An end-to-end multitask method with two targets for high-frequency price movement prediction

: High-frequency price movement prediction is to predict the direction ( e. g. up , unchanged or down ) of the price change in short time ( e. g. one minute ) . It is challenging to use historical high-frequency transaction data to predict price movement because their relation is noisy , nonlinear and complex. We propose an end-to-end multitask method with two targets to improve high-frequency price movement prediction. Specifically , the proposed method introduces an auxiliary target ( high-frequency rate of price change ), which is highly related with the main target ( high-frequency price movement ) and is useful to improve the high-frequency price movement prediction. Moreover , each task has a feature extractor based on recurrent neural network and convolutional neural network to learn the noisy , nonlinear and complex temporal-spatial relation between the historical transaction data and the two targets. Besides , the shared parts and task-specific parts of each task are separated explicitly to alleviate the potential negative transfer caused by the multitask method. Moreover , a gradient balancing approach is adopted to use the close relation between two targets to filter the temporal-spatial dependency learned from the inconsistent noise and retain the dependency learned from the consistent true information to improve the high-frequency price movement prediction. The experimental results on real-world datasets show that the proposed method manages to utilize the highly related auxiliary target to help the feature extractor of the main task to learn the temporal-spatial dependency with more generalization to improve high-frequency price movement prediction. Moreover , the auxiliary target ( high-frequency rate of the price change ) not only improves the generalization of overall temporal-spatial dependency learned by the whole feature extractor but also improve temporal-spatial dependency learned by the different parts of the feature extractor.