Report NEP-BIG-2023-12-18

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.

Subscribe to this report: email, RSS, or Mastodon.

Other reports in NEP-BIG

The following items were announced in this report:

• Yinheng Li & Shaofei Wang & Han Ding & Hang Chen, 2023. "Large Language Models in Finance: A Survey," Papers 2311.10723, arXiv.org, revised Jul 2024.
• Jiahao Chen & Xiaofei Li, 2023. "Analysis of frequent trading effects of various machine learning models," Papers 2311.10719, arXiv.org.
• Khaoula Naili, 2023. "Critical AI Challenges in Legal Practice : An application to French Administrative Decisions," Working Papers AFED 23-04, Association Francaise d'Economie du Droit (AFED).
• Gerard J. van den Berg & Max Kunaschk & Julia Lang & Gesine Stephan & Arne Uhlendorf, 2023. "Predicting Re-Employment: Machine Learning Versus Assessments by Unemployed Workers and by Their Caseworkers," Working Papers 2023-09, Center for Research in Economics and Statistics.
• Branka Hadji Misheva & Joerg Osterrieder, 2023. "A Hypothesis on Good Practices for AI-based Systems for Financial Time Series Forecasting: Towards Domain-Driven XAI Methods," Papers 2311.07513, arXiv.org.
• Gang Hu, 2023. "Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models," Papers 2311.05743, arXiv.org.
• Fecho, Mariska & Zöll, Anne, 2023. "The Power of Trust: Designing Trustworthy Machine Learning Systems in Healthcare," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 138903, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Minati Rath & Hema Date, 2023. "Adaptive Modelling Approach for Row-Type Dependent Predictive Analysis (RTDPA): A Framework for Designing Machine Learning Models for Credit Risk Analysis in Banking Sector," Papers 2311.10799, arXiv.org.
• Abel Sancarlos & Edgar Bahilo & Pablo Mozo & Lukas Norman & Obaid Ur Rehma & Mihails Anufrijevs, 2023. "Towards a data-driven debt collection strategy based on an advanced machine learning framework," Papers 2311.06292, arXiv.org.
• Jirong Zhuang & Deng Ding & Weiguo Lu & Xuan Wu & Gangnan Yuan, 2023. "A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options," Papers 2311.07211, arXiv.org, revised Apr 2024.
• Donna K. Ginther & Carlos Zambrana & Patricia Oslund & Wan-Ying Chang, 2023. "Do Two Wrongs Make a Right? Measuring the Effect of Publications on Science Careers," NBER Working Papers 31844, National Bureau of Economic Research, Inc.
• Zhang, Wen & Shi, Jingwen & Wang, Xiaojun & Wynn, Henry, 2023. "AI-powered decision-making in facilitating insurance claim dispute resolution," LSE Research Online Documents on Economics 120649, London School of Economics and Political Science, LSE Library.
• Alexej Brauer, 2023. "Enhancing Actuarial Non-Life Pricing Models via Transformers," Papers 2311.07597, arXiv.org, revised Jun 2024.
• Ixandra Achitouv & Dragos Gorduza & Antoine Jacquier, 2023. "Natural Language Processing for Financial Regulation," Papers 2311.08533, arXiv.org.
• Moritz Scherrmann & Ralf Elsas, 2023. "Earnings Prediction Using Recurrent Neural Networks," Papers 2311.10756, arXiv.org.
• Ricardo Cuervo, 2023. "Predictive AI for SME and Large Enterprise Financial Performance Management," Papers 2311.05840, arXiv.org.
• Namid R. Stillman & Rory Baggott & Justin Lyon & Jianfei Zhang & Dingqiu Zhu & Tao Chen & Perukrishnen Vytelingum, 2023. "Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks," Papers 2311.11913, arXiv.org, revised Nov 2023.
• Chaturvedi, Sugat & Mahajan, Kanika & Siddique, Zahra, 2023. "Using Domain-Specific Word Embeddings to Examine the Demand for Skills," IZA Discussion Papers 16593, Institute of Labor Economics (IZA).
• Rawin Assabumrungrat & Kentaro Minami & Masanori Hirano, 2023. "Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study," Papers 2311.07231, arXiv.org.
• Soumyadip Sarkar, 2023. "Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration," Papers 2311.10718, arXiv.org.
• Yazan Alnsour & Marina Johnson & Abdullah Albizri & Antoine Harfouche, 2023. "Predicting Patient Length of Stay Using Artificial Intelligence to Assist Healthcare Professionals in Resource Planning and Scheduling Decisions," Post-Print hal-04263512, HAL.
• Wentao Zhang & Yilei Zhao & Shuo Sun & Jie Ying & Yonggang Xie & Zitao Song & Xinrun Wang & Bo An, 2023. "Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock Pools," Papers 2311.10801, arXiv.org, revised Feb 2024.
• Krolage, Carla & Bachtrögler-Unger, Julia & Dolls, Mathias & Schüle, Paul & Taubenböck, Hannes & Weigand, Matthias, 2023. "EU Cohesion Policy on the Ground: Analyzing Small-Scale Effects Using Satellite Data," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277604, Verein für Socialpolitik / German Economic Association.
• Besley, Timothy & Fetzer, Thiemo & Mueller, Hannes, 2024. "How big is the media multiplier? Evidence from dyadic news data," LSE Research Online Documents on Economics 120778, London School of Economics and Political Science, LSE Library.
• George Abi Younes & Gaetan de Rassenfosse, 2023. "Replicable Patent Indicators Using the Google Patents Public Datasets," Working Papers 24, Chair of Science, Technology, and Innovation Policy.
• Moritz Scherrmann, 2023. "Multi-Label Topic Model for Financial Textual Data," Papers 2311.07598, arXiv.org.