Report NEP-CMP-2025-07-28

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:

• Altug Aydemir & Cem Cebi, 2025, "Forecasting Budgetary Items in Türkiye Using Deep Learning," Working Papers, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, number 2509.
• Altug Aydemir & Mert Gokcu, 2025, "Does Deep Learning Improve Forecast Accuracy of Crude Oil Prices? Evidence from a Neural Network Approach," CBT Research Notes in Economics, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, number 2511.
• Yuke Zhang, 2025, "Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study," Papers, arXiv.org, number 2505.16136, May.
• Giorgio Alfredo Spedicato & Christophe Dutang & Quentin Guibert, 2025, "Adjusting Manual Rates to Own Experience: Comparing the Credibility Approach to Machine Learning," Post-Print, HAL, number hal-04821310, Jun.
• Kubra Bolukbas & Ertan Tok, 2025, "Machine Learning Applications in Credit Risk Prediction," Working Papers, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, number 2508.
• Junzhe Jiang & Chang Yang & Aixin Cui & Sihan Jin & Ruiyu Wang & Bo Li & Xiao Huang & Dongning Sun & Xinrun Wang, 2025, "FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs," Papers, arXiv.org, number 2505.13533, May.
• Qingyu Li & Chiranjib Mukhopadhyay & Abolfazl Bayat & Ali Habibnia, 2025, "Quantum Reservoir Computing for Realized Volatility Forecasting," Papers, arXiv.org, number 2505.13933, May.
• Gianluca De Nard & Damjan Kostovic, 2025, "Learning the shrinkage intensity: a data-driven approach for risk-optimized portfolios," ECON - Working Papers, Department of Economics - University of Zurich, number 470, May, revised Nov 2025.
• Alexandre d'Aspremont & Simon Ben Arous & Jean-Charles Bricongne & Benjamin Lietti & Baptiste Meunier, 2024, "Satellites turn “concrete”: Tracking cement with satellite data and neural networks," Post-Print, HAL, number hal-05104995, DOI: 10.1016/j.jeconom.2024.105923.
• Millend Roy & Vladimir Pyltsov & Yinbo Hu, 2025, "IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting," Papers, arXiv.org, number 2505.11390, May.
• Item repec:pra:mprapa:125307 is not listed on IDEAS anymore

• Saad, Haythem, 2025, "Hybrid machine learning models for marketing business analytics: A selective review," OSF Preprints, Center for Open Science, number xtahd_v1, May, DOI: 10.31219/osf.io/xtahd_v1.
• Duane, Jackson & Morgan, Ashley & Carter, Emily, 2025, "A Review of Financial Data Analysis Techniques for Unstructured Data in the Deep Learning Era: Methods, Challenges, and Applications," OSF Preprints, Center for Open Science, number gdvbj_v1, Jun, DOI: 10.31219/osf.io/gdvbj_v1.
• Lisa D. Cook, 2025, "AI: A Fed Policymaker's View: A speech at the National Bureau of Economic Research, Summer Institute 2025: Digital Economics and Artificial Intelligence, Cambridge, Massachusetts., July 17, 2025," Speech, Board of Governors of the Federal Reserve System (U.S.), number 101342, Jul.
• Darija Barak & Miguel Costa-Gomes, 2025, "Humans expect rationality and cooperation from LLM opponents in strategic games," Papers, arXiv.org, number 2505.11011, May.
• Li, Mengbing & Shi, Chengchun & Wu, Zhenke & Fryzlewicz, Piotr, 2025, "Testing stationarity and change point detection in reinforcement learning," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 127507, Jun.
• Mahdi Kohan Sefidi, 2025, "Predicting Financial Market Crises using Multilayer Network Analysis and LSTM-based Forecasting of Spillover Effects," Papers, arXiv.org, number 2505.11019, May.
• Eduardo Abi Jaber, 2024, "Simulation of square-root processes made simple: applications to the Heston model," Post-Print, HAL, number hal-04839193, Dec.
• Paker, Meredith & Stephenson, Judy & Wallis, Patrick, 2025, "Predictive modeling the past," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 128852, Jun.
• Yuante Li & Xu Yang & Xiao Yang & Minrui Xu & Xisen Wang & Weiqing Liu & Jiang Bian, 2025, "R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization," Papers, arXiv.org, number 2505.15155, May, revised Sep 2025.
• Andrea Borsato & André Lorentz, 2025, "Public science vs. mission-oriented policies in long-run growth: An agent-based model," Post-Print, HAL, number hal-05092674, Sep, DOI: 10.1016/j.strueco.2025.03.001.
• Achy, Lahcen, 2025, "Repenser la Gestion des Connaissances à l’ère de l’IA Limites cognitives, tensions éthiques et enjeux organisationnels
  [Rethinking Knowledge Management in the AI Era: Cognitive Boundaries, Ethical ," MPRA Paper, University Library of Munich, Germany, number 125311, Jul.
• Anubha Goel & Puneet Pasricha & Martin Magris & Juho Kanniainen, 2025, "Foundation Time-Series AI Model for Realized Volatility Forecasting," Papers, arXiv.org, number 2505.11163, May.
• Perukrishnen Vytelingum & Rory Baggott & Namid Stillman & Jianfei Zhang & Dingqiu Zhu & Tao Chen & Justin Lyon, 2025, "Agent-based Liquidity Risk Modelling for Financial Markets," Papers, arXiv.org, number 2505.15296, May.
• Sander de Vries, 2025, "Measuring Family (Dis)Advantage: Lessons from Detailed Parental Information," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 25-010/V, Feb.
• Asmaa El Ajouri, 2025, "Opportunities and Risks of Artificial Intelligence in Recruitment and Selection
  [Opportunités et risques de l'intelligence artificielle dans le recrutement et la sélection]," Post-Print, HAL, number hal-05086544, DOI: 10.5281/zenodo.15222634.
• Martin Neil Baily & David M. Byrne & Aidan T. Kane & Paul E. Soto, 2025, "Generative AI at the Crossroads: Light Bulb, Dynamo, or Microscope?," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2025-053, Jul, DOI: 10.17016/FEDS.2025.053.

Report NEP-CMP-2025-07-28

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: