Report NEP-BIG-2020-06-29

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:

• Nils Kobis & Luca Mossink, 2020. "Artificial Intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry," Papers 2005.09980, arXiv.org, revised Sep 2020.
• Marcin Chlebus & Maciej Stefan Świtała, 2020. "So close and so far. Finding similar tendencies in econometrics and machine learning papers. Topic models comparison," Working Papers 2020-16, Faculty of Economic Sciences, University of Warsaw.
• Takanobu Mizuta, 2020. "Does an artificial intelligence perform market manipulation with its own discretion? -- A genetic algorithm learns in an artificial market simulation," Papers 2005.10488, arXiv.org.
• Alain Naef, 2020. "Blowing against the Wind? A Narrative Approach to Central Bank Foreign Exchange Intervention," Working Papers 0188, European Historical Economics Society (EHES).
• Franklin Allen & Julapa Jagtiani, 2020. "A Survey of Fintech Research and Policy Discussion," Working Papers 20-21, Federal Reserve Bank of Philadelphia.
• Jie Fang & Jianwu Lin, 2020. "Prior knowledge distillation based on financial time series," Papers 2006.09247, arXiv.org, revised Nov 2020.
• Matteo Gambara & Josef Teichmann, 2020. "Consistent Recalibration Models and Deep Calibration," Papers 2006.09455, arXiv.org, revised Jul 2021.
• Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Short-term forecasting of the Coronavirus Pandemic - 2020-04-27," Economics Papers 2020-W06, Economics Group, Nuffield College, University of Oxford.
• Laura Leal & Mathieu Lauri`ere & Charles-Albert Lehalle, 2020. "Learning a functional control for high-frequency finance," Papers 2006.09611, arXiv.org, revised Feb 2021.
• Dhagash Mehta & Dhruv Desai & Jithin Pradeep, 2020. "Machine Learning Fund Categorizations," Papers 2006.00123, arXiv.org.
• Sumit Agarwal & John Grigsby & Ali Hortaçsu & Gregor Matvos & Amit Seru & Vincent Yao, 2020. "Searching for Approval," NBER Working Papers 27341, National Bureau of Economic Research, Inc.
• Stetter, Christian & Mennig, Philipp & Sauer, Johannes, 2020. "Going Beyond Average – Using Machine Learning to Evaluate the Effectiveness of Environmental Subsidies at Micro-Level," 94th Annual Conference, April 15-17, 2020, K U Leuven, Belgium (Cancelled) 303699, Agricultural Economics Society - AES.
• codagnone, cristiano & Bogliacino, Francesco & Gómez, Camilo Ernesto & Charris, Rafael Alberto & Montealegre, Felipe & Liva, Giovanni & Villanueva, Francisco Lupiañez & Folkvord, F. & Veltri, Giuseppe, 2020. "Assessing concerns for the economic consequence of the COVID-19 response and mental health problems associated with economic vulnerability and negative economic shock in Italy, Spain, and the United K," SocArXiv x9m36, Center for Open Science.
• Karolina Sowinska & Pranava Madhyastha, 2020. "A Tweet-based Dataset for Company-Level Stock Return Prediction," Papers 2006.09723, arXiv.org.
• Yun-Cheng Tsai & Chun-Chieh Wang, 2019. "Deep Reinforcement Learning for Foreign Exchange Trading," Papers 1908.08036, arXiv.org, revised Jun 2020.
• Carvalho, V & Garcia, Juan R. & Hansen, S. & Ortiz, A. & Rodrigo, T. & More, J. V. R., 2020. "Tracking the COVID-19 Crisis with High-Resolution Transaction Data," Cambridge Working Papers in Economics 2030, Faculty of Economics, University of Cambridge.
• Hansen, Stephen & Carvalho, Vasco & García, Juan Ramón & Ortiz, Alvaro & Rodrigo, Tomasa & Rodríguez Mora, José V & Ruiz, Pep, 2020. "Tracking the COVID-19 Crisis with High-Resolution Transaction Data," CEPR Discussion Papers 14642, C.E.P.R. Discussion Papers.
• Gallego, J & Prem, M & Vargas, J. F, 2020. "Corruption in the times of pandemia," Documentos de Trabajo 18178, Universidad del Rosario.
• Thuy D. Nguyen & Sumedha Gupta & Martin Andersen & Ana Bento & Kosali I. Simon & Coady Wing, 2020. "Impacts of State Reopening Policy on Human Mobility," NBER Working Papers 27235, National Bureau of Economic Research, Inc.
• Jun-Hao Chen & Samuel Yen-Chi Chen & Yun-Cheng Tsai & Chih-Shiang Shur, 2020. "Adversarial Robustness of Deep Convolutional Candlestick Learner," Papers 2006.03686, arXiv.org.
• Fred Espen Benth & Nils Detering & Silvia Lavagnini, 2020. "Accuracy of Deep Learning in Calibrating HJM Forward Curves," Papers 2006.01911, arXiv.org, revised May 2021.
• Malo Huard & Rémy Garnier & Gilles Stoltz, 2020. "Hierarchical robust aggregation of sales forecasts at aggregated levels in e-commerce, based on exponential smoothing and Holt's linear trend method," Working Papers hal-02794320, HAL.
• Pietro Rossi & Flavio Cocco & Giacomo Bormetti, 2020. "Deep learning Profit & Loss," Papers 2006.09955, arXiv.org, revised Aug 2020.