Forecasting rare earth stock prices with machine learning
I Henriques, P Sadorsky - Resources Policy, 2023 - Elsevier
Rare earth elements (REEs) are indispensable for producing green technologies and
electronics. Demand for REEs in clean energy technologies in 2040 are projected to be …
electronics. Demand for REEs in clean energy technologies in 2040 are projected to be …
A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction
Y Lin, Q Liao, Z Lin, B Tan, Y Yu - Resources Policy, 2022 - Elsevier
Accurately predicting precious metal prices is of extreme significance as they possess an
essential position in both financial and industrial fields. To achieve higher prediction …
essential position in both financial and industrial fields. To achieve higher prediction …
Forecasting copper price by application of robust artificial intelligence techniques
Metal price is one of the most important and effective parameters in assessing different
projects such as industry and mining. In this regard, price variations can play a vital role in …
projects such as industry and mining. In this regard, price variations can play a vital role in …
Forecasting monthly copper price: A comparative study of various machine learning-based methods
Copper is one of the valuable natural resources, and it was widely used in many different
industries. The complicated fluctuations of copper prices can significantly affect other …
industries. The complicated fluctuations of copper prices can significantly affect other …
Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms
The focus of this study aims at developing two novel hybrid intelligence models for
forecasting copper prices in the future with high accuracy based on the extreme learning …
forecasting copper prices in the future with high accuracy based on the extreme learning …
Forecasting on metal resource spot settlement price: New evidence from the machine learning model
T Shi, C Li, W Zhang, Y Zhang - Resources Policy, 2023 - Elsevier
Accurate prediction of the price of metal mineral resources is of great practical significance
for guiding the production of non-renewable resource enterprises and maintaining the …
for guiding the production of non-renewable resource enterprises and maintaining the …
Gold and tail risks
In this study, we consider as a predictor of gold return predictability, an alternative measure
of systematic risk using the tail risk obtained from the four variants (Adaptive, Symmetric …
of systematic risk using the tail risk obtained from the four variants (Adaptive, Symmetric …
Prediction of white spot disease susceptibility in shrimps using decision trees based machine learning models
Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture
activity worldwide affecting the economy of the countries, especially South-East Asian …
activity worldwide affecting the economy of the countries, especially South-East Asian …
A medium to long-term multi-influencing factor copper price prediction method based on CNN-LSTM
F Li, H Zhou, M Liu, L Ding - IEEE Access, 2023 - ieeexplore.ieee.org
Non-ferrous copper prices exhibit high noise, non-smoothness, and non-linearity, which
pose significant challenges to accurate price prediction. One of the current methods for …
pose significant challenges to accurate price prediction. One of the current methods for …
“Watch your tone!”: Forecasting mining industry commodity prices with financial report tone
This paper presents robust evidence indicating that the tone of financial reports from the US
mining industry firms can predict certain mining commodity returns. We assess this …
mining industry firms can predict certain mining commodity returns. We assess this …