Report NEP-CMP-2020-10-19

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:

• Lorenc Kapllani & Long Teng, 2020. "Deep learning algorithms for solving high dimensional nonlinear backward stochastic differential equations," Papers 2010.01319, arXiv.org, revised Jun 2022.
• Chuheng Zhang & Yuanqi Li & Xi Chen & Yifei Jin & Pingzhong Tang & Jian Li, 2020. "DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis," Papers 2010.01265, arXiv.org, revised Jan 2021.
• Tullio Mancini & Hector Calvo-Pardo & Jose Olmo, 2020. "Prediction intervals for Deep Neural Networks," Papers 2010.04044, arXiv.org, revised May 2021.
• Janusz Gajda & Rafał Walasek, 2020. "Fractional differentiation and its use in machine learning," Working Papers 2020-32, Faculty of Economic Sciences, University of Warsaw.
• Mosavi, Amir & Faghan, Yaser & Ghamisi, Pedram & Duan, Puhong & Ardabili, Sina Faizollahzadeh & Hassan, Salwana & Band, Shahab S., 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," OSF Preprints jrc58, Center for Open Science.
• Abramov, Dimitri Marques, 2020. "A Complex System Needs Homeostasis: Market Self-Organization Through Negative Feedback Using A Floating Taxation Policy," SocArXiv xj2gb, Center for Open Science.
• Nicola Curci & Giuseppe Grasso & Pasquale Recchia & Marco Savegnago, 2020. "Anti-poverty measures in Italy: a microsimulation analysis," Temi di discussione (Economic working papers) 1298, Bank of Italy, Economic Research and International Relations Area.
• Battula, Swathi & Tesfatsion, Leigh & McDermott, Thomas E., 2019. "A Test System for ERCOT Market Design Studies: Development and Application," ISU General Staff Papers 201912230800001078, Iowa State University, Department of Economics.
• Nadja Klein & Michael Stanley Smith & David J. Nott, 2020. "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers 2010.01844, arXiv.org, revised May 2021.
• Manson, Steven & An, Li & Clarke, Keith C. & Heppenstall, Alison & Koch, Jennifer & Krzyzanowski, Brittany & Morgan, Fraser & O'Sullivan, David & Runck, Bryan C. & Shook, Eric & Tesfatsion, Leigh, 2020. "Methodological Issues of Spatial Agent-Based Models," ISU General Staff Papers 202001010800001690, Iowa State University, Department of Economics.
• Michael Bucker & Gero Szepannek & Alicja Gosiewska & Przemyslaw Biecek, 2020. "Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring," Papers 2009.13384, arXiv.org.
• Ferdinands, Gerbrich & Schram, Raoul & de Bruin, Jonathan & Bagheri, Ayoub & Oberski, Daniel Leonard & Tummers, Lars & van de Schoot, Rens, 2020. "Active learning for screening prioritization in systematic reviews - A simulation study," OSF Preprints w6qbg, Center for Open Science.
• Hinterlang, Natascha & Hollmayr, Josef, 2020. "Classification of monetary and fiscal dominance regimes using machine learning techniques," Discussion Papers 51/2020, Deutsche Bundesbank.
• Tarun Bhatia, 2020. "Predicting Non Farm Employment," Papers 2009.14282, arXiv.org.
• J-C Gerlach & Jerome L Kreuser & Didier Sornette, 2020. "Crash-sensitive Kelly Strategy built on a modified Kreuser-Sornette bubble model tested over three decades of twenty equity indices," Swiss Finance Institute Research Paper Series 20-85, Swiss Finance Institute.
• Raquel Almeida Ramos & Federico Bassi & Dany Lang, 2020. "Bet against the trend and cash in profits," DISCE - Working Papers del Dipartimento di Economia e Finanza def090, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
• Rakshit Jha & Mattijs De Paepe & Samuel Holt & James West & Shaun Ng, 2020. "Deep Learning for Digital Asset Limit Order Books," Papers 2010.01241, arXiv.org.
• Lining Yu & Wolfgang Karl Hardle & Lukas Borke & Thijs Benschop, 2020. "An AI approach to measuring financial risk," Papers 2009.13222, arXiv.org.
• Marco Avellaneda & Juan Andr'es Serur, 2020. "Hierarchical PCA and Modeling Asset Correlations," Papers 2010.04140, arXiv.org.
• Foltas, Alexander & Pierdzioch, Christian, 2020. "On the efficiency of German growth forecasts: An empirical analysis using quantile random forests," Working Papers 21, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
• Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay, 2020. "Time your hedge with Deep Reinforcement Learning," Papers 2009.14136, arXiv.org, revised Nov 2020.
• Masahiro Kato & Shota Yasui, 2020. "Learning Classifiers under Delayed Feedback with a Time Window Assumption," Papers 2009.13092, arXiv.org, revised Jun 2022.