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Report NEP-BIG-2026-05-11
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:
- Yusuke Oh & Mototsugu Shintani, 2026, "Forecasting Recessions Using Machine Learning on Text Data and Mixed-Frequency Predictors," IMES Discussion Paper Series, Institute for Monetary and Economic Studies, Bank of Japan, number 26-E-07, Mar.
- Mgomezulu, Wisdom Richard & Thangata, Paul & Mkandawire, Bertha & Amoah, Nana, 2026, "Advancing Predictive Analytics in Child Malnutrition: Machine, Ensemble and Deep Learning Models with Balanced Class Distribution for Early Detection of Stunting and Wasting," 100th Annual Conference, March 23-25, 2026, Wadham College, University of Oxford, Oxford, UK, Agricultural Economics Society (AES), number 397868, Mar, DOI: 10.22004/ag.econ.397868.
- Olivia Zhang & Zhilin Zhang, 2026, "A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective," Papers, arXiv.org, number 2605.05211, Apr.
- Luuk van Maasakkers & Bas Donkers & Dennis Fok, 2025, "What Did I Forget? Basket Analysis for Large Assortments Using Transformers," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 25-071/XII, Dec.
- Jin, Yan, & Charpe, Matthieu, & Mei, Yang, & Li, Zeshuo,, 2026, "Gridded-labour market data in Ghana using remote sensing and random forest," ILO Working Papers, International Labour Organization, number 995694369302676, DOI: 10.54394/00033744.
- Alexis Lazanas & Spyridon Karpouzis, 2026, "Beyond Sequential Prediction: Learning Financial Market Dynamics in Volatile and Non-Stationary Environments through Sentiment-Conditioned Generative Modelling," Papers, arXiv.org, number 2604.22801, Apr, revised May 2026.
- Filip Blaha & Jan Botka & Josef Sveda & Ales Michl, 2026, "AI-Based Forecasting of Czech Inflation: Quantile Regression Forests with Dynamic Weights," Working Papers, Czech National Bank, Research and Statistics Department, number 2026/09, Apr.
- Thomas Conlon & John Cotter & Iason Kynigakis, 2026, "Machine Learning Forecasts of Asymmetric Betas Using Firm-Specific Information," Papers, arXiv.org, number 2604.22933, Apr.
- Satoko Kojima & Toshiyuki Sakiyama, 2026, "Determinants of Liquidity in the Japanese Government Bond Market: An Interpretable Machine Learning Approach," IMES Discussion Paper Series, Institute for Monetary and Economic Studies, Bank of Japan, number 26-E-03, Mar.
- Fan Wu & Anqi Liu & Jing Chen & Yuhua Li, 2026, "Do News and Social Media Tell the Same Story? Constructing and Comparing Sentiment Spillover Networks," Papers, arXiv.org, number 2604.26811, Apr, revised May 2026.
- Baumert, Josef & Heckelei, Thomas & Estes, Lyndon & Storm, Hugo, 2026, "Fusing Generative AI and Economic Modelling to Estimate Field-Level Crop Production in Data-Scarce World Regions," 100th Annual Conference, March 23-25, 2026, Wadham College, University of Oxford, Oxford, UK, Agricultural Economics Society (AES), number 397894, Mar, DOI: 10.22004/ag.econ.397894.