Report NEP-BIG-2020-04-20

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé issued this report. It is usually issued weekly.

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Other reports in NEP-BIG

The following items were announced in this report:

• Giuseppe Brandi & T. Di Matteo, 2020. "A new multilayer network construction via Tensor learning," Papers 2004.05367, arXiv.org.
• Hannes Wallimann & David Imhof & Martin Huber, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," Papers 2004.05629, arXiv.org.
• YANO Makoto & FURUKAWA Yuichi, 2020. "Economic Black Holes and Labor Singularities in the Presence of Self-replicating Artificial Intelligence," Discussion papers 20009, Research Institute of Economy, Trade and Industry (RIETI).
• Ioannis Boukas & Damien Ernst & Thibaut Th'eate & Adrien Bolland & Alexandre Huynen & Martin Buchwald & Christelle Wynants & Bertrand Corn'elusse, 2020. "A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding," Papers 2004.05940, arXiv.org.
• Jonghyeon Min, 2020. "Financial Market Trend Forecasting and Performance Analysis Using LSTM," Papers 2004.01502, arXiv.org.
• Mohammad Reza Farzanegan & Mehdi Feizi & Saeed Malek Sadati, 2020. "Google It Up! A Google Trends-based analysis of COVID-19 outbreak in Iran," MAGKS Papers on Economics 202017, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
• Philip Ndikum, 2020. "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers 2004.01504, arXiv.org.
• Mahdi Ghodsi & Oliver Reiter & Robert Stehrer & Roman Stöllinger, 2020. "Robotisation, Employment and Industrial Growth Intertwined Across Global Value Chains," wiiw Working Papers 177, The Vienna Institute for International Economic Studies, wiiw.
• Sven Husmann & Antoniya Shivarova & Rick Steinert, 2020. "Company classification using machine learning," Papers 2004.01496, arXiv.org, revised May 2020.
• Glawe, Linda & Wagner, Helmut, 2020. "The Middle-Income Trap 2.0: The Increasing Role of Human Capital in the Age of Automation and Implications for Developing Asia," CEAMeS Discussion Paper Series 15/2018, University of Hagen, Center for East Asia Macro-economic Studies (CEAMeS), revised 2020.
• Ye-Sheen Lim & Denise Gorse, 2020. "Deep Probabilistic Modelling of Price Movements for High-Frequency Trading," Papers 2004.01498, arXiv.org.
• Mojtaba Nabipour & Pooyan Nayyeri & Hamed Jabani & Amir Mosavi, 2020. "Deep learning for Stock Market Prediction," Papers 2004.01497, arXiv.org.
• Ye-Sheen Lim & Denise Gorse, 2020. "Deep Recurrent Modelling of Stationary Bitcoin Price Formation Using the Order Flow," Papers 2004.01499, arXiv.org.
• Jim Samuel, 2020. "Information Token Driven Machine Learning for Electronic Markets: Performance Effects in Behavioral Financial Big Data Analytics," Papers 2004.06642, arXiv.org.
• IKEUCHI Kenta & MOTOHASHI Kazuyuki, 2020. "Linkage of Patent and Design Right Data: Analysis of Industrial Design Activities in Companies at the Creator Level," Discussion papers 20005, Research Institute of Economy, Trade and Industry (RIETI).
• Sandrine Gumbel & Thorsten Schmidt, 2020. "Machine learning for multiple yield curve markets: fast calibration in the Gaussian affine framework," Papers 2004.07736, arXiv.org, revised Apr 2020.
• Thibaut Th'eate & Damien Ernst, 2020. "An Application of Deep Reinforcement Learning to Algorithmic Trading," Papers 2004.06627, arXiv.org, revised Oct 2020.
• Masashi Goto, 2020. "Theorization of Institutional Change in the Rise of Artificial Intelligence," Discussion Paper Series DP2020-12, Research Institute for Economics & Business Administration, Kobe University.
• Guy Aridor & Yeon-Koo Che & Tobias Salz, 2020. "The Effect of Privacy Regulation on the Data Industry: Empirical Evidence from GDPR," NBER Working Papers 26900, National Bureau of Economic Research, Inc.
• Jonathan Sadighian, 2020. "Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making," Papers 2004.06985, arXiv.org.
• Daniel Bjorkegren & Joshua E. Blumenstock & Samsun Knight, 2020. "Manipulation-Proof Machine Learning," Papers 2004.03865, arXiv.org.
• Nik Dawson & Marian-Andrei Rizoiu & Benjamin Johnston & Mary-Anne Williams, 2020. "Predicting Skill Shortages in Labor Markets: A Machine Learning Approach," Papers 2004.01311, arXiv.org, revised Aug 2020.
• Michele Loberto & Andrea Luciani & Marco Pangallo, 2020. "What do online listings tell us about the housing market?," Papers 2004.02706, arXiv.org.
• MORIKAWA Masayuki, 2020. "Heterogeneous Relationships between Automation Technologies and Skilled Labor: Evidence from a Firm Survey," Discussion papers 20004, Research Institute of Economy, Trade and Industry (RIETI).