Report NEP-CMP-2026-05-25

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:

• Kirill Zernikov, 2026, "What Does Deep Hedging Actually Learn? Delta Corrections, Regime Fragility, and Symbolic Distillation," Papers, arXiv.org, number 2605.21696, May.
• Zhe Sage Chen & Quanyan Zhu, 2026, "A Theory of Multilevel Interactive Equilibrium in NeuroAI," Papers, arXiv.org, number 2605.10505, May.
• Santiago Picasso, 2026, "Measuring Services Complexity:A Novel Machine Learning Approach Using U.S. Input–Output Data," Documentos de Trabajo (working papers), Department of Economics - dECON, number 0126, Feb.
• Jagdish Tripathy & Marcus Buckmann, 2026, "Fair outputs, Biased Internals: Causal Potency and Asymmetry of Latent Bias in LLMs for High-Stakes Decisions," Papers, arXiv.org, number 2605.15217, May.
• Marco Gregnanin & Johannes De Smedt & Giorgio Gnecco & Maurizio Parton, 2026, "A Generative Adversarial Graph Neural Network for Synthetic Time Series Data," Papers, arXiv.org, number 2605.22215, May.
• Mohammad Jalili Torkamani & Pedro Gomes & Amirmohammad Sadeghnejad & Jason Le, 2026, "Analyzing the Impact of Release Season and Production Budget on Movie Revenue and Profitability," Papers, arXiv.org, number 2605.12551, May.
• Kausar, Shafiya, 2026, "When LLM Signals Hurt: A Coverage-Density Analysis of LLM-Augmented Reinforcement Learning for Stock Trading," SocArXiv, Center for Open Science, number nxvdp_v1, May, DOI: 10.31219/osf.io/nxvdp_v1.
• Kamil Kashif & Robert 'Slepaczuk, 2026, "Deep Reinforcement Learning Framework for Diversified Portfolio Management Across Global Equity Markets," Papers, arXiv.org, number 2605.17307, May.
• Marjan Petreski, 2026, "Pegs, Floats, and Forests: A Machine Learning Revisit of Exchange Rate Regimes and Growth in Transition Economies," Papers, arXiv.org, number 2605.17391, May.
• Werner, Sven & Trotter, Philipp, 2026, "When development finance spurs entrepreneurship: New evidence from 5 million projects using a machine learning classifier," Ruhr Economic Papers, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, number 1205, DOI: 10.4419/96973390.
• Christos Spyridon Koulouris & Carlo Campajola, 2026, "Memory-Induced Supra-Competitive Outcomes Between Deep Reinforcement Learning Agents in Optimal Trade Execution," Papers, arXiv.org, number 2605.20348, May.
• Francesco A. Fabozzi & Dasol Kim & William N. Goetzmann, 2026, "Beyond Sentiment Classification: A Generative Framework for Emotion Intensity Evaluation in Text," Papers, arXiv.org, number 2605.16613, May.
• Qinwen Zhu & Wen Chen & Nicolas Langren'e, 2026, "A deep learning approach for pricing convertible bonds with path-dependent reset and call provisions," Papers, arXiv.org, number 2605.12189, May.
• Jesus Cañas & Emily Kerr, 2024, "Texas firms use AI with little employment impact so far," Dallas Fed Economics, Federal Reserve Bank of Dallas, number 98445, Jun.
• Mathias Mesfin, 2026, "Sequential Structure in Intraday Futures Data: LSTM vs Gradient Boosting on MNQ," Papers, arXiv.org, number 2605.17724, May.
• Lee, Kamwoo & Blankespoor, Brian & Newhouse, David, 2026, "Fine-Scale Spatial Disaggregation of Statistical Data via Graph Neural Networks," Policy Research Working Paper Series, The World Bank, number 11360, Apr.
• Stefano Blando & Giorgio Fagiolo & Mauro Napoletano & Tania Treibich & Andrea Vandin, 2026, "Statistical Model Checking of the Keynes+Schumpeter Model: A Transient Sensitivity Analysis of a Macroeconomic ABM," Papers, arXiv.org, number 2605.10447, May.
