Report NEP-BIG-2023-06-19

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.

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Other reports in NEP-BIG

The following items were announced in this report:

• Agam Shah & Suvan Paturi & Sudheer Chava, 2023, "Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis," Papers, arXiv.org, number 2305.07972, May.
• Nordmeyer, Eike Florenz, 2023, "German farmers' perceived usefulness of satellite-based index insurance - Insights from a transtheoretical model," 97th Annual Conference, March 27-29, 2023, Warwick University, Coventry, UK, Agricultural Economics Society - AES, number 334557, Mar, DOI: 10.22004/ag.econ.334557.
• Perico Ortiz, Daniel & Schnaubelt, Matthias & Seifert, Oleg, 2023, "A topic modeling perspective on investor uncertainty," FAU Discussion Papers in Economics, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, number 04/2023.
• Lin An & Andrew A. Li & Benjamin Moseley & R. Ravi, 2023, "The Nonstationary Newsvendor with (and without) Predictions," Papers, arXiv.org, number 2305.07993, May, revised Feb 2025.
• Pierre Durand & Gaëtan Le Quang & Arnold Vialfont, 2023, "Are Basel III requirements up to the task? Evidence from bankruptcy prediction models," Working Papers, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon, number 2308.
• Benatti, Nicola & Groiss, Martin & Kelly, Petra & Lopez-Garcia, Paloma, 2023, "Environmental regulation and productivity growth in the euro area: testing the Porter hypothesis," Working Paper Series, European Central Bank, number 2820, May.
• Jia Xu & Longbing Cao, 2023, "Copula Variational LSTM for High-dimensional Cross-market Multivariate Dependence Modeling," Papers, arXiv.org, number 2305.08778, May.
• Nicolas Ameye, 2023, "Essays on the Adoption and Diffusion of Big Data Analytics and Artificial Intelligence Technology," ULB Institutional Repository, ULB -- Universite Libre de Bruxelles, number 2013/358706, May.
• Fridgen, Gilbert & Kräussl, Roman & Papageorgiou, Orestis & Tugnetti, Alessandro, 2023, "The fundamental value of art NFTs," CFS Working Paper Series, Center for Financial Studies (CFS), number 709, DOI: 10.2139/ssrn.4337173.
• Sarah Robinson & Alisa Tazhitdinova, 2023, "What Drives Tax Policy? Political, Institutional and Economic Determinants of State Tax Policy," NBER Working Papers, National Bureau of Economic Research, Inc, number 31268, May.
• Linyi Yang & Yingpeng Ma & Yue Zhang, 2023, "Measuring Consistency in Text-based Financial Forecasting Models," Papers, arXiv.org, number 2305.08524, May, revised Jun 2023.
• Julien Pascal, 2023, "Artificial neural networks to solve dynamic programming problems: A bias-corrected Monte Carlo operator," BCL working papers, Central Bank of Luxembourg, number 172, Mar.
• Jonas Tallberg & Eva Erman & Markus Furendal & Johannes Geith & Mark Klamberg & Magnus Lundgren, 2023, "The Global Governance of Artificial Intelligence: Next Steps for Empirical and Normative Research," Papers, arXiv.org, number 2305.11528, May.
• Gimpel, Henner & Hall, Kristina & Decker, Stefan & Eymann, Torsten & Lämmermann, Luis & Mädche, Alexander & Röglinger, Maximilian & Ruiner, Caroline & Schoch, Manfred & Schoop, Mareike & Urbach, Nils , 2023, "Unlocking the power of generative AI models and systems such as GPT-4 and ChatGPT for higher education: A guide for students and lecturers," Hohenheim Discussion Papers in Business, Economics and Social Sciences, University of Hohenheim, Faculty of Business, Economics and Social Sciences, number 02-2023.
• Kyra Hanemaaijer & Olivier Marie & Marco Musumeci, 2023, "The Fast and The Studious? Ramadan Observance and Student Performance," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 23-023/V, Apr.
• Sheng Xiang & Dawei Cheng & Chencheng Shang & Ying Zhang & Yuqi Liang, 2023, "Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction," Papers, arXiv.org, number 2305.08740, May.
• David P. Brown & Daniel O. Cajueiro & Andrew Eckert & Douglas Silveira, 2023, "Information and Transparency: Using Machine Learning to Detect Communication," Working Papers, University of Alberta, Department of Economics, number 2023-06, May.
• Niklas Mueller & Steffen Klug & Andreas Koenig & Alexander Kathan & Lukas Christ & Bjoern Schuller & Shahin Amiriparian, 2023, "Executive Voiced Laughter and Social Approval: An Explorative Machine Learning Study," Papers, arXiv.org, number 2305.09485, May, revised May 2023.
• Borisenko Georgy, 2023, "Using neural networks to predict the value of stocks based on news data," Working Papers, Moscow State University, Faculty of Economics, number 0055, May.
• Khezr, Peyman & Pourkhanali, Armin, 2023, "An investigation of auctions in the Regional Greenhouse Gas Initiative," MPRA Paper, University Library of Munich, Germany, number 117267, Apr.
• Ölkers, Tim & Liu, Shuang & Mußhoff, Oliver, 2023, "A typology of Malian farmers and their credit repayment performance - An unsupervised machine learning approach," 97th Annual Conference, March 27-29, 2023, Warwick University, Coventry, UK, Agricultural Economics Society - AES, number 334547, Mar, DOI: 10.22004/ag.econ.334547.
• Chen, Yutong & Chiplunkar, Gaurav & Sekhri, Sheetal & Sen, Anirban & Seth, Aaditeshwar, 2023, "How Do Political Connections of Firms Matter during an Economic Crisis?," IZA Discussion Papers, Institute of Labor Economics (IZA), number 16131, May.
• Zikai Wei & Bo Dai & Dahua Lin, 2023, "E2EAI: End-to-End Deep Learning Framework for Active Investing," Papers, arXiv.org, number 2305.16364, May.
• Claire Greene & Oz Shy & Joanna Stavins, 2023, "Personality Traits and Financial Outcomes," Working Papers, Federal Reserve Bank of Boston, number 23-4, Mar, DOI: 10.29412/res.wp.2023.04.
• Kim, Minho & Han, Jaepil, 2022, "Can artificial intelligence improve the effectiveness of government support policies?," KDI Policy Forum, Korea Development Institute (KDI), number 288, DOI: 10.22740/kdi.forum.e.2022.288.
• Nasir, Nida & Kansal, Afreen & Alshaltone, Omar & Barneih, Feras & Shanableh, Abdallah & Al-Shabi, Mohammad & Al Shammaa, Ahmed, 2023, "Deep learning detection of types of water-bodies using optical variables and ensembling," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 118724, May.
• Agam Shah & Sudheer Chava, 2023, "Zero is Not Hero Yet: Benchmarking Zero-Shot Performance of LLMs for Financial Tasks," Papers, arXiv.org, number 2305.16633, May.

Report NEP-BIG-2023-06-19

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: