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 (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:

• Nils Kobis & Luca Mossink, 2020, "Artificial Intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry," Papers, arXiv.org, number 2005.09980, May, 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, Faculty of Economic Sciences, University of Warsaw, number 2020-16.
• Takanobu Mizuta, 2020, "Does an artificial intelligence perform market manipulation with its own discretion? -- A genetic algorithm learns in an artificial market simulation," Papers, arXiv.org, number 2005.10488, May.
• Alain Naef, 2020, "Blowing against the Wind? A Narrative Approach to Central Bank Foreign Exchange Intervention," Working Papers, European Historical Economics Society (EHES), number 0188, Jun.
• Franklin Allen & Julapa Jagtiani, 2020, "A Survey of Fintech Research and Policy Discussion," Working Papers, Federal Reserve Bank of Philadelphia, number 20-21, May, DOI: 10.21799/frbp.wp.2020.21.
• Jie Fang & Jianwu Lin, 2020, "Prior knowledge distillation based on financial time series," Papers, arXiv.org, number 2006.09247, Jun, revised Nov 2020.
• Matteo Gambara & Josef Teichmann, 2020, "Consistent Recalibration Models and Deep Calibration," Papers, arXiv.org, number 2006.09455, Jun, 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, Economics Group, Nuffield College, University of Oxford, number 2020-W06, Apr.
• Laura Leal & Mathieu Lauri`ere & Charles-Albert Lehalle, 2020, "Learning a functional control for high-frequency finance," Papers, arXiv.org, number 2006.09611, Jun, revised Feb 2021.
• Dhagash Mehta & Dhruv Desai & Jithin Pradeep, 2020, "Machine Learning Fund Categorizations," Papers, arXiv.org, number 2006.00123, May.
• Sumit Agarwal & John Grigsby & Ali Hortaçsu & Gregor Matvos & Amit Seru & Vincent Yao, 2020, "Searching for Approval," NBER Working Papers, National Bureau of Economic Research, Inc, number 27341, Jun.
• 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), Agricultural Economics Society - AES, number 303699, Apr, DOI: 10.22004/ag.econ.303699.
• 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 Kingdom," SocArXiv, Center for Open Science, number x9m36, May, DOI: 10.31219/osf.io/x9m36.
• Karolina Sowinska & Pranava Madhyastha, 2020, "A Tweet-based Dataset for Company-Level Stock Return Prediction," Papers, arXiv.org, number 2006.09723, Jun.
• Yun-Cheng Tsai & Chun-Chieh Wang, 2019, "Deep Reinforcement Learning for Foreign Exchange Trading," Papers, arXiv.org, number 1908.08036, Aug, 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, Faculty of Economics, University of Cambridge, number 2030, Apr.
• 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, C.E.P.R. Discussion Papers, number 14642, Apr.
• J Gallego & M Prem & J. F Vargas, 2020, "Corruption in the times of pandemia," Documentos de Trabajo, Universidad del Rosario, number 18178, May.
• 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, National Bureau of Economic Research, Inc, number 27235, May.
• Jun-Hao Chen & Samuel Yen-Chi Chen & Yun-Cheng Tsai & Chih-Shiang Shur, 2020, "Adversarial Robustness of Deep Convolutional Candlestick Learner," Papers, arXiv.org, number 2006.03686, May.
• Fred Espen Benth & Nils Detering & Silvia Lavagnini, 2020, "Accuracy of Deep Learning in Calibrating HJM Forward Curves," Papers, arXiv.org, number 2006.01911, Jun, 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, number hal-02794320, Jun.
• Pietro Rossi & Flavio Cocco & Giacomo Bormetti, 2020, "Deep learning Profit & Loss," Papers, arXiv.org, number 2006.09955, Jun, revised Aug 2020.

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 (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: