Report NEP-BIG-2024-09-23

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:

• Tom Coupé, 2024, "Revealed Preferences: ChatGPT’s Opinion on Economic Issues and the Economics Profession," Working Papers in Economics, University of Canterbury, Department of Economics and Finance, number 24/13, Aug.
• Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024, "Should Central Banks Care About Text Mining? A Literature Review," Working papers, Banque de France, number 950.
• Tarek A. Hassan & Stephan Hollander & Aakash Kalyani & Markus Schwedeler & Ahmed Tahoun & Laurence van Lent, 2024, "Text as Data in Economic Analysis," Working Papers, Federal Reserve Bank of St. Louis, number 2024-022, Sep, revised 11 Sep 2025, DOI: 10.20955/wp.2024.022.
• Lucas Z. Zhang, 2024, "Continuous difference-in-differences with double/debiased machine learning," Papers, arXiv.org, number 2408.10509, Aug, revised Dec 2025.
• Domenico Moramarco & Paolo Brunori & Pedro Salas-Rojo, 2024, "Biases in inequality of opportunity estimates: measures and solutions," Working Papers, ECINEQ, Society for the Study of Economic Inequality, number 675, Sep.
• Sina Montazeri & Haseebullah Jumakhan & Sonia Abrasiabian & Amir Mirzaeinia, 2024, "Gradient Reduction Convolutional Neural Network Policy for Financial Deep Reinforcement Learning," Papers, arXiv.org, number 2408.11859, Aug.
• Sid Bhatia & Sidharth Peri & Sam Friedman & Michelle Malen, 2024, "High-Frequency Trading Liquidity Analysis | Application of Machine Learning Classification," Papers, arXiv.org, number 2408.10016, Aug.
• Lijuan Wang & Yijia Hu & Yan Zhou, 2024, "Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis," Papers, arXiv.org, number 2408.12115, Aug.
• Daniel Souza & Aldo Geuna & Jeff Rodr'iguez, 2024, "How Small is Big Enough? Open Labeled Datasets and the Development of Deep Learning," Papers, arXiv.org, number 2408.10359, Aug.
• Yuntao Wu & Jiayuan Guo & Goutham Gopalakrishna & Zissis Poulos, 2024, "Deep-MacroFin: Informed Equilibrium Neural Network for Continuous Time Economic Models," Papers, arXiv.org, number 2408.10368, Aug, revised May 2025.
• Soren Bettels & Stefan Weber, 2024, "An Integrated Approach to Importance Sampling and Machine Learning for Efficient Monte Carlo Estimation of Distortion Risk Measures in Black Box Models," Papers, arXiv.org, number 2408.02401, Aug, revised Aug 2025.
• Stephan Hetzenecker & Maximilian Osterhaus, 2024, "Deep Learning for the Estimation of Heterogeneous Parameters in Discrete Choice Models," Papers, arXiv.org, number 2408.09560, Aug.
• Zheng Cao, 2024, "Stochastic Calculus for Option Pricing with Convex Duality, Logistic Model, and Numerical Examination," Papers, arXiv.org, number 2408.05672, Aug.
• Domenico Moramarco & Paolo Brunori & Pedro Salas-Rojo, 2024, "Biases in inequality of opportunity estimates: measures and solutions," SERIES, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", number 02-2024, Aug, revised Aug 2024.
• Abhinandan Dalal & Patrick Blobaum & Shiva Kasiviswanathan & Aaditya Ramdas, 2024, "Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters," Papers, arXiv.org, number 2408.09598, Aug, revised Sep 2024.
• Jimin Huang & Mengxi Xiao & Dong Li & Zihao Jiang & Yuzhe Yang & Yifei Zhang & Lingfei Qian & Yan Wang & Xueqing Peng & Yang Ren & Ruoyu Xiang & Zhengyu Chen & Xiao Zhang & Yueru He & Weiguang Han & S, 2024, "Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications," Papers, arXiv.org, number 2408.11878, Aug, revised Jun 2025.
• Federico Daniel Forte, 2024, "Argentina | Pronóstico de inflación de corto plazo con modelos Random Forest
  [Argentina | Forecasting short-term inflation with Random Forest Models]," Working Papers, BBVA Bank, Economic Research Department, number 24/10, Sep.
• CJ Finnegan & James F. McCann & Salissou Moutari, 2024, "Less is more: AI Decision-Making using Dynamic Deep Neural Networks for Short-Term Stock Index Prediction," Papers, arXiv.org, number 2408.11740, Aug.
• Simon D Angus & Lachlan O'Neill, 2024, "Paired Completion: Flexible Quantification of Issue-framing at Scale with LLMs," Papers, arXiv.org, number 2408.09742, Aug, revised Jun 2025.
• Zitian Gao & Yihao Xiao, 2024, "Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method," Papers, arXiv.org, number 2408.09420, Aug, revised Mar 2025.
• Julia M. Puaschunder, 2024, "Knowledge in the 21st Century: Making Sense of Big Data," RAIS Conference Proceedings 2022-2026, Research Association for Interdisciplinary Studies, number 0386, Jul.
• Mathias Valla, 2024, "A Longitudinal Tree-Based Framework for Lapse Management in Life Insurance," Post-Print, HAL, number hal-04178278, Aug.
• Jianqing Fan & Weining Wang & Yue Zhao, 2024, "Conditional nonparametric variable screening by neural factor regression," Papers, arXiv.org, number 2408.10825, Aug.
• Julia Schmidt & Graham Pilgrim & Annabelle Mourougane, 2024, "Measuring the demand for AI skills in the United Kingdom," OECD Artificial Intelligence Papers, OECD Publishing, number 25, Sep, DOI: 10.1787/1d6474ef-en.
• Aleksandar Arandjelovi'c & Julia Eisenberg, 2024, "Optimal risk mitigation by deep reinsurance," Papers, arXiv.org, number 2408.06168, Aug, revised Nov 2025.
• Aleksandar Arandjelovi'c & Pavel V. Shevchenko & Tomoko Matsui & Daisuke Murakami & Tor A. Myrvoll, 2024, "Solving stochastic climate-economy models: A deep least-squares Monte Carlo approach," Papers, arXiv.org, number 2408.09642, Aug, revised Dec 2025.
• Julia M. Puaschunder, 2024, "Big Data Inequality," RAIS Conference Proceedings 2022-2026, Research Association for Interdisciplinary Studies, number 0415, Jul.
• Hamza Bennani & Davide Romelli, 2024, "Exploring the informativeness and drivers of tone during committee meetings: the case of the Federal Reserve," Post-Print, HAL, number hal-04670309, Aug, DOI: 10.1016/j.jimonfin.2024.103161.
• Nicolas Forteza & Elvira Prades & Marc Roca, 2024, "Analysing the VAT Cut Pass-Through in Spain Using Web Scraped Supermarket Data and Machine Learning," Working papers, Banque de France, number 951.

Report NEP-BIG-2024-09-23

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: