Report NEP-FOR-2019-09-09

This is the archive for NEP-FOR, a report on new working papers in the area of Forecasting. Rob J Hyndman issued this report. It is usually issued weekly.

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Other reports in NEP-FOR

The following items were announced in this report:

• Morita, Hiroshi & 森田, 裕史, 2019. "Forecasting Public Investment Using Daily Stock Returns," Discussion paper series HIAS-E-88, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
• G'abor Petneh'azi, 2019. "Quantile Convolutional Neural Networks for Value at Risk Forecasting," Papers 1908.07978, arXiv.org, revised Sep 2020.
• Denis Shibitov & Mariam Mamedli, 2019. "The finer points of model comparison in machine learning: forecasting based on russian banks’ data," Bank of Russia Working Paper Series wps43, Bank of Russia.
• Samuel Asante Gyamerah, 2019. "Are Bitcoins price predictable? Evidence from machine learning techniques using technical indicators," Papers 1909.01268, arXiv.org.
• Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Research Discussion Papers 14/2019, Bank of Finland.
• Michał Rubaszek, 2019. "Forecasting crude oil prices with DSGE models," GRU Working Paper Series GRU_2019_024, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
• Raehyun Kim & Chan Ho So & Minbyul Jeong & Sanghoon Lee & Jinkyu Kim & Jaewoo Kang, 2019. "HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction," Papers 1908.07999, arXiv.org, revised Nov 2019.
• Zheng Tracy Ke & Bryan T. Kelly & Dacheng Xiu, 2019. "Predicting Returns With Text Data," NBER Working Papers 26186, National Bureau of Economic Research, Inc.
• Mat'uv{s} Maciak & Ostap Okhrin & Michal Pev{s}ta, 2019. "Infinitely Stochastic Micro Forecasting," Papers 1908.10636, arXiv.org, revised Sep 2019.
• Xiong, Tao, 2019. "Forecasting CBOT Corn Futures Price with Dynamic Model Averaging: The Roles of Fundamentals, Financial Markets, and Economics Environment," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 290773, Agricultural and Applied Economics Association.
• Chen, Jian & Katchova, Ani, 2019. "Agricultural Loan Delinquency Prediction Using Machine Learning Methods," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 290745, Agricultural and Applied Economics Association.
• Zhou, Yujun & Baylis, Kathy, 2019. "Predict Food Security with Machine Learning: Application in Eastern Africa," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 291056, Agricultural and Applied Economics Association.
• Schünemann, Johannes & Strulik, Holger & Trimborn, Timo, 2019. "Medical and long-term care with endogenous health and longevity," FSES Working Papers 505, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland, revised 20 Jan 2020.
• Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Papers 1908.11498, arXiv.org, revised Oct 2019.
• Fayçal Mraihi & Inane Kanzari, 2019. "Predicting financial distress of companies: Comparison between multivariate discriminant analysis and multilayer perceptron for Tunisian case," Working Papers 1328, Economic Research Forum, revised 21 Aug 2019.