Report NEP-BIG-2020-05-11

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:

• Brian Huge & Antoine Savine, 2020, "Differential Machine Learning," Papers, arXiv.org, number 2005.02347, May, revised Sep 2020.
• Sidra Mehtab & Jaydip Sen, 2020, "A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models," Papers, arXiv.org, number 2004.11697, Apr, revised May 2021.
• Makarov, Vladimir & Stouch, Terry & Allgood, Brandon & Willis, Christopher & Lynch, Nick, 2020, "Best Practices for Artificial Intelligence in Life Sciences Research," OSF Preprints, Center for Open Science, number eqm9j, Apr, DOI: 10.31219/osf.io/eqm9j.
• Dimitris Korobilis & Davide Pettenuzzo, 2020, "Machine Learning Econometrics: Bayesian algorithms and methods," Papers, arXiv.org, number 2004.11486, Apr.
• Alexander Arimond & Damian Borth & Andreas Hoepner & Michael Klawunn & Stefan Weisheit, 2020, "Neural Networks and Value at Risk," Papers, arXiv.org, number 2005.01686, May, revised May 2020.
• Lucio Fernandez Arjona & Damir Filipović, 2020, "A machine learning approach to portfolio pricing and risk management for high-dimensional problems," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-28, Apr.
• -, 2020, "Tracking the digital footprint in Latin America and the Caribbean: Lessons learned from using big data to assess the digital economy," Documentos de Proyectos, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), number 45484, Jul.
• Michael Roberts & Indranil SenGupta, 2020, "Sequential hypothesis testing in machine learning, and crude oil price jump size detection," Papers, arXiv.org, number 2004.08889, Apr, revised Dec 2020.
• Manuel Nunes & Enrico Gerding & Frank McGroarty & Mahesan Niranjan, 2020, "Long short-term memory networks and laglasso for bond yield forecasting: Peeping inside the black box," Papers, arXiv.org, number 2005.02217, May.
• Grogger, Jeffrey & Ivandic, Ria & Kirchmaier, Thomas, 2020, "Comparing conventional and machine-learning approaches to risk assessment in domestic abuse cases," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 104159, Feb.
• Humayra Shoshi & Indranil SenGupta, 2020, "Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model," Papers, arXiv.org, number 2004.14862, Apr, revised Feb 2021.
• Tian Guo & Nicolas Jamet & Valentin Betrix & Louis-Alexandre Piquet & Emmanuel Hauptmann, 2020, "ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility Prediction," Papers, arXiv.org, number 2005.02527, May.
• Ruda Zhang & Patrick Wingo & Rodrigo Duran & Kelly Rose & Jennifer Bauer & Roger Ghanem, 2020, "Environmental Economics and Uncertainty: Review and a Machine Learning Outlook," Papers, arXiv.org, number 2004.11780, Apr.
• Poppius, Hampus, 2020, "Multimarket Contact and Collusion in Online Retail," Working Papers, Lund University, Department of Economics, number 2020:5, Apr.
• Lucio Fernandez-Arjona & Damir Filipovi'c, 2020, "A machine learning approach to portfolio pricing and risk management for high-dimensional problems," Papers, arXiv.org, number 2004.14149, Apr, revised May 2022.
• Jonathan Gruber & Benjamin R. Handel & Samuel H. Kina & Jonathan T. Kolstad, 2020, "Managing Intelligence: Skilled Experts and AI in Markets for Complex Products," NBER Working Papers, National Bureau of Economic Research, Inc, number 27038, Apr.
• Christa Cuchiero & Wahid Khosrawi & Josef Teichmann, 2020, "A generative adversarial network approach to calibration of local stochastic volatility models," Papers, arXiv.org, number 2005.02505, May, revised Sep 2020.
• Breda, Thomas & Grenet, Julien & Monnet, Marion & Van Effenterre, Clémentine, 2020, "Do Female Role Models Reduce the Gender Gap in Science? Evidence from French High Schools," IZA Discussion Papers, IZA Network @ LISER, number 13163, Apr.
• Lucio Fernandez-Arjona, 2020, "A neural network model for solvency calculations in life insurance," Papers, arXiv.org, number 2005.02318, May.
• Marina Toger & Ian Shuttleworth & John Osth, 2020, "How average is average? Temporal patterns in human behaviour as measured by mobile phone data -- or why chose Thursdays," Papers, arXiv.org, number 2005.00137, Apr.
• Roy Gernhardt & Bjorn Persson, 2020, "On the Equivalence of Neural and Production Networks," Papers, arXiv.org, number 2005.00510, May, revised Mar 2021.
• Toro Hardy, Alfredo, 2020, "The technological contest between China and the United States," GLO Discussion Paper Series, Global Labor Organization (GLO), number 521.
• Johannes Ruf & Weiguan Wang, 2020, "Hedging with Linear Regressions and Neural Networks," Papers, arXiv.org, number 2004.08891, Apr, revised Jun 2021.
• Alessandro Gnoatto & Athena Picarelli & Christoph Reisinger, 2020, "Deep xVA solver -- A neural network based counterparty credit risk management framework," Papers, arXiv.org, number 2005.02633, May, revised Dec 2022.
• Kocornik-Mina, Adriana & McDermott, Thomas K.J. & Michaels, Guy & Rauch, Ferdinand, 2020, "Flooded cities," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 100031, Apr.
• Carlo Baldassi & Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci & Marco Pirazzini, 2020, "Multialternative Neural Decision Processes," Papers, arXiv.org, number 2005.01081, May, revised May 2021.

Report NEP-BIG-2020-05-11

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: