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 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:

• Abhiroop Mukherjee & George Panayotov & Janghoon Shon, 2020. "Eye in the Sky: Private Satellites and Government Macro Data," HKUST IEMS Thought Leadership Brief Series 2020-42, HKUST Institute for Emerging Market Studies, revised Sep 2020.
• Yucheng Yang & Zhong Zheng & Weinan E, 2020. "Interpretable Neural Networks for Panel Data Analysis in Economics," Papers 2010.05311, arXiv.org, 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 3869, Stanford University, Graduate School of Business.
• 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 2010.08698, arXiv.org.
• Miquel Noguer i Alonso & Sonam Srivastava, 2020. "Deep Reinforcement Learning for Asset Allocation in US Equities," Papers 2010.04404, arXiv.org.
• 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 yc6e2, Center for Open Science.
• Patrick T. Harker, 2020. "The Economy, the Pandemic, and Machine Learning," Speech 88805, Federal Reserve Bank of Philadelphia.
• Yucheng Yang & Yue Pang & Guanhua Huang & Weinan E, 2020. "The Knowledge Graph for Macroeconomic Analysis with Alternative Big Data," Papers 2010.05172, arXiv.org.
• Ma, Ji, 2020. "Automated coding using machine-learning and remapping the U.S. nonprofit sector: A guide and benchmark," OSF Preprints pt3q9, Center for Open Science.
• 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 27950, National Bureau of Economic Research, Inc.
• Ollech, Daniel & Webel, Karsten, 2020. "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers 55/2020, Deutsche Bundesbank.
• Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay, 2020. "Bridging the gap between Markowitz planning and deep reinforcement learning," Papers 2010.09108, arXiv.org.
• Patrick T. Harker, 2020. "The Pandemic, Automation, and Artificial Intelligence: Executive Briefing: AI and Machine Learning," Speech 88840, Federal Reserve Bank of Philadelphia.
• Taeyoung Doh & Dongho Song & Shu-Kuei X. Yang, 2020. "Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements," Research Working Paper RWP 20-14, Federal Reserve Bank of Kansas City.
• Huseyin Gurkan & Francis de Véricourt, 2020. "Contracting, pricing, and data collection under the AI flywheel effect," ESMT Research Working Papers ESMT-20-01_R1, ESMT European School of Management and Technology, revised 19 Oct 2020.
• Stubbers, Michaëla & Holvoet, Nathalie, 2020. "Big data for poverty measurement: insights from a scoping review," IOB Discussion Papers 2020.03, Universiteit Antwerpen, Institute of Development Policy (IOB).
• 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 202095, University of Pretoria, Department of Economics.
• Matt Marx & Aaron Fuegi, 2020. "Reliance on Science by Inventors: Hybrid Extraction of In-text Patent-to-Article Citations," NBER Working Papers 27987, National Bureau of Economic Research, Inc.
• Shenhao Wang & Baichuan Mo & Jinhua Zhao, 2020. "Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks," Papers 2010.11644, arXiv.org.
• Elior Nehemya & Yael Mathov & Asaf Shabtai & Yuval Elovici, 2020. "Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders," Papers 2010.09246, arXiv.org, revised Sep 2021.
• Huber, Martin & Imhof, David, 2020. "Transnational machine learning with screens for flagging bid-rigging cartels," FSES Working Papers 519, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
• Patrick Bareinz & Silke Uebelmesser, 2020. "The Role of Information Provision for Attitudes Towards Immigration: An Experimental Investigation," CESifo Working Paper Series 8635, CESifo.
• Peiwan Wang & Lu Zong, 2020. "Are Crises Predictable? A Review of the Early Warning Systems in Currency and Stock Markets," Papers 2010.10132, arXiv.org.
• KONISHI Yoko & SAITO Takashi & ISHIKAWA Toshiki & IGEI Naoya, 2020. "How did Japan cope with COVID-19? Big Data and purchasing behavior (Japanese)," Discussion Papers (Japanese) 20037, Research Institute of Economy, Trade and Industry (RIETI).
• Nowosad, Jakub, 2020. "Motif: an open-source R tool for pattern-based spatial analysis," EcoEvoRxiv kj7fu, Center for Open Science.