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. Stan Miles issued this report. It is usually issued weekly.

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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 2507.12369, arXiv.org, 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) WORMS/25/05, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
• Daniele Ballinari & Jessica Maly, 2025. "FX sentiment analysis with large language models," Working Papers 2025-11, Swiss National Bank.
• Rehim Kılıç, 2025. "Linear and nonlinear econometric models against machine learning models: realized volatility prediction," Finance and Economics Discussion Series 2025-061, Board of Governors of the Federal Reserve System (U.S.).
• 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 2508.11152, arXiv.org.
• Gambara, Matteo & Livieri, Giulia & Pallavicini, Andrea, 2025. "Machine-learning regression methods for American-style path-dependent contracts," LSE Research Online Documents on Economics 128600, London School of Economics and Political Science, LSE Library.
• 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 2507.08584, arXiv.org.
• Diego Vallarino, 2025. "Adaptive Market Intelligence: A Mixture of Experts Framework for Volatility-Sensitive Stock Forecasting," Papers 2508.02686, arXiv.org.
• Orhan Erdem & Ragavi Pobbathi Ashok, 2025. "Artificial Finance: How AI Thinks About Money," Papers 2507.10933, arXiv.org.
• Fabio Baschetti & Giacomo Bormetti & Pietro Rossi, 2025. "Joint deep calibration of the 4-factor PDV model," Papers 2507.09412, arXiv.org.
• 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 y6jh2_v1, Center for Open Science.
• Yueyi Wang & Qiyao Wei, 2025. "Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading," Papers 2508.07408, arXiv.org.
• Jeremy Proz & Martin Huber, 2025. "Machine Learning for Detecting Collusion and Capacity Withholding in Wholesale Electricity Markets," Papers 2508.09885, arXiv.org.
• Vidya Sagar G & Shifat Ali & Siddhartha P. Chakrabarty, 2025. "Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios," Papers 2507.02011, arXiv.org.
• 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 2507.08019, arXiv.org.
• Igor Halperin, 2025. "Prompt-Response Semantic Divergence Metrics for Faithfulness Hallucination and Misalignment Detection in Large Language Models," Papers 2508.10192, arXiv.org.
• Nishan Ranabhat & Behnam Javanparast & David Goerz & Estelle Inack, 2025. "Large-scale portfolio optimization with variational neural annealing," Papers 2507.07159, arXiv.org.
• Jinbo Cai & Wenze Li & Wenjie Wang, 2025. "Electricity Market Predictability: Virtues of Machine Learning and Links to the Macroeconomy," Papers 2507.07477, arXiv.org.
• 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) WORMS/25/06, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
• John Rust & Tianshi Mu & Pranjal Rawat & Chengjun Zhang & Qixuan Zhong, 2025. "Who is More Bayesian: Humans or ChatGPT?," Working Papers gueconwpa~25-25-02, Georgetown University, Department of Economics.
• Ivan Letteri, 2025. "A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books," Papers 2507.14960, arXiv.org.
• Öztürk Kösenciğ, Kamile & Okuyucu, Elif Bahar & Balaban, Özgün, 2024. "Structural Plan Schema Generation Through Generative Adversarial Networks," Other publications TiSEM 011d684a-ee40-426c-a8f2-0, Tilburg University, School of Economics and Management.
• Dixon Domfeh & Saeid Safarveisi, 2025. "CATNet: A geometric deep learning approach for CAT bond spread prediction in the primary market," Papers 2508.10208, arXiv.org.
• Askitas, Nikos, 2025. "The Behavioral Signature of GenAI in Scientific Communication," IZA Discussion Papers 18062, Institute of Labor Economics (IZA).
• Askitas, Nikos, 2025. "Notes on a World with Generative AI," IZA Discussion Papers 18070, Institute of Labor Economics (IZA).
• 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 3093, European Central Bank.
• Harold Gu'eneau & Alain Celisse & Pascal Delange, 2025. "Representation learning with a transformer by contrastive learning for money laundering detection," Papers 2507.08835, arXiv.org.
• Arkadiusz Lipiecki & Kaja Bilinska & Nicolaos Kourentzes & Rafal Weron, 2025. "Stealing Accuracy: Predicting Day-ahead Electricity Prices with Temporal Hierarchy Forecasting (THieF)," Papers 2508.11372, arXiv.org.
• Qizhao Chen, 2025. "Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies," Papers 2508.16378, arXiv.org.
• Ryuji Hashimoto & Kiyoshi Izumi, 2025. "Towards Realistic and Interpretable Market Simulations: Factorizing Financial Power Law using Optimal Transport," Papers 2507.09863, arXiv.org.
• Juchan Kim & Inwoo Tae & Yongjae Lee, 2025. "Estimating Covariance for Global Minimum Variance Portfolio: A Decision-Focused Learning Approach," Papers 2508.10776, arXiv.org.
• Jackson, Emerson Abraham, 2025. "The Evolving Landscape of Artificial Intelligence on Knowledge Acquisition: An Empirical Assessment," MPRA Paper 125529, University Library of Munich, Germany, revised Feb 2025.
• Sipeng Zeng & Xiaoning Wang & Tianshu Sun, 2025. "Artificial Intelligence, Domain AI Readiness, and Firm Productivity," Papers 2508.09634, arXiv.org.