Report NEP-BIG-2021-01-18

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.

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Other reports in NEP-BIG

The following items were announced in this report:

• Farnoush Ronaghi & Mohammad Salimibeni & Farnoosh Naderkhani & Arash Mohammadi, 2021. "COVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information Fusion Framework for Stock Price Movement Prediction," Papers 2101.02287, arXiv.org, revised Jul 2021.
• Yiping Huang & Ms. Longmei Zhang & Zhenhua Li & Han Qiu & Tao Sun & Xue Wang, 2020. "Fintech Credit Risk Assessment for SMEs: Evidence from China," IMF Working Papers 2020/193, International Monetary Fund.
• Wei Li & Denis Mike Becker, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Papers 2101.05249, arXiv.org, revised Jul 2021.
• Verluise, Cyril & Cristelli, Gabriele & Higham, Kyle & de Rassenfosse, Gaetan, 2020. "The Missing 15 Percent of Patent Citations," SocArXiv x78ys, Center for Open Science.
• Cheng, Kent Jason Go, 2021. "Using Predictive Analytics for Public Policy: The Case for Lost Work due to the COVID-19," SocArXiv e5z73, Center for Open Science.
• Mueller, H. & Rauh, C., 2021. "The Hard Problem of Prediction for Conflict Prevention," Cambridge Working Papers in Economics 2103, Faculty of Economics, University of Cambridge.
• Sridhar Ravula, 2021. "Text analysis in financial disclosures," Papers 2101.04480, arXiv.org.
• Ivana Loli? & Petar Sori? & Marija Logaru?i?, 0000. "Economic Policy Uncertainty index meets ensemble learning," Proceedings of International Academic Conferences 11313180, International Institute of Social and Economic Sciences.
• Peter Grajzl & Peter Murrell, 2020. "A Machine-Learning History of English Caselaw and Legal Ideas Prior to the Industrial Revolution I: Generating and Interpreting the Estimates," CESifo Working Paper Series 8774, CESifo.
• Christoph Schlembach & Sascha L. Schmidt & Dominik Schreyer & Linus Wunderlich, 2020. "Forecasting the Olympic medal distribution during a pandemic: a socio-economic machine learning model," Papers 2012.04378, arXiv.org, revised Jun 2021.
• Benjamin Carton & Nan Hu & Mr. Joannes Mongardini & Kei Moriya & Aneta Radzikowski, 2020. "Improving the Short-term Forecast of World Trade During the Covid-19 Pandemic Using Swift Data on Letters of Credit," IMF Working Papers 2020/247, International Monetary Fund.
• Peter Grajzl & Peter Murrell, 2020. "A Machine-Learning History of English Caselaw and Legal Ideas Prior to the Industrial Revolution II: Applications," CESifo Working Paper Series 8775, CESifo.
• Enrico Santarelli & Jacopo Staccioli & Marco Vivarelli, 2021. "Robots, AI, and Related Technologies: A Mapping of the New Knowledge Base," LEM Papers Series 2021/01, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
• Alaeddine Mihoub & Hosni Snoun & Moez Krichen & Montassar Kahia & Riadh Bel Hadj Salah, 2020. "Predicting COVID-19 Spread Level using Socio-Economic Indicators and Machine Learning Techniques," Post-Print hal-03002886, HAL.
• Jeffrey P. Clemens & Parker Rogers, 2020. "Demand Shocks, Procurement Policies, and the Nature of Medical Innovation: Evidence from Wartime Prosthetic Device Patents," CESifo Working Paper Series 8781, CESifo.
• Damien Azzopardi & Fozan Fareed & Patrick Lenain & Douglas Sutherland, 2020. "Why are some U.S. cities successful, while others are not? Empirical evidence from machine learning," OECD Economics Department Working Papers 1643, OECD Publishing.
• Maria Mercanti-Guérin, 2020. "The Improvement of Retargeting by Big Data: a Decision Support that Threatens the Brand Image?," Post-Print hal-03027981, HAL.
• Paola Tubaro, 2020. "Whose intelligence is artificial intelligence?," Post-Print hal-03029735, HAL.