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Stock movement prediction is a challenging problem: the market is highly stochastic, and we make temporally-dependent predictions from chaotic data.
Stock trend prediction plays a critical role in seeking maximized profit from stock investment.
Stock market prediction is one of the most attractive research topic since the successful prediction on the market's future movement leads to significant profit.
In this paper, we address these issues by proposing a novel RNN architecture based on RHN, namely the Recurrent Highway Network with Grouped Auxiliary Memory (GAM-RHN).
Ranked #1 on Stock Trend Prediction on FI-2010