Abstract
In recent years, Chinese futures market meets inefficiency with liquidity boom, providing investors potential arbitrage opportunities. Various studies have applied artificial intelligent methods in stock and futures trading. We propose a new model which combines GARCH and wavelet neural network (GARCH-WNN) to predict the threshold of trading, and compare the model to classical Back propagation Neural Network (BP) and Historical Back testing (HB) models in detail. The back testing results show that GARCH-WNN model provides trading thresholds with higher and more robust return-rates in various typical market conditions, especially when the prices of arbitrage contracts move in opposite directions.
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