Predicting the oil prices: Do technical indicators help?
By Libo Yin, Qingyuan Yang. 2016
126 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/3f45e7e600f7c617f1f7b2b2a3bee0c998984060
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Stavros Degiannakis *
- George Filis
- Vipin Arora
- D. Broadstock
- Ioannis Chatziantoniou
- Perry Sadorsky
- Nikolaos Antonakakis
- R. Brooks
In Renewable Energy, Sustainability and the Environment
- Stavros Degiannakis *
- George Filis
- Vipin Arora
- Perry Sadorsky
- Ronald A. Ratti
- Wensheng Kang
- D. Broadstock
- Ioannis Chatziantoniou
Citations in the corpus
- Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence (2018, methodology): “For instance, in the oil price forecasting literature Chen (2014) and Yin and Yang (2016) use predictive regression models 12 , whereas Baumeister et al. (2015) em - ploy a Mixed-Data Sampling (MIDAS) framework, which allows the researcher to combine low and high frequency data in the same model.”
- A Random Forests Approach to Predicting Clean Energy Stock Prices (2021, unknown): “While there are many different technical indicators the ones chosen in this paper are widely used in academics and practice (Yin and Yang 2016; Yin et al. 2017; Neely et al. 2014; Wang et al. 2020; Bustos and Pomares-Quimbaya 2020).”