Report NEP-BIG-2021-04-05

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:

• Kollár, Aladár, 2021. "Betting models using AI: a review on ANN, SVM, and Markov chain," MPRA Paper 106821, University Library of Munich, Germany.
• Hanjo Odendaal, 2021. "A machine learning approach to domain specific dictionary generation. An economic time series framework," Working Papers 06/2021, Stellenbosch University, Department of Economics.
• Mukul Jaggi & Priyanka Mandal & Shreya Narang & Usman Naseem & Matloob Khushi, 2021. "Text Mining of Stocktwits Data for Predicting Stock Prices," Papers 2103.16388, arXiv.org.
• Hannes Mueller & Christopher Rauh, 2021. "The Hard Problem of Prediction for Conflict Prevention," Working Papers 1244, Barcelona School of Economics.
• Shan Huang & Michael Allan Ribers & Hannes Ullrich, 2021. "The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing," Discussion Papers of DIW Berlin 1939, DIW Berlin, German Institute for Economic Research.
• Aleksy Klimowicz & Krzysztof Spirzewski, 2021. "Concept of peer-to-peer lending and application of machine learning in credit scoring," Working Papers 2021-04, Faculty of Economic Sciences, University of Warsaw.
• Martin Beraja & David Y. Yang & Noam Yuchtman, 2021. "Data-intensive innovation and the State: evidence from AI firms in China," CEP Discussion Papers dp1755, Centre for Economic Performance, LSE.
• Yiyan Huang & Cheuk Hang Leung & Qi Wu & Xing Yan, 2021. "Robust Orthogonal Machine Learning of Treatment Effects," Papers 2103.11869, arXiv.org, revised Dec 2022.
• Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News media vs. FRED-MD for macroeconomic forecasting," Working Paper 2020/14, Norges Bank.
• Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can Machine Learning Help to Select Portfolios of Mutual Funds?," Working Papers 1245, Barcelona School of Economics.
• Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can machine learning help to select portfolios of mutual funds?," Economics Working Papers 1772, Department of Economics and Business, Universitat Pompeu Fabra.
• Artur Sokolovsky & Luca Arnaboldi & Jaume Bacardit & Thomas Gross, 2021. "Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications," Papers 2103.12419, arXiv.org, revised May 2022.
• Ariel Neufeld & Julian Sester, 2021. "A deep learning approach to data-driven model-free pricing and to martingale optimal transport," Papers 2103.11435, arXiv.org, revised Dec 2022.
• Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.
• Karush Suri & Xiao Qi Shi & Konstantinos Plataniotis & Yuri Lawryshyn, 2021. "TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution," Papers 2104.00620, arXiv.org.
• Kässi, Otto & Lehdonvirta, Vili & Stephany, Fabian, 2021. "How Many Online Workers are there in the World? A Data-Driven Assessment," SocArXiv 78nge, Center for Open Science.
• Otto Kassi & Vili Lehdonvirta & Fabian Stephany, 2021. "How Many Online Workers are there in the World? A Data-Driven Assessment," Papers 2103.12648, arXiv.org, revised Apr 2021.
• Elif Semra Ceylan & Semih Tumen, 2021. "Measuring the Economic Cost of Conflict in Afflicted Arab Countries," Working Papers 1459, Economic Research Forum, revised 20 Feb 2021.
• Q. Wang & Y. Zhou & J. Shen, 2021. "Intraday trading strategy based on time series and machine learning for Chinese stock market," Papers 2103.13507, arXiv.org.