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Report NEP-CMP-2019-08-19
This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stanley Miles issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-CMP
The following items were announced in this report:
- Item repec:hal:wpaper:hal-02183587 is not listed on IDEAS anymore
- 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.
- 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.
- 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.
- 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.
- 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.
- Martin Magris, 2019, "On the simulation of the Hawkes process via Lambert-W functions," Papers, arXiv.org, number 1907.09162, Jul.
- Kaushal , Kevin R. & Rosendahl, Knut Einar, 2019, "Optimal REDD+ in the carbon market," Working Paper Series, Norwegian University of Life Sciences, School of Economics and Business, number 3-2019, 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.
- Ge, Houtian & Canning, Patrick N. & Li, Jie, , "Hub Location in the U.S. Fresh Produce Supply Chain - A Computational Optimization Model," 2019 Annual Meeting, July 21-23, Atlanta, Georgia, Agricultural and Applied Economics Association, number 290998, DOI: 10.22004/ag.econ.290998.
- 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.