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Report NEP-BIG-2026-04-06
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.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-BIG
The following items were announced in this report:
- Vegard H. Larsen & Leif Anders Thorsrud, 2026, "Using Transformers and Reinforcement Learning as Narrative Filters in Macroeconomics," Working Papers, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School, number No 02/2026, Feb.
- Ricardo Crisostomo & Diana Mykhalyuk, 2026, "Large Language Models and Stock Investing: Is the Human Factor Required?," Papers, arXiv.org, number 2603.19944, Mar, revised Apr 2026.
- Alexander Eliseev & Sergei Seleznev, 2026, "Fake Date Tests: Can We Trust In-sample Accuracy of LLMs in Macroeconomic Forecasting?," Bank of Russia Working Paper Series, Bank of Russia, number wps167, Mar.
- Kemal Kirtac, 2026, "Learning to Aggregate Zero-Shot LLM Agents for Corporate Disclosure Classification," Papers, arXiv.org, number 2603.20965, Mar, revised Apr 2026.
- Martin, Reiner & Klacso, Jan & Mohácsi, Piroska Nagy & Evdokimova, Tatiana & Ponomarenko, Olga, 2026, "Central bank communication on financial stability – A shadowed sibling?," ESRB Working Paper Series, European Systemic Risk Board, number 154, Apr.
- Mehmet Caner & Agostino Capponi & Nathan Sun & Jonathan Y. Tan, 2026, "Designing Agentic AI-Based Screening for Portfolio Investment," Papers, arXiv.org, number 2603.23300, Mar.
- Abdelfatah, Omar Sharafeldin Mohamed, 2026, "AI-Driven Demand Forecasting and Its Impact on Inventory Optimization," SocArXiv, Center for Open Science, number uw57j_v1, Mar, DOI: 10.31219/osf.io/uw57j_v1.
- DiGiuseppe, Matthew & Fu, Xuelong & Flynn, Michael E, 2026, "LLM-Based Measurement of Latent Attributes in Trade Data," SocArXiv, Center for Open Science, number t8wdg_v1, Mar, DOI: 10.31219/osf.io/t8wdg_v1.