Report NEP-CMP-2019-05-27

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:

• Daniel Ladley, 2019. "The Design and Regulation of High Frequency Traders," Discussion Papers in Economics 19/02, Division of Economics, School of Business, University of Leicester.
• Chunding Li & Jing Wang & John Whalley, 2019. "Trade Protectionism and US Manufacturing Employment," NBER Working Papers 25860, National Bureau of Economic Research, Inc.
• Gawlitza, Joshua & Sturm, Timo & Spohrer, Kai & Henzler, Thomas & Akin, Ibrahim & Schönberg, Stefan & Borggrefe, Martin & Haubenreisser, Holger & Trinkmann, Frederik, 2019. "Predicting Pulmonary Function Testing from Quantified Computed Tomography Using Machine Learning Algorithms in Patients with COPD," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 113226, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Kim, T.Y., 2018. "Improving warehouse responsiveness by job priority management," Econometric Institute Research Papers EI2018-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• Christopher Kath & Florian Ziel, 2019. "Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets," Papers 1905.07886, arXiv.org, revised Sep 2020.
• Reaz Chowdhury & M. Arifur Rahman & M. Sohel Rahman & M. R. C. Mahdy, 2019. "Predicting and Forecasting the Price of Constituents and Index of Cryptocurrency Using Machine Learning," Papers 1905.08444, arXiv.org.
• de Kok, Ties, 2019. "Essays on reporting and information processing," Other publications TiSEM 468fd12b-19c0-4c7b-a33a-6, Tilburg University, School of Economics and Management.
• Wu, Guoyuan & Ye, Fei & Hao, Peng & Esaid, Danial & Boriboonsomsin, Kanok & Barth, Matthew J., 2019. "Deep Learning–based Eco-driving System for Battery Electric Vehicles," Institute of Transportation Studies, Working Paper Series qt9fz140zt, Institute of Transportation Studies, UC Davis.
• Sudiksha Joshi, 2019. "Time Series Analysis and Forecasting of the US Housing Starts using Econometric and Machine Learning Model," Papers 1905.07848, arXiv.org.
• Patel, Abhishek & Anand, Rajesh, 2019. "Fast Security Constraint Unit Commitment by Utilizing Chaotic Crow Search Algorithm," MPRA Paper 93971, University Library of Munich, Germany.
• Samuel Asante Gyamerah & Philip Ngare & Dennis Ikpe, 2019. "Hedging crop yields against weather uncertainties -- a weather derivative perspective," Papers 1905.07546, arXiv.org, revised Aug 2019.
• Krupitzer, Christian & Drechsel, Guido & Mateja, Deborah & Pollklasener, Alina & Schrage, Florian & Sturm, Timo & Tomasovic, Aleksandar & Becker, Christian, 2018. "Using Spreadsheet-defined Rules for Reasoning in Self-Adaptive Systems," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 113225, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Arthur Turrell & Bradley J. Speigner & Jyldyz Djumalieva & David Copple & James Thurgood, 2019. "Transforming Naturally Occurring Text Data Into Economic Statistics: The Case of Online Job Vacancy Postings," NBER Working Papers 25837, National Bureau of Economic Research, Inc.
• Seth G. Benzell & Laurence J. Kotlikoff & Guillermo Lagarda & Yifan Ye, 2018. "Simulating U.S. Business Cash Flow Taxation in a 17-Region Global Model," Boston University - Department of Economics - The Institute for Economic Development Working Papers Series dp-312, Boston University - Department of Economics.
• Ludovic Gouden`ege & Andrea Molent & Antonino Zanette, 2019. "Machine Learning for Pricing American Options in High-Dimensional Markovian and non-Markovian models," Papers 1905.09474, arXiv.org, revised Jun 2019.
• Shangeth Rajaa & Jajati Keshari Sahoo, 2019. "Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction," Papers 1905.07581, arXiv.org.
• Lisa R. Goldberg & Saad Mouti, 2019. "Sustainable Investing and the Cross-Section of Returns and Maximum Drawdown," Papers 1905.05237, arXiv.org, revised Dec 2023.
• Heinisch, Dominik & König, Johannes & Otto, Anne, 2019. "The IAB-INCHER project of earned doctorates (IIPED): A supervised machine learning approach to identify doctorate recipients in the German integrated employment biography data," IAB-Discussion Paper 201913, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
• Gogas, Periklis & Papadimitriou, Theophilos & Plakandaras, Vasilios & Gupta, Rangan, 2019. "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," DUTH Research Papers in Economics 3-2016, Democritus University of Thrace, Department of Economics.
• Berardi, Michele, 2019. "A probabilistic interpretation of the constant gain algorithm," MPRA Paper 94023, University Library of Munich, Germany.