Report NEP-BIG-2021-06-28

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:

• Victor Klockmann & Alicia von Schenk & Marie Claire Villeval, 2021, "Artificial Intelligence, Ethics, and Diffused Pivotality," Working Papers, HAL, number halshs-03237453.
• Liping Yang, 2021, "Next-Day Bitcoin Price Forecast Based on Artificial intelligence Methods," Papers, arXiv.org, number 2106.12961, Jun.
• Katsafados, Apostolos G. & Leledakis, George N. & Pyrgiotakis, Emmanouil G. & Androutsopoulos, Ion & Fergadiotis, Manos, 2021, "Machine Learning in U.S. Bank Merger Prediction: A Text-Based Approach," MPRA Paper, University Library of Munich, Germany, number 108272, Jun.
• Juranek, Steffen & Otneim, Håkon, 2021, "Using machine learning to predict patent lawsuits," Discussion Papers, Norwegian School of Economics, Department of Business and Management Science, number 2021/6, Jun.
• Liao Zhu & Haoxuan Wu & Martin T. Wells, 2021, "A News-based Machine Learning Model for Adaptive Asset Pricing," Papers, arXiv.org, number 2106.07103, Jun.
• Ivan Fursov & Matvey Morozov & Nina Kaploukhaya & Elizaveta Kovtun & Rodrigo Rivera-Castro & Gleb Gusev & Dmitry Babaev & Ivan Kireev & Alexey Zaytsev & Evgeny Burnaev, 2021, "Adversarial Attacks on Deep Models for Financial Transaction Records," Papers, arXiv.org, number 2106.08361, Jun.
• Toru Kitagawa & Shosei Sakaguchi & Aleksey Tetenov, 2021, "Constrained Classification and Policy Learning," Papers, arXiv.org, number 2106.12886, Jun, revised Jul 2023.
• Jordan Vazquez & Cécile Godé & Jean-Fabrice Lebraty, 2021, "Environnement big data et prise de décision : maintien de l'ordre durant un évènement sportif d'ampleur," Post-Print, HAL, number hal-03252399, Jun.
• Florian Eckerli & Joerg Osterrieder, 2021, "Generative Adversarial Networks in finance: an overview," Papers, arXiv.org, number 2106.06364, Jun, revised Jul 2021.
• Hengxu Lin & Dong Zhou & Weiqing Liu & Jiang Bian, 2021, "Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport," Papers, arXiv.org, number 2106.12950, Jun, revised Jun 2021.
• Muyang Ge & Shen Zhou & Shijun Luo & Boping Tian, 2021, "3D Tensor-based Deep Learning Models for Predicting Option Price," Papers, arXiv.org, number 2106.02916, Jun, revised Sep 2021.
• Victor Klockmann & Alicia von Schenk & Marie Claire Villeval, 2021, "Artificial Intelligence, Ethics, and Intergenerational Responsibility," Working Papers, HAL, number halshs-03237437.
• Vipul Satone & Dhruv Desai & Dhagash Mehta, 2021, "Fund2Vec: Mutual Funds Similarity using Graph Learning," Papers, arXiv.org, number 2106.12987, Jun.
• Izumi Yamashita & Akiyoshi Murakami & Stephanie Cairns & Fernando Galindo-Rueda, 2021, "Measuring the AI content of government-funded R&D projects: A proof of concept for the OECD Fundstat initiative," OECD Science, Technology and Industry Working Papers, OECD Publishing, number 2021/09, Jun, DOI: 10.1787/7b43b038-en.
• Vincent Van Roy & Fiammetta Rossetti & Karine Perset & Laura Galindo-Romero, 2021, "AI Watch - National strategies on Artificial Intelligence: A European perspective, 2021 edition," JRC Research Reports, Joint Research Centre, number JRC122684, Jun.
• Jaydip Sen & Sidra Mehtab, 2021, "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction," Papers, arXiv.org, number 2106.09664, Jun.
• Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021, "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Economics Working Paper Series, University of St. Gallen, School of Economics and Political Science, number 2108, Jun.
• Anand Deo & Karthyek Murthy, 2021, "Efficient Black-Box Importance Sampling for VaR and CVaR Estimation," Papers, arXiv.org, number 2106.10236, Jun.
• Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021, "Stock Market Analysis with Text Data: A Review," Papers, arXiv.org, number 2106.12985, Jun, revised Jul 2021.
• Oecd, 2021, "Tools for trustworthy AI: A framework to compare implementation tools for trustworthy AI systems," OECD Digital Economy Papers, OECD Publishing, number 312, Jun, DOI: 10.1787/008232ec-en.
• Daniel Goller & Tamara Harrer & Michael Lechner & Joachim Wolff, 2021, "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Papers, arXiv.org, number 2106.10141, Jun, revised May 2023.
• Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021, "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers, arXiv.org, number 2106.07177, Jun, revised Jan 2022.
• Nektarios Aslanidis & Aurelio F. Bariviera & 'Oscar G. L'opez, 2021, "The link between Bitcoin and Google Trends attention," Papers, arXiv.org, number 2106.07104, Jun.
• Daniel Aaronson & Scott A. Brave & R. Andrew Butters & Daniel Sacks & Boyoung Seo, 2020, "Using the Eye of the Storm to Predict the Wave of Covid-19 UI Claims," Working Paper Series, Federal Reserve Bank of Chicago, number WP-2020-10, Apr, revised 16 Apr 2020, DOI: 10.21033/wp-2020-10.
• Shohei Ohsawa, 2021, "Truthful Self-Play," Papers, arXiv.org, number 2106.03007, Jun, revised Feb 2023.
• Monica Pratesi & Claudio Ceccarelli & Stefano Menghinello, 2021, "Citizen-Generated Data and Official Statistics: an application to SDG indicators," Discussion Papers, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy, number 2021/274, Jun.
• Meena Jagadeesan & Celestine Mendler-Dunner & Moritz Hardt, 2021, "Alternative Microfoundations for Strategic Classification," Papers, arXiv.org, number 2106.12705, Jun.
• Maciej Berk{e}sewicz & Dagmara Nikulin & Marcin Szymkowiak & Kamil Wilak, 2021, "The gig economy in Poland: evidence based on mobile big data," Papers, arXiv.org, number 2106.12827, Jun.
• Mathieu Mercadier & Jean-Pierre Lardy, 2021, "Credit spread approximation and improvement using random forest regression," Papers, arXiv.org, number 2106.07358, Jun.
• Long Chen & Yadong Huang & Shumiao Ouyang & Wei Xiong, 2021, "The Data Privacy Paradox and Digital Demand," NBER Working Papers, National Bureau of Economic Research, Inc, number 28854, May.
• Zach Y. Brown & Alexander MacKay, 2021, "Competition in Pricing Algorithms," NBER Working Papers, National Bureau of Economic Research, Inc, number 28860, May.
• Carlos Newland & Juan Carlos Rosiello & Roberto Salinas, 2021, "Influencers on Economic Issues in Latin America, Spain and the United States – II," Studies in Applied Economics, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise, number 183, Jun.

Report NEP-BIG-2021-06-28

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: