Report NEP-FOR-2020-04-20

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.

Subscribe to this report: email, RSS, or Mastodon.

Other reports in NEP-FOR

The following items were announced in this report:

• Michael Pfarrhofer, 2020. "Forecasts with Bayesian vector autoregressions under real time conditions," Papers 2004.04984, arXiv.org.
• Jonghyeon Min, 2020. "Financial Market Trend Forecasting and Performance Analysis Using LSTM," Papers 2004.01502, arXiv.org.
• Zsolt Darvas & Zoltán Schepp, 2020. "Forecasting exchange rates of major currencies with long maturity forward rates," Working Papers 35829, Bruegel.
• Philip Ndikum, 2020. "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers 2004.01504, arXiv.org.
• Daniel Borup & Bent Jesper Christensen & Nicolaj N{o}rgaard Muhlbach & Mikkel Slot Nielsen, 2020. "Targeting predictors in random forest regression," Papers 2004.01411, arXiv.org, revised Nov 2020.
• Ioannis Boukas & Damien Ernst & Thibaut Th'eate & Adrien Bolland & Alexandre Huynen & Martin Buchwald & Christelle Wynants & Bertrand Corn'elusse, 2020. "A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding," Papers 2004.05940, arXiv.org.
• David F. Hendry, 2020. "First in, First out: Econometric Modelling of UK Annual CO_2 Emissions, 1860–2017," Economics Papers 2020-W02, Economics Group, Nuffield College, University of Oxford.