Report NEP-BIG-2020-08-24

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:

• Hellerstein, Judith K. & Neumark, David, 2020, "Social Capital, Networks, and Economic Wellbeing," IZA Discussion Papers, IZA Network @ LISER, number 13413, Jun.
• Ivan Slobozhan & Peter Ormosi & Rajesh Sharma, 2020, "Which bills are lobbied? Predicting and interpreting lobbying activity in the US," Working Paper series, University of East Anglia, Centre for Competition Policy (CCP), Centre for Competition Policy, University of East Anglia, Norwich, UK., number 2020-03, Jan.
• Yang Li & Yi Pan, 2020, "A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News," Papers, arXiv.org, number 2007.12620, Jul.
• Henri Bourdeau & Corentin Petit & Christophe Midler, 2019, "Du concept à la mise en œuvre du machine learning dans les entreprises : L'expérience de Datapred," Post-Print, HAL, number hal-02873935, Jun.
• Yongtong Shao & Minghao Li & Dermot J. Hayes & Wendong Zhang & Tao Xiong & Wei Xie, 2020, "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," Center for Agricultural and Rural Development (CARD) Publications, Center for Agricultural and Rural Development (CARD) at Iowa State University, number 20-wp607, Aug.
• Marcin Chlebus & Michał Dyczko & Michał Woźniak, 2020, "Nvidia’s stock returns prediction using machine learning techniques for time series forecasting problem," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-22.
• Dominique Guegan, 2020, "A Note on the Interpretability of Machine Learning Algorithms," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers), HAL, number halshs-02900929, Jul.
• Dominique Guégan, 2020, "A Note on the Interpretability of Machine Learning Algorithms," Documents de travail du Centre d'Economie de la Sorbonne, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, number 20012, Jul.
• Longbing Cao & Qiang Yang & Philip S. Yu, 2020, "Data science and AI in FinTech: An overview," Papers, arXiv.org, number 2007.12681, Jul, revised Jul 2021.
• A. R. Provenzano & D. Trifir`o & A. Datteo & L. Giada & N. Jean & A. Riciputi & G. Le Pera & M. Spadaccino & L. Massaron & C. Nordio, 2020, "Machine Learning approach for Credit Scoring," Papers, arXiv.org, number 2008.01687, Jul.
• von Essen, Emma & Jansson, Joakim, 2020, "Misogynistic and xenophobic hate language online: a matter of anonymity," Working Paper Series, Stockholm University, Swedish Institute for Social Research, number 7/2020, Aug.
• K., Sai Manoj & Aithal, Sreeramana, 2020, "Data Mining and Machine Learning Techniques for Cyber Security Intrusion Detection," MPRA Paper, University Library of Munich, Germany, number 101753, Feb.
• Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020, "Macroeconomic Data Transformations Matter," Papers, arXiv.org, number 2008.01714, Aug, revised Mar 2021.
• Neng-Chieh Chang, 2020, "The Mode Treatment Effect," Papers, arXiv.org, number 2007.11606, Jul.
• Anindya Goswami & Sharan Rajani & Atharva Tanksale, 2020, "Data-Driven Option Pricing using Single and Multi-Asset Supervised Learning," Papers, arXiv.org, number 2008.00462, Aug, revised Dec 2020.
• Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2020, "Local mortality estimates during the COVID-19 pandemic in Italy," Working Papers, Sapienza University of Rome, DISS, number 14/20, Jul.
• María Florencia Camusso & Ramiro Emmanuel Jorge, 2019, "Google Correlate y Google Trends como herramientas para realizar un nowcast de las ventas minoristas," Asociación Argentina de Economía Política: Working Papers, Asociación Argentina de Economía Política, number 4127, Nov.
• Prat, Andrea & Montiel Olea , José Luis & Ortoleva, Pietro & Pai, Mallesh, 2019, "Competing Models," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14066, Oct.
• Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2020, "Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Working papers, University of Connecticut, Department of Economics, number 2020-10, Aug.
• Illya Barziy & Marcin Chlebus, 2020, "HRP performance comparison in portfolio optimization under various codependence and distance metrics," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-21.
• Zhongfang Zhuang & Chin-Chia Michael Yeh & Liang Wang & Wei Zhang & Junpeng Wang, 2020, "Multi-stream RNN for Merchant Transaction Prediction," Papers, arXiv.org, number 2008.01670, Jul.
• Chao Deng & Xizhi Su & Chao Zhou, 2020, "Relative wealth concerns with partial information and heterogeneous priors," Papers, arXiv.org, number 2007.11781, Jul.
• Munoz,Juan Eduardo & Gallegos Munoz,Jose Victor & Olivieri,Sergio Daniel, 2020, "Big Data for Sampling Design : The Venezuelan Migration Crisis in Ecuador," Policy Research Working Paper Series, The World Bank, number 9329, Jul.
• Kwadwo Osei Bonsu, 2020, "Weighted Accuracy Algorithmic Approach In Counteracting Fake News And Disinformation," Papers, arXiv.org, number 2008.01535, Jul, revised Aug 2020.
• Alessandro Gnoatto & Athena Picarelli & Christoph Reisinger, 2020, "Deep xVA solver - A neural network based counterparty credit risk management framework," Working Papers, University of Verona, Department of Economics, number 07/2020, May.

Report NEP-BIG-2020-08-24

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: