Report NEP-BIG-2020-07-13

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:

• Mueller, H. & Rauh, C., 2020, "The Hard Problem of Prediction for Conflict Prevention," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2015, Mar.
• Bartram, Söhnke & Branke, Jürgen & Motahari, Mehrshad, 2020, "Artificial Intelligence in Asset Management," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14525, Mar.
• S M Raju & Ali Mohammad Tarif, 2020, "Real-Time Prediction of BITCOIN Price using Machine Learning Techniques and Public Sentiment Analysis," Papers, arXiv.org, number 2006.14473, Jun.
• Abhishek Gupta & Camylle Lanteigne & Sara Kingsley, 2020, "SECure: A Social and Environmental Certificate for AI Systems," Papers, arXiv.org, number 2006.06217, Jun, revised Jul 2020.
• Bart Cockx & Michael Lechner & Joost Bollens, 2020, "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium, Ghent University, Faculty of Economics and Business Administration, number 20/998, May.
• Bart Cockx & Michael Lechner & Joost Bollens, 2020, "Priority of Unemployed Immigrants? A Causal Machine Learning Evaluation of Training in Belgium," CESifo Working Paper Series, CESifo, number 8297.
• Akash Doshi & Alexander Issa & Puneet Sachdeva & Sina Rafati & Somnath Rakshit, 2020, "Deep Stock Predictions," Papers, arXiv.org, number 2006.04992, Jun.
• Daniel J. Egger & Claudio Gambella & Jakub Marecek & Scott McFaddin & Martin Mevissen & Rudy Raymond & Andrea Simonetto & Stefan Woerner & Elena Yndurain, 2020, "Quantum Computing for Finance: State of the Art and Future Prospects," Papers, arXiv.org, number 2006.14510, Jun, revised Jan 2021.
• Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2020, "Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Working Papers, University of Pretoria, Department of Economics, number 202056, Jun.
• Geoffroy G Dolphin & Michael G Pollitt, 2020, "Identifying innovative actors in the Electricity Supply Industry using machine learning: an application to UK patent data," Working Papers, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge, number EPRG2004, Mar.
• Christoph Berninger & Almond Stocker & David Rugamer, 2020, "A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction," Papers, arXiv.org, number 2006.05750, Jun, revised Feb 2021.
• Item repec:ecr:col043:45619 is not listed on IDEAS anymore

• Hans Buhler & Blanka Horvath & Terry Lyons & Imanol Perez Arribas & Ben Wood, 2020, "A Data-driven Market Simulator for Small Data Environments," Papers, arXiv.org, number 2006.14498, Jun.
• Nawaf Almaskati & Ron Bird & Yue Lu & Danny Leung, 2019, "The Role of Corporate Governance and Estimation Methods in Predicting Bankruptcy," Working Papers in Economics, University of Waikato, number 19/16, Jul.
• Massimo Guidolin & Manuela Pedio, 2020, "Distilling Large Information Sets to Forecast Commodity Returns: Automatic Variable Selection or HiddenMarkov Models?," BAFFI CAREFIN Working Papers, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy, number 20140.
• Ransom, Tyler, 2020, "Selective Migration, Occupational Choice, and the Wage Returns to College Majors," IZA Discussion Papers, IZA Network @ LISER, number 13370, Jun.
• Jiaming Mao & Jingzhi Xu, 2020, "Ensemble Learning with Statistical and Structural Models," Papers, arXiv.org, number 2006.05308, Jun.
• Dolphin, G. & Pollitt, M., 2020, "Identifying Innovative Actors in the Electricicity Supply Industry Using Machine Learning: An Application to UK Patent Data," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2013, Mar.
• Hema Yoganarasimhan & Ebrahim Barzegary & Abhishek Pani, 2020, "Design and Evaluation of Personalized Free Trials," Papers, arXiv.org, number 2006.13420, Jun.
• Adams-Prassl, Abi & Boneva, Teodora & Golin, Marta & Rauh, Christopher, 2020, "Work That Can Be Done from Home: Evidence on Variation within and across Occupations and Industries," IZA Discussion Papers, IZA Network @ LISER, number 13374, Jun.
• Thibaut Th'eate & S'ebastien Mathieu & Damien Ernst, 2020, "An Artificial Intelligence Solution for Electricity Procurement in Forward Markets," Papers, arXiv.org, number 2006.05784, Jun, revised Dec 2020.
• Tomasz Chmielewski & Andrzej Kocięcki & Tomasz Łyziak & Jan Przystupa & Ewa Stanisławska & Małgorzata Walerych & Ewa Wróbel, 2020, "Monetary policy transmission mechanism in Poland What do we know in 2019?," NBP Working Papers, Narodowy Bank Polski, number 329.
• Brodeur, Abel & Clark, Andrew E. & Fleche, Sarah & Powdthavee, Nattavudh, 2020, "COVID-19, Lockdowns and Well-Being: Evidence from Google Trends," GLO Discussion Paper Series, Global Labor Organization (GLO), number 552.

Report NEP-BIG-2020-07-13

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: