Report NEP-BIG-2023-07-31

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:

• LIM Jaehwan & ITO Asei & ZHANG Hongyong, 2023. "Policy Agenda and Trajectory of the Xi Jinping Administration: Textual Evidence from 2012 to 2022," Policy Discussion Papers 23008, Research Institute of Economy, Trade and Industry (RIETI).
• Marc Chen & Mohammad Shirazi & Peter A. Forsyth & Yuying Li, 2023. "Machine Learning and Hamilton-Jacobi-Bellman Equation for Optimal Decumulation: a Comparison Study," Papers 2306.10582, arXiv.org.
• Wenbo Ge & Pooia Lalbakhsh & Leigh Isai & Artem Lensky & Hanna Suominen, 2023. "Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Data," Papers 2306.12446, arXiv.org, revised Jun 2023.
• Erik Altman & Jovan Blanuv{s}a & Luc von Niederhausern & B'eni Egressy & Andreea Anghel & Kubilay Atasu, 2023. "Realistic Synthetic Financial Transactions for Anti-Money Laundering Models," Papers 2306.16424, arXiv.org, revised Jan 2024.
• Emily Silcock & Melissa Dell, 2023. "A Massive Scale Semantic Similarity Dataset of Historical English," Papers 2306.17810, arXiv.org, revised Aug 2023.
• Marc Velay & Bich-Li^en Doan & Arpad Rimmel & Fabrice Popineau & Fabrice Daniel, 2023. "Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management," Papers 2306.10950, arXiv.org.
• David Noel, 2023. "Stock Price Prediction using Dynamic Neural Networks," Papers 2306.12969, arXiv.org.
• Pumplun, Luisa & Peters, Felix & Gawlitza, Joshua & Buxmann, Peter, 2023. "Bringing Machine Learning Systems into Clinical Practice: A Design Science Approach to Explainable Machine Learning-Based Clinical Decision Support Systems," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 138523, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Gerhard Hellstern & Vanessa Dehn & Martin Zaefferer, 2023. "Quantum computer based Feature Selection in Machine Learning," Papers 2306.10591, arXiv.org.
• Cécile Godé, 2023. "Le processus de décision en environnement Big Data," Post-Print hal-04125364, HAL.
• Boyu Zhang & Hongyang Yang & Xiao-Yang Liu, 2023. "Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models," Papers 2306.12659, arXiv.org.
• Johanna Deperi & Ludovic Dibiaggio & Mohamed Keita & Lionel Nesta, 2023. "Ideas Without Scale in French Artificial Intelligence Innovations," SciencePo Working papers Main hal-04144817, HAL.
• Thomas Dohmke & Marco Iansiti & Greg Richards, 2023. "Sea Change in Software Development: Economic and Productivity Analysis of the AI-Powered Developer Lifecycle," Papers 2306.15033, arXiv.org.
• Gideon du Rand & Hylton Hollander & Dawie van Lill, 2023. "A deep learning approach to estimation of the Phillips curve in South Africa," WIDER Working Paper Series wp-2023-79, World Institute for Development Economic Research (UNU-WIDER).
• Haohan Zhang & Fengrui Hua & Chengjin Xu & Hao Kong & Ruiting Zuo & Jian Guo, 2023. "Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?," Papers 2306.14222, arXiv.org, revised May 2024.
• Sherly Alfonso-S'anchez & Jes'us Solano & Alejandro Correa-Bahnsen & Kristina P. Sendova & Cristi'an Bravo, 2023. "Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning," Papers 2306.15585, arXiv.org, revised Feb 2024.
• Yu-Chin Hsu & Martin Huber & Yu-Min Yen, 2023. "Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning," Papers 2307.01049, arXiv.org.
• Ellenrieder, Sara & Jourdan, Nicolas & Biegel, Tobias & Bretones Cassoli, Beatriz & Metternich, Joachim & Buxmann, Peter, 2023. "Toward the sustainable development of machine learning applications in Industry 4.0," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 138521, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Joel Ong & Dorien Herremans, 2023. "Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning," Papers 2306.13661, arXiv.org.
• Stempel, Daniel & Zahner, Johannes, 2023. "Whose inflation rates matter most? A DSGE model and machine learning approach to monetary policy in the Euro area," IMFS Working Paper Series 188, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
• Raffaele Sgarlato, 2023. "Statistical electricity price forecasting: A structural approach," Papers 2306.14186, arXiv.org.
• Jiafa He & Cong Zheng & Can Yang, 2023. "Integrating Tick-level Data and Periodical Signal for High-frequency Market Making," Papers 2306.17179, arXiv.org.