Report NEP-BIG-2021-03-22

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:

• Steve J. Bickley & Alison Macintyre & Benno Torgler, 2021, "Artificial Intelligence and Big Data in Sustainable Entrepreneurship," CREMA Working Paper Series, Center for Research in Economics, Management and the Arts (CREMA), number 2021-11, Mar.
• Ly, Racine & Traoré, Fousseini & Dia, Khadim, 2021, "Forecasting commodity prices using long-short-term memory neural networks," IFPRI discussion papers, International Food Policy Research Institute (IFPRI), number 2000.
• Item repec:hal:wpaper:hal-03154116 is not listed on IDEAS anymore

• Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021, "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers, arXiv.org, number 2103.09750, Mar.
• Yusen Lin & Jinming Xue & Louiqa Raschid, 2021, "Predicting the Behavior of Dealers in Over-The-Counter Corporate Bond Markets," Papers, arXiv.org, number 2103.09098, Mar.
• Luigi Longo & Massimo Riccaboni & Armando Rungi, 2021, "A Neural Network Ensemble Approach for GDP Forecasting," Working Papers, IMT School for Advanced Studies Lucca, number 02/2021, Mar, revised Mar 2021.
• Liu, Liang & Yang, Kun & Fujii, Hidemichi & Liu, Jun, 2021, "Artificial Intelligence and Energy Intensity in China’s Industrial Sector: Effect and Transmission Channel," MPRA Paper, University Library of Munich, Germany, number 106333, Mar.
• J. Ignacio Conde-Ruiz & Juan José Ganuza & Manu García & Luis A. Puch, 2021, "Gender Distribution across Topics in Top 5 Economics Journals: A Machine Learning Approach," Working Papers, FEDEA, number 2021-07, Mar.
• Anthony Strittmatter & Conny Wunsch, 2021, "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," CESifo Working Paper Series, CESifo, number 8912.
• Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021, "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers, arXiv.org, number 2103.09603, Mar, revised Jun 2024.
• Mengda Li & Charles-Albert Lehalle, 2021, "Do Word Embeddings Really Understand Loughran-McDonald's Polarities?," Papers, arXiv.org, number 2103.09813, Mar.
• Qi Tang & Tongmei Fan & Ruchen Shi & Jingyan Huang & Yidan Ma, 2021, "Prediction of financial time series using LSTM and data denoising methods," Papers, arXiv.org, number 2103.03505, Mar.
• Sturm, Timo & Gerlach, Jin & Pumplun, Luisa & Mesbah, Neda & Peters, Felix & Tauchert, Christoph & Nan, Ning & Buxmann, Peter, 2021, "Coordinating Human and Machine Learning for Effective Organizational Learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL), Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL), number 125653, Mar.
• Jin Li & Ye Luo & Zigan Wang & Xiaowei Zhang, 2021, "Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity," Papers, arXiv.org, number 2103.04021, Mar, revised Dec 2024.
• Alexander J. M. Kell & A. Stephen McGough & Matthew Forshaw, 2021, "The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets," Papers, arXiv.org, number 2103.04327, Mar.
• Michel Denuit & Arthur Charpentier & Julien Trufin, 2021, "Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning," Papers, arXiv.org, number 2103.03635, Mar, revised Jul 2021.
• Kirsten Hillebrand & Lars Hornuf, 2021, "The Social Dilemma of Big Data: Donating Personal Data to Promote Social Welfare," CESifo Working Paper Series, CESifo, number 8926.
• Raymond C. W. Leung & Yu-Man Tam, 2021, "Statistical Arbitrage Risk Premium by Machine Learning," Papers, arXiv.org, number 2103.09987, Mar.
• Hou, Bohan, 2021, "A Novel Data Governance Scheme Based on the Behavioral Economics Theory," SocArXiv, Center for Open Science, number 2b9dc, Jan, DOI: 10.31219/osf.io/2b9dc.
• Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021, "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers, arXiv.org, number 2103.10251, Mar, revised Sep 2021.
• Matteo Garzoli & Alberto Plazzi & Rossen I. Valkanov, 2021, "Backcasting, Nowcasting, and Forecasting Residential Repeat-Sales Returns: Big Data meets Mixed Frequency," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-21, Mar.
• Item repec:smo:conswp:032mb is not listed on IDEAS anymore

Report NEP-BIG-2021-03-22

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: