Report NEP-BIG-2019-08-19

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.

Subscribe to this report: email, RSS, or Mastodon.

Other reports in NEP-BIG

The following items were announced in this report:

• Yingying Lu & Yixiao Zhou, 2019. "A short review on the economics of artificial intelligence," CAMA Working Papers 2019-54, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
• Christian S. Otchia & Simplice A. Asongu, 2019. "Industrial Growth in Sub-Saharan Africa: Evidence from Machine Learning with Insights from Nightlight Satellite Images," Working Papers 19/046, European Xtramile Centre of African Studies (EXCAS).
• Christian S. Otchia & Simplice A. Asongu, 2019. "Industrial Growth in Sub-Saharan Africa: Evidence from Machine Learning with Insights from Nightlight Satellite Images," Working Papers of the African Governance and Development Institute. 19/046, African Governance and Development Institute..
• Adamantios Ntakaris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators," Papers 1907.09452, arXiv.org.
• Dinesh Reddy Vangumalli & Konstantinos Nikolopoulos & Konstantia Litsiou, 2019. "Clustering, Forecasting and Cluster Forecasting: using k-medoids, k-NNs and random forests for cluster selection," Working Papers 19016, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
• Marco Schreyer & Timur Sattarov & Christian Schulze & Bernd Reimer & Damian Borth, 2019. "Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks," Papers 1908.00734, arXiv.org.
• Xinyi Li & Yinchuan Li & Xiao-Yang Liu & Christina Dan Wang, 2019. "Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction," Papers 1908.01112, arXiv.org.
• Bernard Lapeyre & Jérôme Lelong, 2020. "Neural network regression for Bermudan option pricing," Working Papers hal-02183587, HAL.
• Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2019. "Machine Learning for Forecasting Excess Stock Returns The Five-Year-View," Graz Economics Papers 2019-06, University of Graz, Department of Economics.
• Bracke, Philippe & Datta, Anupam & Jung, Carsten & Sen, Shayak, 2019. "Machine learning explainability in finance: an application to default risk analysis," Bank of England working papers 816, Bank of England.
• Lionel Yelibi & Tim Gebbie, 2019. "Agglomerative Likelihood Clustering," Papers 1908.00951, arXiv.org, revised Oct 2021.
• Terry McKinley, 2019. "Worried about the fourth industrial revolution's impact on jobs? Scale up skills development and training!," One Pager Arabic 425, International Policy Centre for Inclusive Growth.
• Sebastian Frischbier & Mario Paic & Alexander Echler & Christian Roth, 2019. "Managing the Complexity of Processing Financial Data at Scale -- an Experience Report," Papers 1908.03206, arXiv.org.
• Sebastian Becker & Patrick Cheridito & Arnulf Jentzen & Timo Welti, 2019. "Solving high-dimensional optimal stopping problems using deep learning," Papers 1908.01602, arXiv.org, revised Aug 2021.