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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 (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:
- Kollár, Aladár, 2021, "Betting models using AI: a review on ANN, SVM, and Markov chain," MPRA Paper, University Library of Munich, Germany, number 106821, Mar.
- Hanjo Odendaal, 2021, "A machine learning approach to domain specific dictionary generation. An economic time series framework," Working Papers, Stellenbosch University, Department of Economics, number 06/2021.
- Mukul Jaggi & Priyanka Mandal & Shreya Narang & Usman Naseem & Matloob Khushi, 2021, "Text Mining of Stocktwits Data for Predicting Stock Prices," Papers, arXiv.org, number 2103.16388, Mar.
- Hannes Mueller, 2021, "The Hard Problem of Prediction for Conflict Prevention," Working Papers, Barcelona School of Economics, number 1244, Mar.
- 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, DIW Berlin, German Institute for Economic Research, number 1939.
- Aleksy Klimowicz & Krzysztof Spirzewski, 2021, "Concept of peer-to-peer lending and application of machine learning in credit scoring," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-04.
- Martin Beraja & David Y. Yang & Noam Yuchtman, 2021, "Data-intensive innovation and the State: evidence from AI firms in China," CEP Discussion Papers, Centre for Economic Performance, LSE, number dp1755, Mar.
- Yiyan Huang & Cheuk Hang Leung & Qi Wu & Xing Yan, 2021, "Robust Orthogonal Machine Learning of Treatment Effects," Papers, arXiv.org, number 2103.11869, Mar, revised Dec 2022.
- Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020, "News media vs. FRED-MD for macroeconomic forecasting," Working Paper, Norges Bank, number 2020/14, Oct.
- Item repec:bge:wpaper:1245 is not listed on IDEAS anymore
- 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, Department of Economics and Business, Universitat Pompeu Fabra, number 1772, Mar.
- 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, arXiv.org, number 2103.12419, Mar, revised May 2022.
- Ariel Neufeld & Julian Sester, 2021, "A deep learning approach to data-driven model-free pricing and to martingale optimal transport," Papers, arXiv.org, number 2103.11435, Mar, revised Dec 2022.
- Hans Genberg & Özer Karagedikli, 2021, "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers, South East Asian Central Banks (SEACEN) Research and Training Centre, number wp43, Mar.
- Karush Suri & Xiao Qi Shi & Konstantinos Plataniotis & Yuri Lawryshyn, 2021, "TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution," Papers, arXiv.org, number 2104.00620, Feb.
- Kässi, Otto & Lehdonvirta, Vili & Stephany, Fabian, 2021, "How Many Online Workers are there in the World? A Data-Driven Assessment," SocArXiv, Center for Open Science, number 78nge, Mar, DOI: 10.31219/osf.io/78nge.
- Otto Kassi & Vili Lehdonvirta & Fabian Stephany, 2021, "How Many Online Workers are there in the World? A Data-Driven Assessment," Papers, arXiv.org, number 2103.12648, Mar, revised Apr 2021.
- Elif Semra Ceylan & Semih Tumen, 2021, "Measuring the Economic Cost of Conflict in Afflicted Arab Countries," Working Papers, Economic Research Forum, number 1459, Feb, 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, arXiv.org, number 2103.13507, Mar.