Abstract
The fluctuation of financial assets is non-stationary and nonlinear, so the stock trend prediction is a hard task. Limit order book (LOB) takes an important role in the order-driven market. Investors can make decisions referring to LOBs, which affects the movement of stock prices. Existing networks with a recurrent structure cannot learn temporal features well for the analysis of LOBs. To remedy it, this paper proposes a stacked residual gated recurrent unit (SRGRU) network to forecast the stock trend by utilizing high-frequency LOBs. SRGRU contains multiple residual gated recurrent unit (RGRU) blocks that are stacked to increase the depth of the network and improve the generalization ability. RGRU, which is designed based on gated recurrent unit (GRU), can learn temporal features and prevent the degradation of a network caused by deepening it. Experiments are conducted on FI-2010. The results show that SRGRU goes beyond the state-of-the-art models.
This work was supported in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 19KJA550002, by the Six Talent Peak Project of Jiangsu Province of China under Grant No. XYDXX-054, by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and by the Collaborative Innovation Center of Novel Software Technology and Industrialization.
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Lv, X., Zhang, L. (2021). Residual Gated Recurrent Unit-Based Stacked Network for Stock Trend Prediction from Limit Order Book. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management . KSEM 2021. Lecture Notes in Computer Science(), vol 12816. Springer, Cham. https://doi.org/10.1007/978-3-030-82147-0_29
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