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Report NEP-BIG-2026-03-30
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:
- Pei-Jun Liao & Hung-Shin Lee & Yao-Fei Cheng & Li-Wei Chen & Hung-yi Lee & Hsin-Min Wang, 2026, "Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion," Papers, arXiv.org, number 2603.19286, Mar.
- Takashi Kameyama & Masahiro Kato & Yasuko Hio & Yasushi Takano & Naoto Minakawa, 2026, "Causality Elicitation from Large Language Models," Papers, arXiv.org, number 2603.04276, Mar.
- Rahul Billakanti & Minchul Shin, 2026, "At-Risk Transformation for U.S. Recession Prediction," Papers, arXiv.org, number 2603.07813, Mar.
- Shogo Fukui, 2026, "Enhancing the Accuracy of Regional Input-Output Table Estimation: A Deep Learning Approach," Papers, arXiv.org, number 2603.13823, Mar.
- Oleksandr Castello & Marco Corazza, 2026, "Machine Learning techniques for synthetic data generation in Energy and Financial Markets," Working Papers, Department of Economics, University of Venice "Ca' Foscari", number 2026: 11.
- Mahmoud, Mai & Kurdi, Sikandra, 2025, "Targeting of food aid programs: Evidence from Egypt," IFPRI discussion papers, International Food Policy Research Institute (IFPRI), number 2393, Dec.
- Mahmoud, Mai & Kurdi, Sikandra, 2025, "Targeting of food aid programs: Evidence from Egypt," GSSP working papers, International Food Policy Research Institute (IFPRI), number 2393, Dec.
- Haochen Luo & Zhengzhao Lai & Junjie Xu & Yifan Li & Tang Pok Hin & Yuan Zhang & Chen Liu, 2026, "From Natural Language to Executable Option Strategies via Large Language Models," Papers, arXiv.org, number 2603.16434, Mar.
- Hanyong Cho & Geumil Bae & Jang Ho Kim, 2026, "Investor risk profiles of large language models," Papers, arXiv.org, number 2603.09303, Mar.
- Ziqin Gong & Ning Li & Huaikang Zhou, 2026, "LLMs learn scientific taste from institutional traces across the social sciences," Papers, arXiv.org, number 2603.16659, Mar, revised May 2026.