Report NEP-BIG-2020-11-23

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:

• Esther Rolf & Jonathan Proctor & Tamma Carleton & Ian Bolliger & Vaishaal Shankar & Miyabi Ishihara & Benjamin Recht & Solomon Hsiang, 2020. "A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery," NBER Working Papers 28045, National Bureau of Economic Research, Inc.
• Steven Lehrer & Tian Xie, 2020. "The Bigger Picture: Combining Econometrics with Analytics Improve Forecasts of Movie Success," Working Paper 1449, Economics Department, Queen's University.
• Maria Concetta Ambra, 2020. "Platforms from the Inside-Out," Working Papers 19/20, Sapienza University of Rome, DISS.
• Brian Quistorff & Gentry Johnson, 2020. "Machine Learning for Experimental Design: Methods for Improved Blocking," Papers 2010.15966, arXiv.org.
• Tadeu A. Ferreira, 2020. "Reinforced Deep Markov Models With Applications in Automatic Trading," Papers 2011.04391, arXiv.org.
• Pooja Gupta & Angshul Majumdar & Emilie Chouzenoux & Giovanni Chierchia, 2020. "SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems," Papers 2011.04364, arXiv.org.
• Hal Ashton, 2020. "Causal Campbell-Goodhart's law and Reinforcement Learning," Papers 2011.01010, arXiv.org, revised Feb 2021.
• Alexander Wong & Andrew Hryniowski & Xiao Yu Wang, 2020. "Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning," Papers 2011.01961, arXiv.org.
• Olivier Guéant & Iuliia Manziuk & Jiang Pu, 2020. "Accelerated Share Repurchase and other buyback programs: what neural networks can bring," Working Papers hal-02987889, HAL.
• Olivier Guéant & Iuliia Manziuk & Jiang Pu, 2020. "Accelerated Share Repurchase and other buyback programs: what neural networks can bring," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02987889, HAL.
• Sinha, Pankaj & Verma, Aniket & Shah, Purav & Singh, Jahnavi & Panwar, Utkarsh, 2020. "Prediction for the 2020 United States Presidential Election using Machine Learning Algorithm: Lasso Regression," MPRA Paper 103889, University Library of Munich, Germany, revised 31 Oct 2020.
• Diunugala, Hemantha Premakumara & Mombeuil, Claudel, 2020. "Modeling and predicting foreign tourist arrivals to Sri Lanka: A comparison of three different methods," MPRA Paper 103779, University Library of Munich, Germany.
• Jianqing Fan & Ricardo P. Masini & Marcelo C. Medeiros, 2020. "Do We Exploit all Information for Counterfactual Analysis? Benefits of Factor Models and Idiosyncratic Correction," Papers 2011.03996, arXiv.org, revised Jan 2022.
• Gries, Thomas & Naude, Wim, 2020. "Artificial Intelligence, Income Distribution and Economic Growth," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224623, Verein für Socialpolitik / German Economic Association.
• Loann David Denis Desboulets, 2020. "Sparse Manifolds Graphical Modelling with Missing Values: An Application to the Commodity Futures Market," Working Papers hal-02986982, HAL.
• Qing Yang & Zhenning Hong & Ruyan Tian & Tingting Ye & Liangliang Zhang, 2020. "Asset Allocation via Machine Learning and Applications to Equity Portfolio Management," Papers 2011.00572, arXiv.org, revised Nov 2020.
• Ferman, Bruno & Lima, Lycia & Riva, Flavio, 2020. "Experimental Evidence on Artificial Intelligence in the Classroom," MPRA Paper 103934, University Library of Munich, Germany.