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...

Using the minute-frequency data of the top 30 coins listed on Binance, which represent 86% of the total dollar trading volume of the cryptocurrency market, we document strong evidence of cross-cryptocurrency return predictability. The lagged returns of other cryptocurrencies serve as significant...

Using the minute-frequency data on Binance, we find strong evidence of cross-cryptocurrency return predictability. The lagged returns of other cryptocurrencies serve as significant predictors of focal cryptocurrencies up to ten minutes, in line with slow information diffusion. The results are...