Report NEP-BIG-2020-11-09

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:

• Abhiroop Mukherjee & George Panayotov & Janghoon Shon, 2020, "Eye in the Sky: Private Satellites and Government Macro Data," HKUST IEMS Thought Leadership Brief Series, HKUST Institute for Emerging Market Studies, number 2020-42, Sep, revised Sep 2020.
• Yucheng Yang & Zhong Zheng & Weinan E, 2020, "Interpretable Neural Networks for Panel Data Analysis in Economics," Papers, arXiv.org, number 2010.05311, Oct, revised Nov 2020.
• Vicinanza, Paul & Goldberg, Amir & Srivastava, Sameer B., 2020, "Who Sees the Future? A Deep Learning Language Model Demonstrates the Vision Advantage of Being Small," Research Papers, Stanford University, Graduate School of Business, number 3869, May.
• Dan Wang & Tianrui Wang & Ionuc{t} Florescu, 2020, "Is Image Encoding Beneficial for Deep Learning in Finance? An Analysis of Image Encoding Methods for the Application of Convolutional Neural Networks in Finance," Papers, arXiv.org, number 2010.08698, Oct.
• Miquel Noguer i Alonso & Sonam Srivastava, 2020, "Deep Reinforcement Learning for Asset Allocation in US Equities," Papers, arXiv.org, number 2010.04404, Oct.
• Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020, "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," OSF Preprints, Center for Open Science, number yc6e2, Oct, DOI: 10.31219/osf.io/yc6e2.
• Patrick T. Harker, 2020, "The Economy, the Pandemic, and Machine Learning," Speech, Federal Reserve Bank of Philadelphia, number 88805, Sep.
• Yucheng Yang & Yue Pang & Guanhua Huang & Weinan E, 2020, "The Knowledge Graph for Macroeconomic Analysis with Alternative Big Data," Papers, arXiv.org, number 2010.05172, Oct.
• Ma, Ji, 2020, "Automated coding using machine-learning and remapping the U.S. nonprofit sector: A guide and benchmark," OSF Preprints, Center for Open Science, number pt3q9, Oct, DOI: 10.31219/osf.io/pt3q9.
• Sean Cao & Wei Jiang & Baozhong Yang & Alan L. Zhang, 2020, "How to Talk When a Machine is Listening?: Corporate Disclosure in the Age of AI," NBER Working Papers, National Bureau of Economic Research, Inc, number 27950, Oct.
• Ollech, Daniel & Webel, Karsten, 2020, "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers, Deutsche Bundesbank, number 55/2020.
• Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay, 2020, "Bridging the gap between Markowitz planning and deep reinforcement learning," Papers, arXiv.org, number 2010.09108, Sep.
• Patrick T. Harker, 2020, "The Pandemic, Automation, and Artificial Intelligence: Executive Briefing: AI and Machine Learning," Speech, Federal Reserve Bank of Philadelphia, number 88840, Oct.
• Taeyoung Doh & Dongho Song & Shu-Kuei X. Yang, 2020, "Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 20-14, Oct, revised 16 Oct 2025, DOI: 10.18651/RWP2020-14.
• Item repec:esm:wpaper:esmt-20-01_r1 is not listed on IDEAS anymore

• Stubbers, Micha�la & Holvoet, Nathalie, 2020, "Big data for poverty measurement: insights from a scoping review," IOB Discussion Papers, Universiteit Antwerpen, Institute of Development Policy (IOB), number 2020.03, Oct.
• Rangan Gupta & Christian Pierdzioch & Afees A. Salisu, 2020, "Oil-Price Uncertainty and the U.K. Unemployment Rate: A Forecasting Experiment with Random Forests Using 150 Years of Data," Working Papers, University of Pretoria, Department of Economics, number 202095, Oct.
• Matt Marx & Aaron Fuegi, 2020, "Reliance on Science by Inventors: Hybrid Extraction of In-text Patent-to-Article Citations," NBER Working Papers, National Bureau of Economic Research, Inc, number 27987, Oct.
• Shenhao Wang & Baichuan Mo & Jinhua Zhao, 2020, "Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks," Papers, arXiv.org, number 2010.11644, Oct.
• Elior Nehemya & Yael Mathov & Asaf Shabtai & Yuval Elovici, 2020, "Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders," Papers, arXiv.org, number 2010.09246, Oct, revised Sep 2021.
• Huber, Martin & Imhof, David, 2020, "Transnational machine learning with screens for flagging bid-rigging cartels," FSES Working Papers, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland, number 519, Oct.
• Patrick Bareinz & Silke Uebelmesser, 2020, "The Role of Information Provision for Attitudes Towards Immigration: An Experimental Investigation," CESifo Working Paper Series, CESifo, number 8635.
• Peiwan Wang & Lu Zong, 2020, "Are Crises Predictable? A Review of the Early Warning Systems in Currency and Stock Markets," Papers, arXiv.org, number 2010.10132, Oct.
• Yoko KONISHI & Takashi SAITO & Toshiki ISHIKAWA & Naoya IGEI, 2020, "How did Japan cope with COVID-19? Big Data and purchasing behavior (Japanese)," Discussion Papers (Japanese), Research Institute of Economy, Trade and Industry (RIETI), number 20037, Sep.
• Nowosad, Jakub, 2020, "Motif: an open-source R tool for pattern-based spatial analysis," EcoEvoRxiv, Center for Open Science, number kj7fu, Oct, DOI: 10.31219/osf.io/kj7fu.

Report NEP-BIG-2020-11-09

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: