Printed from https://ideas.repec.org/n/nep-big/2024-11-25.html
Report NEP-BIG-2024-11-25
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:
- Nikos Askitas & Nikolaos Askitas, 2024, "A Hands-On Machine Learning Primer for Social Scientists: Math, Algorithms and Code," CESifo Working Paper Series, CESifo, number 11353.
- Lin, Yang & Thackway, William & Soundararaj, Balamurugan & Eagleson, Serryn & Han, Hoon & Pettit, Christopher, 2024, "Transforming Urban Planning through Machine Learning: A Study on Planning Application Classification using Natural Language Processing," OSF Preprints, Center for Open Science, number fs76e, Oct, DOI: 10.31219/osf.io/fs76e.
- Belguutei Ariuntugs & Kehelwala Dewage Gayan Madurang, 2024, "Optimization of Actuarial Neural Networks with Response Surface Methodology," Papers, arXiv.org, number 2410.12824, Oct.
- Jian-Qiao Zhu & Joshua C. Peterson & Benjamin Enke & Thomas L. Griffiths, 2024, "Capturing the Complexity of Human Strategic Decision-Making with Machine Learning," CESifo Working Paper Series, CESifo, number 11296.
- Opeyemi Sheu Alamu & Md Kamrul Siam, 2024, "Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goals," Papers, arXiv.org, number 2410.07220, Sep.
- Mohamed Bassi, 2023, "Machine Learning et Veille économique : Analyse des données RePEc à l’aide des techniques du NLP," Policy briefs on Economic Trends and Policies, Policy Center for the New South, number 2307, Feb.
- Te Li & Mengze Zhang & Yan Zhou, 2024, "LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting," Papers, arXiv.org, number 2410.15286, Oct.
- Mahdi Ebrahimi Kahou & Jesús Fernández-Villaverde & Sebastián Gómez-Cardona & Jesse Perla & Jan Rosa, 2024, "Spooky Boundaries at a Distance: Inductive Bias, Dynamic Models, and Behavioral Macro," CESifo Working Paper Series, CESifo, number 11292.
- Emmanuel Gnabeyeu & Omar Karkar & Imad Idboufous, 2024, "Solving The Dynamic Volatility Fitting Problem: A Deep Reinforcement Learning Approach," Papers, arXiv.org, number 2410.11789, Oct.
- Lukas Gonon & Thilo Meyer-Brandis & Niklas Weber, 2024, "Computing Systemic Risk Measures with Graph Neural Networks," Papers, arXiv.org, number 2410.07222, Sep, revised Oct 2025.
- Zimeng Lyu & Amulya Saxena & Rohaan Nadeem & Hao Zhang & Travis Desell, 2024, "Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading," Papers, arXiv.org, number 2410.17212, Oct.
- Zijie Zhao & Roy E. Welsch, 2024, "Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution," Papers, arXiv.org, number 2410.14927, Oct.
- Kelvin J. L. Koa & Yunshan Ma & Yi Xu & Ritchie Ng & Huanhuan Zheng & Tat-Seng Chua, 2024, "Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes," Papers, arXiv.org, number 2410.17266, Oct, revised Oct 2025.
- Riccardo Di Francesco, 2024, "Aggregation Trees," Papers, arXiv.org, number 2410.11408, Oct, revised Oct 2025.
- Yikai Zhao & Jun Nagayasu & Xinyi Geng, 2024, "Measuring Climate Policy Uncertainty with LLMs: New Insights into Corporate Bond Credit Spreads," DSSR Discussion Papers, Graduate School of Economics and Management, Tohoku University, number 143, Nov.