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. Stan Miles issued this report. It is usually issued weekly.

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Other reports in NEP-CMP

The following items were announced in this report:

• Bernard Lapeyre & Jérôme Lelong, 2020. "Neural network regression for Bermudan option pricing," Working Papers hal-02183587, HAL.
• 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.
• 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).
• 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.
• 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.
• 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.
• Martin Magris, 2019. "On the simulation of the Hawkes process via Lambert-W functions," Papers 1907.09162, arXiv.org.
• Kaushal , Kevin R. & Rosendahl, Knut Einar, 2019. "Optimal REDD+ in the carbon market," Working Paper Series 3-2019, Norwegian University of Life Sciences, School of Economics and Business.
• 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.
• Ge, Houtian & Canning, Patrick N. & Li, Jie, 2019. "Hub Location in the U.S. Fresh Produce Supply Chain - A Computational Optimization Model," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 290998, Agricultural and Applied Economics Association.
• 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.