Report NEP-BIG-2025-02-03

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:

• Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2025, "Large Language Models: An Applied Econometric Framework," NBER Working Papers, National Bureau of Economic Research, Inc, number 33344, Jan.
• Jingfeng Chen & Wanlin Deng & Dangxing Chen & Luyao Zhang, 2024, "FinML-Chain: A Blockchain-Integrated Dataset for Enhanced Financial Machine Learning," Papers, arXiv.org, number 2411.16277, Nov.
• Kuan-Ming Liu & Ming-Chih Lo, 2025, "LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading," Papers, arXiv.org, number 2501.09636, Jan, revised Jan 2025.
• Gabriel Okasa & Alberto de Le'on & Michaela Strinzel & Anne Jorstad & Katrin Milzow & Matthias Egger & Stefan Muller, 2024, "A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports," Papers, arXiv.org, number 2411.16662, Nov, revised Dec 2024.
• Adamantios Ntakaris & Gbenga Ibikunle, 2024, "Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks," Papers, arXiv.org, number 2412.16160, Nov, revised Dec 2024.
• Bo Yuan & Damiano Brigo & Antoine Jacquier & Nicola Pede, 2024, "Deep learning interpretability for rough volatility," Papers, arXiv.org, number 2411.19317, Nov.
• Jimmy Cheung & Smruthi Rangarajan & Amelia Maddocks & Xizhe Chen & Rohitash Chandra, 2024, "Quantile deep learning models for multi-step ahead time series prediction," Papers, arXiv.org, number 2411.15674, Nov.
• Yonggai Zhuang & Haoran Chen & Kequan Wang & Teng Fei, 2024, "GRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Network," Papers, arXiv.org, number 2411.18997, Nov.
• Fischer, Manfred M., 2025, "Convolutional Neural Networks:: Key Components and Architectures," Working Papers in Regional Science, WU Vienna University of Economics and Business, number 01.
• Bryan T. Kelly & Boris Kuznetsov & Semyon Malamud & Teng Andrea Xu, 2025, "Artificial Intelligence Asset Pricing Models," NBER Working Papers, National Bureau of Economic Research, Inc, number 33351, Jan.
• Briola, Antonio & Bartolucci, Silvia & Aste, Tomaso, 2025, "HLOB–Information persistence and structure in limit order books," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 126623, Mar.
• Roberto-Rafael Maura-Rivero & Chirag Nagpal & Roma Patel & Francesco Visin, 2025, "Utility-inspired Reward Transformations Improve Reinforcement Learning Training of Language Models," Papers, arXiv.org, number 2501.06248, Jan, revised Feb 2025.
• Qian Yu & Zhen Xu & Zong Ke, 2024, "Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems," Papers, arXiv.org, number 2412.07027, Nov.
• Muhammad Arslan & Christophe Cruz, 2024, "Business-RAG: Information Extraction for Business Insights," Post-Print, HAL, number hal-04862172, Jul, DOI: 10.5220/0012812800003764.
• Chi-Sheng Chen & Ying-Jung Chen, 2025, "Optimizing Supply Chain Networks with the Power of Graph Neural Networks," Papers, arXiv.org, number 2501.06221, Jan.
• Suyeol Yun, 2024, "Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading," Papers, arXiv.org, number 2411.17900, Nov.
• Subhasis Dasgupta & Pratik Satpati & Ishika Choudhary & Jaydip Sen, 2024, "Understanding the Impact of News Articles on the Movement of Market Index: A Case on Nifty 50," Papers, arXiv.org, number 2412.06794, Nov.
• Kelsee Bratley & Alexis Meyer-Cirkel, 2025, "Understanding Agricultural Output in Mozambique: Using remote sensing to initiate a discussion on development," IMF Working Papers, International Monetary Fund, number 2025/009, Jan.
• Haonan Xu & Alessio Brini, 2025, "Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning," Papers, arXiv.org, number 2501.07508, Jan.
• Jiaan Han & Junxiao Chen & Yanzhe Fu, 2024, "CatNet: Controlling the False Discovery Rate in LSTM with SHAP Feature Importance and Gaussian Mirrors," Papers, arXiv.org, number 2411.16666, Nov, revised Jun 2025.
• Robert Novy-Marx & Mihail Z. Velikov, 2025, "AI-Powered (Finance) Scholarship," NBER Working Papers, National Bureau of Economic Research, Inc, number 33363, Jan.
• Filipovska, Elena & Mladenovska, Ana & Bajrami, Merxhan & Dobreva, Jovana & Hillman, Velislava & Lameski, Petre & Zdravevski, Eftim, 2024, "Benchmarking OpenAI's APIs and other Large Language Models for repeatable and efficient question answering across multiple documents," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 126674, Dec.
• Kun Liu & Jin Zhao, 2024, "KACDP: A Highly Interpretable Credit Default Prediction Model," Papers, arXiv.org, number 2411.17783, Nov.
• Jerick Shi & Burton Hollifield, 2024, "Predictive Power of LLMs in Financial Markets," Papers, arXiv.org, number 2411.16569, Nov.
• Ryo Okui & Yutao Sun & Wendun Wang, 2025, "Recovering latent linkage structures and spillover effects with structural breaks in panel data models," Papers, arXiv.org, number 2501.09517, Jan.
• Mahdi Salahshour & Amirahmad Shafiee & Mojtaba Tefagh, 2024, "Joint Combinatorial Node Selection and Resource Allocations in the Lightning Network using Attention-based Reinforcement Learning," Papers, arXiv.org, number 2411.17353, Nov.
• Hamza Bodor & Laurent Carlier, 2025, "Deep Learning Meets Queue-Reactive: A Framework for Realistic Limit Order Book Simulation," Papers, arXiv.org, number 2501.08822, Jan.
• Augusto Gonzalez-Bonorino & Monica Capra & Emilio Pantoja, 2025, "LLMs Model Non-WEIRD Populations: Experiments with Synthetic Cultural Agents," Papers, arXiv.org, number 2501.06834, Jan.
• Ling Chen, 2024, "Risk Management with Feature-Enriched Generative Adversarial Networks (FE-GAN)," Papers, arXiv.org, number 2411.15519, Nov.
• Ashish Ashok Uikey & Zericho Marak & Dhoha Alsaleh & Ruturaj Baber, 2024, "Decoding Intentions to Purchase Organic Food Products in an Emerging Economy via Artificial Neural Networks," Post-Print, HAL, number hal-04861233, Dec, DOI: 10.51599/are.2024.10.04.12.

Report NEP-BIG-2025-02-03

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: