Report NEP-CMP-2026-06-22

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.

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Other reports in NEP-CMP

The following items were announced in this report:

• Tyler H. McCormick, 2026, "Position: Prioritize Identifying Structure, Not Complex Models, for Scientific Discovery," Papers, arXiv.org, number 2606.02632, May.
• Yuqi Li & Siyuan Liu & Bingjun Liu, 2026, "PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance," Papers, arXiv.org, number 2606.06823, Jun.
• Damian Lebied'z & Robert 'Slepaczuk, 2026, "Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning," Papers, arXiv.org, number 2606.04574, Jun, revised Jun 2026.
• Ferrara, Andreas, 2026, "A Practitioner's Guide to Using Large Language Models and Generative AI in Economic History," CAGE Online Working Paper Series, Competitive Advantage in the Global Economy (CAGE), number 810.
• Helyette Geman, 2026, "From NLP to Hype and Financial Bubbles: Integrating News Attention with Bubble Detection Models," Policy briefs on Economic Trends and Policies, Policy Center for the New South, number 2617, Jun.
• Helyette Geman, 2026, "From NLP to Hype and Financial Bubbles: Integrating News Attention with Bubble Detection Models," Policy briefs on Commodities & Energy, Policy Center for the New South, number 2614, Jun.
• Zhu, C. & Wang, X. & Zhang, W., 2026, "(Human) Attention Is (Still) All You Need: Human Oversight Makes Ai-Assisted Social Science," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2643, Jun.
• Eugene Park, 2026, "Reflexivity as Prompt: Does Awareness of Self-Reinforcing Market Dynamics Improve LLMs as Financial Market Forecasters?," Papers, arXiv.org, number 2606.00061, May.
• Yoshiyuki ARATA, 2026, "Predicting Shock Propagation and Uncovering Heterogeneity with Graph Neural Networks," Discussion papers, Research Institute of Economy, Trade and Industry (RIETI), number 26045, May.
• Prashik N. Somkuwar & K. Srinivasan & G. Raghavan, 2026, "Benchmarking Quantum Algorithmic Resilience for CVaR Portfolio Optimization: The Expressibility-Coherence Trade-off," Papers, arXiv.org, number 2606.07727, Jun.
• Andrei Bysik & Robert 'Slepaczuk, 2026, "Machine Learning-Based Bitcoin Trading Under Transaction Costs: Evidence From Walk-Forward Forecasting," Papers, arXiv.org, number 2606.00060, May.
• Bryan T. Kelly & Semyon Malamud & Johannes Schwab & Teng Andrea Xu, 2026, "Scaling Point-in-Time Language Models," NBER Working Papers, National Bureau of Economic Research, Inc, number 35247, May.
• Albanese, Andrea & Marguerit, David, 2026, "Labor-Market Consequences of Cross-Border Employment: A Machine Learning Approach," IZA Discussion Papers, IZA Network @ LISER, number 18674, May.
• David Arbour & Eli Ben-Michael & Avi Feller & Apoorva Lal & Lo-Hua Yuan, 2026, "AI-Assisted Variance Reduction in Randomized Experiments," Papers, arXiv.org, number 2606.08853, Jun.
• Yanchen Jiang & David C. Parkes & Tonghan Wang, 2026, "Duality for Optimal Multi-Item, Multi-Bidder Auction Design: Revenue Certificates through Deep Learning," Papers, arXiv.org, number 2606.10112, Jun.
• Arvind Ashta, 2026, "Artificial Intelligence in Microfinance and Financial Inclusion: Applications, Issues, and Future Directions," Working Papers CEB, ULB -- Universite Libre de Bruxelles, number 26-008, Jun.
• Daniel Cunha Oliveira & Kieran Wood & Stefan Zohren & Mihai Cucuringu & Andr'e Fujita, 2026, "Macro-aware time series forecasting via hierarchical mixed-frequency attention models," Papers, arXiv.org, number 2606.00624, May.
• Enoch Hyunwook Kang, 2026, "A Lecture Note on Offline RL and IRL, Part II: Foundations of Inverse Reinforcement Learning and Dynamic Discrete Choice Models," Papers, arXiv.org, number 2605.30843, May.
• Daniil Mikriukov & Ruoyu Sun & Angelos Stefanidis & Jionglong Su & Zhengyong Jiang, 2026, "Addressing Market Regime Changes and Heavy-Tailed Returns in Portfolio Optimization via Bayesian VAR and Elliptical Black-Litterman," Papers, arXiv.org, number 2606.09104, Jun.
• Jan Rovirosa & Jesse Schmolze, 2026, "Inspectable Neural Markov Models for Non-Stationary Time Series," Papers, arXiv.org, number 2605.30943, May.
• Mingxuan Yi & Vidal Mehra & Jing Chen & John Cartlidge, 2026, "Enhancing Regime Shift Detection Using Unstructured Data: A Study on the Treasury Market," Papers, arXiv.org, number 2605.30363, May.
• Andreas Aigner, 2026, "Hybrid News Sentiment Engine: Real-Time Market Analysis via Adaptive Ensemble Learning on News-Price Pairs," Papers, arXiv.org, number 2606.03457, Jun.
• Yichi Zhang & Ke Zhu & Zhoufan Zhu, 2026, "ReSGA: A Large Tail Risk Model for Learning Value-at-Risk and Expected Shortfall," Papers, arXiv.org, number 2606.04576, Jun.