Report NEP-BIG-2026-06-22

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé (Tom Coupe) issued this report. It is usually issued weekly.

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Other reports in NEP-BIG

The following items were announced in this report:

• 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.
• 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.
• Delgado-Téllez, Mar & Ceglar, Andrej & Spiteri, Sarah & Lebouteiller, Léonore & Vorderobermeier, Nicole, 2026, "Beat the heat, the role of heat waves and droughts in regional EU economies," Working Paper Series, European Central Bank, number 3248, Jun.
• 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.
• 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.
• 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.
• 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.
• Vanesa Jordá & Miguel Niño-Zarazúa, , "Measuring Poverty and Inequality with Reduced Data: A Machine Learning Approach Using Nigerian Household Data," Working Papers, Department of Economics, SOAS University of London, UK, number 275.
• 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.