• Bjorn Lofdahl Grelsson, 2026, "On the modeling assumptions of Historical Simulation for Value-at-Risk," Papers, arXiv.org, number 2605.10066, May.
• Marco Gregnanin & Johannes De Smedt & Giorgio Gnecco & Maurizio Parton, 2026, "The Statistical Significance of the Inclusion of Graph Neural Networks in the Financial Time Series Forecasting Problem," Papers, arXiv.org, number 2605.21192, May.
• Emmanouil Sofianos & Thierry Betti & Theophilos Papadimitriou & Amélie Barbier-Gauchard & Periklis Gogas, 2026, "Using DSGE and Machine Learning to Forecast Public Debt for France," Post-Print, HAL, number hal-05620169, Mar, DOI: 10.1002/for.70144.
• Aditya Retnanto & Yohan Iddawela & Elaine Tan, 2026, "Automating Evidence Synthesis: A Comparative Evaluation of Large Language Models for Data Extraction," ADB Economics Working Paper Series, Asian Development Bank, number 845, May.
• Lake Yang & Junwei Su & Jingfeng Zeng & Wenhao Lu & Xingzhi Qian & Weitong Zhang & Chuan Wu & Dunhong Jin, 2026, "GeomHerd: A Forward-looking Herding Quantification via Ricci Flow Geometry on Agent Interactive Simulations," Papers, arXiv.org, number 2605.11645, May.
• Eliseo Curcio, 2026, "EnergyAgentBench: Benchmarking LLM Agents on Live Energy Infrastructure Data," Papers, arXiv.org, number 2605.15230, May.
• Dmitry Dagaev & Egor Ivanov & Petr Parshakov & Alexey Savvateev & Gleb Vasiliev, 2026, "Not Yet: Humans Outperform LLMs in a Colonel Blotto Tournament," Papers, arXiv.org, number 2605.22095, May.
• Firmin Ayivodji & Etienne Briand & Kevin Moran & Dalibor Stevanovic, 2026, "Monetary Policy in the Media Spotlight: Sentiments, Signals, and Economic Impact," Working Papers, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, number 26-03, May.
• Runyao Yu & Julia Lin & Derek W. Bunn & Jochen Stiasny & Wentao Wang & Yujie Chen & Tara Esterl & Peter Palensky & Jochen L. Cremer, 2026, "A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting," Papers, arXiv.org, number 2605.09061, May.
• Lin William Cong & Ke Tang & Jingyuan Wang, 2026, "AlphaPortfolio: Goal-Oriented Investment Management Through Deep Reinforcement Learning," NBER Working Papers, National Bureau of Economic Research, Inc, number 35195, May.
• Bektemir Ysmailov, 2026, "GenAI-Based Index of Financial Constraints," Working Papers, Nazarbayev University, Graduate School of Business, number 2026/01, Jan.
• Michele Zampa, 2026, "Following the Crowd: Literature Support and the Capabilities of Autonomous Research Agents," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 14-2026, May.
• Massimo Giannini, 2026, "Nowcasting Italian Municipal Income with Nightlights: A Deep Learning Approach," Papers, arXiv.org, number 2605.08782, May.
• Omar Abdel Haq & Amitabh Chandra & Tomáš Jagelka & Erzo F.P. Luttmer & Joshua Schwartzstein, 2026, "Revealing Life Preferences Through LLMs," NBER Working Papers, National Bureau of Economic Research, Inc, number 35185, May.
• Philippe Goulet Coulombe, 2026, "Quantifying the Risk-Return Tradeoff in Forecasting," Papers, arXiv.org, number 2605.09712, May.
• Sebastian Bell & Ali Kakhbod & Martin Lettau & Abdolreza Nazemi, 2026, "AlphaGlass: Interpretable Characteristic-Based Portfolio Choice," NBER Working Papers, National Bureau of Economic Research, Inc, number 35186, May.

Report NEP-CMP-2026-05-25

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: