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Report NEP-BIG-2020-10-26
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:
- Saeed Nosratabadi & Amir Mosavi & Ramin Keivani & Sina Ardabili & Farshid Aram, 2020, "State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability," Papers, arXiv.org, number 2010.02670, Oct.
- Sidra Mehtab & Jaydip Sen & Abhishek Dutta, 2020, "Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models," Papers, arXiv.org, number 2009.10819, Sep.
- Ya Chen & Mike Tsionas & Valentin Zelenyuk, 2020, "LASSO DEA for small and big data," CEPA Working Papers Series, School of Economics, University of Queensland, Australia, number WP092020, Oct.
- Hannes Mueller & Andre Groger & Jonathan Hersh & Andrea Matranga & Joan Serrat, 2020, "Monitoring War Destruction from Space: A Machine Learning Approach," Papers, arXiv.org, number 2010.05970, Oct, revised Oct 2020.
- Cosimo Magazzino & Marco Mele & Nicolas Schneider & Guillaume Vallet, 2020, "The relationship between nuclear energy consumption and economic growth: evidence from Switzerland," Post-Print, HAL, number halshs-02951860, Sep, DOI: 10.1088/1748-9326/abadcd.
- Thomas Dierckx & Jesse Davis & Wim Schoutens, 2020, "Using Machine Learning and Alternative Data to Predict Movements in Market Risk," Papers, arXiv.org, number 2009.07947, Sep.
- Anastasios Petropoulos & Vassilis Siakoulis & Konstantinos P. Panousis & Loukas Papadoulas & Sotirios Chatzis, 2020, "A Deep Learning Approach for Dynamic Balance Sheet Stress Testing," Papers, arXiv.org, number 2009.11075, Sep, revised Sep 2022.
- Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020, "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers, Sapienza University of Rome, DISS, number 16/20, Sep.
- Steven J. Davis & Stephen Hansen & Cristhian Seminario-Amez, 2020, "Firm-Level Risk Exposures and Stock Returns in the Wake of Covid-19," CESifo Working Paper Series, CESifo, number 8594.
- Olivier Darné & Amelie Charles, 2020, "Nowcasting GDP growth using data reduction methods: Evidence for the French economy," Post-Print, HAL, number hal-02948802, Sep.
- Albarrán, Irene & Molina, José Manuel & Gijón, Covadonga, 2020, "Perception of Artificial Intelligence in Spain," ITS Conference, Online Event 2020, International Telecommunications Society (ITS), number 224843.
- Junfeng Hu & Xiaosa Li & Yuru Xu & Shaowu Wu & Bin Zheng, 2020, "Evaluation of company investment value based on machine learning," Papers, arXiv.org, number 2010.01996, Sep.
- Andreas Gulyas & Krzysztof Pytka, 2020, "The Consequences of the COVID-19 Job Losses: Who Will Suffer Most and by How Much?," CRC TR 224 Discussion Paper Series, University of Bonn and University of Mannheim, Germany, number crctr224_2020_212, Sep.
- Pedro M. Gardete & Carlos D. Santos, 2020, "No data? No problem! A Search-based Recommendation System with Cold Starts," Papers, arXiv.org, number 2010.03455, Oct.
- Grzegorz Krochmal, 2020, "Sentiment of tweets and socio-economic characteristics as the determinants of voting behavior at the regional level. Case study of 2019 Polish parliamentary election," Papers, arXiv.org, number 2010.03493, Oct.
- Xing Wang & Yijun Wang & Bin Weng & Aleksandr Vinel, 2020, "Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network," Papers, arXiv.org, number 2010.01197, Sep.
- Hamed Vaheb, 2020, "Asset Price Forecasting using Recurrent Neural Networks," Papers, arXiv.org, number 2010.06417, Oct, revised Oct 2020.
- Bruno Spilak & Wolfgang Karl Hardle, 2020, "Tail-risk protection: Machine Learning meets modern Econometrics," Papers, arXiv.org, number 2010.03315, Oct, revised Aug 2021.
- Oskar KOWALEWSKI & Paweł PISANY, 2020, "The Rise of Fintech: A Cross-Country Perspective," Working Papers, IESEG School of Management, number 2020-ACF-07, Jul.
- Susanto, Stefanny Magdalena, 2020, "Influence of big data and analysis of orientation effect on firm performance," OSF Preprints, Center for Open Science, number sjdpf, Sep, DOI: 10.31219/osf.io/sjdpf.
- Artur Sokolovsky & Luca Arnaboldi, 2020, "A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components," Papers, arXiv.org, number 2009.09993, Sep, revised Jun 2022.
- Andrew Clark, 2020, "A Pound Centric look at the Pound vs. Krona Exchange Rate Movement from 1844 to 1965," Economics Discussion Papers, Department of Economics, University of Reading, number em-dp2020-22, Oct.
- Lea Bernhardt, 2020, "Common factors of withdrawn and prohibited mergers in the European Union," Working Paper, Helmut Schmidt University, Hamburg, number 184/2020, Oct.
- Qi Zhao, 2020, "A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data," Papers, arXiv.org, number 2010.07404, Oct.
- Joshua Wilde & Wei Chen & Sophie Lohmann, 2020, "COVID-19 and the future of US fertility: what can we learn from Google?," MPIDR Working Papers, Max Planck Institute for Demographic Research, Rostock, Germany, number WP-2020-034, DOI: 10.4054/MPIDR-WP-2020-034.
- Avinash Collis & Alex Moehring & Ananya Sen, 2020, "Economic Value of Data: Quantification Using Online Experiments," Working Papers, NET Institute, number 20-13, Oct.
- Kluge, Jan & Lappoehn, Sarah & Plank, Kerstin, 2020, "The Determinants of Economic Competitiveness," IHS Working Paper Series, Institute for Advanced Studies, number 24, Oct.