Arbitrage strategies of sugar futures based on CEEMD-LSTM-Adaboost model
The 5-minute high-frequency data of white sugar futures contracts SR2201 and SR2109 are taken as the research object.Under the condition that there is a long-term equilibrium relationship between them,GARCH model is constructed to describe the ARCH effect of the residual.When the complementary set empiri-cal mode decomposition(CEEMD)method is combined with the long-term and short-term memory network(LSTM)and adaptive lifting algorithm(Adaboost),arbitrage operation is carried out by predicting the rise and fall of price difference,setting different opening and closing thresholds,and making a comparative study of four neural network arbitrage strategies in the sample range.Results show that the neural network arbitrage strategy based on CEEMD-LSTM-Adaboost model is feasible and effective in white sugar futures market,and it has more advantages than BP,LSTM and LSTM-Adaboost neural networks in terms of prediction accuracy and arbitrage effect.
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