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Report NEP-BIG-2024-03-25
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:
- Juan Carlos Escanciano & Ricardo Parra, 2024, "Extending the Scope of Inference About Predictive Ability to Machine Learning Methods," Papers, arXiv.org, number 2402.12838, Feb, revised May 2025.
- Catayoun Azarm & Erman Acar & Mickey van Zeelt, 2024, "On the Potential of Network-Based Features for Fraud Detection," Papers, arXiv.org, number 2402.09495, Feb, revised Feb 2024.
- Sina Montazeri & Akram Mirzaeinia & Amir Mirzaeinia, 2024, "CNN-DRL with Shuffled Features in Finance," Papers, arXiv.org, number 2402.03338, Jan.
- Yifan Duan & Guibin Zhang & Shilong Wang & Xiaojiang Peng & Wang Ziqi & Junyuan Mao & Hao Wu & Xinke Jiang & Kun Wang, 2024, "CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks," Papers, arXiv.org, number 2402.14708, Feb, revised Nov 2024.
- Jeroen Rombouts & Marie Ternes & Ines Wilms, 2024, "Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning," Papers, arXiv.org, number 2402.09033, Feb, revised May 2024.
- Hao Qian & Hongting Zhou & Qian Zhao & Hao Chen & Hongxiang Yao & Jingwei Wang & Ziqi Liu & Fei Yu & Zhiqiang Zhang & Jun Zhou, 2024, "MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction," Papers, arXiv.org, number 2402.06633, Jan.
- Huang, Justin & Kaul, Rupali & Narayanan, Sridhar, 2023, "Novelty in Content Creation: Experimental Results Using Image Recognition on a Large Social Network," Research Papers, Stanford University, Graduate School of Business, number 4040, Nov.
- Kaori Narita & J.D. Tena & Babatunde Buraimo, 2022, "Causal and Consequences of Multiple Dismissals: Evidence from Italian Football League," Working Papers, University of Liverpool, Department of Economics, number 202226, Nov.
- Sarah Soleiman & Julien Randon-Furling & Marie Cottrell, 2022, "Machine Learning and Data-Driven Approaches in Spatial Statistics: A Case Study of Housing Price Estimation," Post-Print, HAL, number hal-03900972, Jul, DOI: 10.1007/978-3-031-15444-7_4.
- Daniel Celeny & Loic Mar'echal, 2024, "Cyber risk and the cross-section of stock returns," Papers, arXiv.org, number 2402.04775, Feb, revised Mar 2024.
- Athey, Susan & Keleher, Niall & Spiess, Jann, 2023, "Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal," Research Papers, Stanford University, Graduate School of Business, number 4146, Oct.
- Andrea Macr`i & Fabrizio Lillo, 2024, "Reinforcement Learning for Optimal Execution when Liquidity is Time-Varying," Papers, arXiv.org, number 2402.12049, Feb, revised Feb 2024.
- Steven Y. K. Wong & Jennifer S. K. Chan & Lamiae Azizi, 2024, "Quantifying neural network uncertainty under volatility clustering," Papers, arXiv.org, number 2402.14476, Feb, revised Sep 2024.
- Chinmaya Behera & Badri Narayan Rath & Pramod Kumar Mishra, 2023, "The Impact of Monetary and Fiscal Stimulus on Stock Returns During the COVID-19 Pandemic," Working Papers, Madras School of Economics,Chennai,India, number 2023-247, Sep.
- Leek, Lauren Caroline & Bischl, Simeon & Freier, Maximilian, 2024, "Introducing Textual Measures of Central Bank Policy-Linkages Using ChatGPT," SocArXiv, Center for Open Science, number 78wnp, Feb, DOI: 10.31219/osf.io/78wnp.