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 (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:

• Giuseppe Brandi & T. Di Matteo, 2020, "A new multilayer network construction via Tensor learning," Papers, arXiv.org, number 2004.05367, Apr.
• Hannes Wallimann & David Imhof & Martin Huber, 2020, "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," Papers, arXiv.org, number 2004.05629, Apr.
• Makoto YANO & Yuichi FURUKAWA, 2020, "Economic Black Holes and Labor Singularities in the Presence of Self-replicating Artificial Intelligence," Discussion papers, Research Institute of Economy, Trade and Industry (RIETI), number 20009, Feb.
• 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, arXiv.org, number 2004.05940, Apr.
• Jonghyeon Min, 2020, "Financial Market Trend Forecasting and Performance Analysis Using LSTM," Papers, arXiv.org, number 2004.01502, Mar.
• 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, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202017.
• Philip Ndikum, 2020, "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers, arXiv.org, number 2004.01504, Mar.
• Mahdi Ghodsi & Oliver Reiter & Robert Stehrer & Roman Stöllinger, 2020, "Robotisation, Employment and Industrial Growth Intertwined Across Global Value Chains," wiiw Working Papers, The Vienna Institute for International Economic Studies, wiiw, number 177, Apr.
• Sven Husmann & Antoniya Shivarova & Rick Steinert, 2020, "Company classification using machine learning," Papers, arXiv.org, number 2004.01496, Mar, 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, University of Hagen, Center for East Asia Macro-economic Studies (CEAMeS), number 15/2018, revised 2020, DOI: 10.18445/20190124-114606-0.
• Ye-Sheen Lim & Denise Gorse, 2020, "Deep Probabilistic Modelling of Price Movements for High-Frequency Trading," Papers, arXiv.org, number 2004.01498, Mar.
• Mojtaba Nabipour & Pooyan Nayyeri & Hamed Jabani & Amir Mosavi, 2020, "Deep learning for Stock Market Prediction," Papers, arXiv.org, number 2004.01497, Mar.
• Ye-Sheen Lim & Denise Gorse, 2020, "Deep Recurrent Modelling of Stationary Bitcoin Price Formation Using the Order Flow," Papers, arXiv.org, number 2004.01499, Mar.
• Jim Samuel, 2020, "Information Token Driven Machine Learning for Electronic Markets: Performance Effects in Behavioral Financial Big Data Analytics," Papers, arXiv.org, number 2004.06642, Mar.
• Kenta IKEUCHI & Kazuyuki MOTOHASHI, 2020, "Linkage of Patent and Design Right Data: Analysis of Industrial Design Activities in Companies at the Creator Level," Discussion papers, Research Institute of Economy, Trade and Industry (RIETI), number 20005, Jan.
• Sandrine Gumbel & Thorsten Schmidt, 2020, "Machine learning for multiple yield curve markets: fast calibration in the Gaussian affine framework," Papers, arXiv.org, number 2004.07736, Apr, revised Apr 2020.
• Thibaut Th'eate & Damien Ernst, 2020, "An Application of Deep Reinforcement Learning to Algorithmic Trading," Papers, arXiv.org, number 2004.06627, Apr, revised Oct 2020.
• Masashi Goto, 2020, "Theorization of Institutional Change in the Rise of Artificial Intelligence," Discussion Paper Series, Research Institute for Economics & Business Administration, Kobe University, number DP2020-12, Mar.
• Guy Aridor & Yeon-Koo Che & Tobias Salz, 2020, "The Effect of Privacy Regulation on the Data Industry: Empirical Evidence from GDPR," NBER Working Papers, National Bureau of Economic Research, Inc, number 26900, Mar.
• Jonathan Sadighian, 2020, "Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making," Papers, arXiv.org, number 2004.06985, Apr.
• Daniel Bjorkegren & Joshua E. Blumenstock & Samsun Knight, 2020, "Manipulation-Proof Machine Learning," Papers, arXiv.org, number 2004.03865, Apr.
• Nik Dawson & Marian-Andrei Rizoiu & Benjamin Johnston & Mary-Anne Williams, 2020, "Predicting Skill Shortages in Labor Markets: A Machine Learning Approach," Papers, arXiv.org, number 2004.01311, Apr, revised Aug 2020.
• Michele Loberto & Andrea Luciani & Marco Pangallo, 2020, "What do online listings tell us about the housing market?," Papers, arXiv.org, number 2004.02706, Apr.
• Masayuki MORIKAWA, 2020, "Heterogeneous Relationships between Automation Technologies and Skilled Labor: Evidence from a Firm Survey," Discussion papers, Research Institute of Economy, Trade and Industry (RIETI), number 20004, Jan.

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 (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: