2021, 51(3): 246-258.
doi: 10.52396/JUST-2021-0052
Abstract:
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.
