Networks," Other publications TiSEM, Tilburg University, School of Economics and Management, number 011d684a-ee40-426c-a8f2-0.
• Dixon Domfeh & Saeid Safarveisi, 2025, "CATNet: A geometric deep learning approach for CAT bond spread prediction in the primary market," Papers, arXiv.org, number 2508.10208, Aug.
• Askitas, Nikos, 2025, "The Behavioral Signature of GenAI in Scientific Communication," IZA Discussion Papers, Institute of Labor Economics (IZA), number 18062, Aug.
• Askitas, Nikos, 2025, "Notes on a World with Generative AI," IZA Discussion Papers, Institute of Labor Economics (IZA), number 18070, Aug.
• Ca' Zorzi, Michele & Manu, Ana-Simona & Lopardo, Gianluigi, 2025, "Verba volant, transcripta manent: what corporate earnings calls reveal about the AI stock rally," Working Paper Series, European Central Bank, number 3093, Aug.
• Harold Gu'eneau & Alain Celisse & Pascal Delange, 2025, "Representation learning with a transformer by contrastive learning for money laundering detection," Papers, arXiv.org, number 2507.08835, Jul.
• Arkadiusz Lipiecki & Kaja Bilinska & Nicolaos Kourentzes & Rafal Weron, 2025, "Stealing Accuracy: Predicting Day-ahead Electricity Prices with Temporal Hierarchy Forecasting (THieF)," Papers, arXiv.org, number 2508.11372, Aug.
• Qizhao Chen, 2025, "Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies," Papers, arXiv.org, number 2508.16378, Aug.
• Ryuji Hashimoto & Kiyoshi Izumi, 2025, "Towards Realistic and Interpretable Market Simulations: Factorizing Financial Power Law using Optimal Transport," Papers, arXiv.org, number 2507.09863, Jul.
• Juchan Kim & Inwoo Tae & Yongjae Lee, 2025, "Estimating Covariance for Global Minimum Variance Portfolio: A Decision-Focused Learning Approach," Papers, arXiv.org, number 2508.10776, Aug.
• Jackson, Emerson Abraham, 2025, "The Evolving Landscape of Artificial Intelligence on Knowledge Acquisition: An Empirical Assessment," MPRA Paper, University Library of Munich, Germany, number 125529, Jan, revised Feb 2025.
• Sipeng Zeng & Xiaoning Wang & Tianshu Sun, 2025, "Artificial Intelligence, Domain AI Readiness, and Firm Productivity," Papers, arXiv.org, number 2508.09634, Aug.

Report NEP-CMP-2025-09-01

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: