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Report NEP-BIG-2021-01-11
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:
- Anna Baiardi & Andrea A. Naghi, 2021, "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 21-001/V, Jan.
- Akyildirim, Erdinc & Cepni, Oguzhan & Corbet, Shaen & Uddin, Gazi Salah, 2020, "Forecasting Mid-price Movement of Bitcoin Futures Using Machine Learning," Working Papers, Copenhagen Business School, Department of Economics, number 20-2020, Dec.
- Turan G. Bali & Amit Goyal & Dashan Huang & Fuwei Jiang & Quan Wen, 2020, "The Cross-Sectional Pricing of Corporate Bonds Using Big Data and Machine Learning," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-110, Sep.
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020, "Machine Learning Advances for Time Series Forecasting," Papers, arXiv.org, number 2012.12802, Dec, revised Apr 2021.
- Paul Waddell & Arezoo Besharati-Zadeh, 2020, "A Comparison of Statistical and Machine Learning Algorithms for Predicting Rents in the San Francisco Bay Area," Papers, arXiv.org, number 2011.14924, Nov.
- Katsuya Ito & Kentaro Minami & Kentaro Imajo & Kei Nakagawa, 2020, "Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction," Papers, arXiv.org, number 2012.10215, Dec.
- Cyril Verluise & Gabriele Cristelli & Kyle Higham & Gaetan de Rassenfosse, 2020, "The Missing 15 Percent of Patent Citations," Working Papers, Chair of Science, Technology, and Innovation Policy, number 13, Dec.
- Riu Naito & Toshihiro Yamada, 2020, "A machine learning solver for high-dimensional integrals: Solving Kolmogorov PDEs by stochastic weighted minimization and stochastic gradient descent through a high-order weak approximation scheme of SDEs with Malliavin weights," Papers, arXiv.org, number 2012.12346, Dec, revised Feb 2021.
- Breuer, Wolfgang & Steininger, Bertram, 2020, "Recent Trends in Real Estate Research: A Comparison of Recent Working Papers and Publications using Machine Learning Algorithms," Working Paper Series, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance, number 20/15, Dec.
- Drydakis, Nick, 2020, "Mobile Applications Aiming to Facilitate Immigrants' Societal Integration and Overall Level of Integration, Health and Mental Health: Does Artificial Intelligence Enhance Outcomes?," IZA Discussion Papers, IZA Network @ LISER, number 13933, Dec.
- Olivier Dessaint & Thierry Foucault & Laurent Frésard, 2020, "Does Big Data Improve Financial Forecasting? The Horizon Effect," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-106, Nov.
- Xunyi Wang & Reza Mousavi & Yili Hong, 2020, "The Unintended Consequences of Stay-at-Home Policies on Work Outcomes: The Impacts of Lockdown Orders on Content Creation," Papers, arXiv.org, number 2011.15068, Nov.
- Marc Chataigner & Stephane Crepey & Jiang Pu, 2020, "Nowcasting Networks," Papers, arXiv.org, number 2011.13687, Nov.
- Nikita Gusarov & Amirreza Talebijamalabad & Iragaël Joly, 2020, "Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness," Working Papers, HAL, number hal-03019739, Oct.
- Matteo Sostero, 2020, "Automation and Robots in Services: Review of Data and Taxonomy," JRC Working Papers on Labour, Education and Technology, Joint Research Centre, number 2020-14, Dec.
- Cevat Giray Aksoy & Panu Poutvaara & Felicitas Schikora, 2020, "First Time Around: Local Conditions and Multi-dimensional Integration of Refugees," SOEPpapers on Multidisciplinary Panel Data Research, DIW Berlin, The German Socio-Economic Panel (SOEP), number 1115.
- Lundborg, Martin & Märkel, Christian & Schrade-Grytsenko, Lisa & Stamm, Peter, 2019, "Künstliche Intelligenz im Telekommunikationssektor – Bedeutung, Entwicklungsperspektiven und regulatorische Implikationen," WIK Discussion Papers, WIK Wissenschaftliches Institut für Infrastruktur und Kommunikationsdienste GmbH, number 453.