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 issued this report. It is usually issued weekly.

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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 11353, CESifo.
• 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 fs76e, Center for Open Science.
• Belguutei Ariuntugs & Kehelwala Dewage Gayan Madurang, 2024. "Optimization of Actuarial Neural Networks with Response Surface Methodology," Papers 2410.12824, arXiv.org.
• 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 11296, CESifo.
• Opeyemi Sheu Alamu & Md Kamrul Siam, 2024. "Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goals," Papers 2410.07220, arXiv.org.
• 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 2307, Policy Center for the New South.
• Te Li & Mengze Zhang & Yan Zhou, 2024. "LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting," Papers 2410.15286, arXiv.org.
• 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 11292, CESifo.
• Emmanuel Gnabeyeu & Omar Karkar & Imad Idboufous, 2024. "Solving The Dynamic Volatility Fitting Problem: A Deep Reinforcement Learning Approach," Papers 2410.11789, arXiv.org.
• Lukas Gonon & Thilo Meyer-Brandis & Niklas Weber, 2024. "Computing Systemic Risk Measures with Graph Neural Networks," Papers 2410.07222, arXiv.org.
• 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 2410.17212, arXiv.org.
• Zijie Zhao & Roy E. Welsch, 2024. "Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution," Papers 2410.14927, arXiv.org.
• Kelvin J. L. Koa & Yunshan Ma & Ritchie Ng & Huanhuan Zheng & Tat-Seng Chua, 2024. "Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes," Papers 2410.17266, arXiv.org.
• Riccardo Di Francesco, 2024. "Aggregation Trees," Papers 2410.11408, arXiv.org.
• Yikai Zhao & Jun Nagayasu & Xinyi Geng, 2024. "Measuring Climate Policy Uncertainty with LLMs: New Insights into Corporate Bond Credit Spreads," DSSR Discussion Papers 143, Graduate School of Economics and Management, Tohoku University.