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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 (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:
- Yingying Lu & Yixiao Zhou, 2019, "A Short Review on the Economics of Artificial Intelligence," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2019-54, Aug.
- 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, European Xtramile Centre of African Studies (EXCAS), number 19/046, Aug.
- 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., African Governance and Development Institute., number 19/046, Aug.
- Adamantios Ntakaris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019, "Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators," Papers, arXiv.org, number 1907.09452, Jul.
- 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, Bangor Business School, Prifysgol Bangor University (Cymru / Wales), number 19016, Aug.
- 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, arXiv.org, number 1908.00734, Aug.
- Xinyi Li & Yinchuan Li & Xiao-Yang Liu & Christina Dan Wang, 2019, "Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction," Papers, arXiv.org, number 1908.01112, Aug.
- Item repec:hal:wpaper:hal-02183587 is not listed on IDEAS anymore
- Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2019, "Machine Learning for Forecasting Excess Stock Returns The Five-Year-View," Graz Economics Papers, University of Graz, Department of Economics, number 2019-06, Aug.
- Philippe Bracke & Anupam Datta & Carsten Jung & Shayak Sen, 2019, "Machine learning explainability in finance: an application to default risk analysis," Bank of England working papers, Bank of England, number 816, Aug.
- Lionel Yelibi & Tim Gebbie, 2019, "Agglomerative Likelihood Clustering," Papers, arXiv.org, number 1908.00951, Aug, 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, International Policy Centre, number 425, Jul.
- Sebastian Frischbier & Mario Paic & Alexander Echler & Christian Roth, 2019, "Managing the Complexity of Processing Financial Data at Scale -- an Experience Report," Papers, arXiv.org, number 1908.03206, Aug.
- Sebastian Becker & Patrick Cheridito & Arnulf Jentzen & Timo Welti, 2019, "Solving high-dimensional optimal stopping problems using deep learning," Papers, arXiv.org, number 1908.01602, Aug, revised Aug 2021.