This paper proposes an EEMD-Hurst-LSTM prediction method based on the ensemble learning framework, which is applied to the prediction of typical commodities in China's commodity futures market. This method performs ensemble empirical mode decomposition (EEMD) on commodity futures prices, and...

This paper aims to study the correlation structure, key commodity and cluster characteristics of China's commodity markets during the period of the 2020-2021 global commodity price boom via applying Empirical Mode Decomposition (EMD) of commodity futures price indices to obtain high-frequency...

In statistical arbitrage, paired trading, as a market-neutral strategy, is widely used because of its simple method and easy implementation. This paper constructs a machine learning framework for commodity futures matching trading from four aspects: systematic feature extraction, unsupervised...

The primary use of futures is hedging risk. Traders in the spot market can hedge certain risks through the futures market. With the development of the futures market, the arbitrage transactions around futures have attracted increasingly attention. The aim of this paper is to establish an...