In order to maximize the profit, investors usually examine diverse investment strategies based on various information when constructing their portfolios. However, they can hardly always construct a profitable portfolio due to the dynamic performance of the strategies and the dynamics of the market state along with the time. To address this challenge, we propose a 2D-attention framework to capture the dynamics of the above two factors in this paper. To capture the dynamic of the first factor, we design a strategy-wise attention model to dynamically combine multiple strategies according to their respective effectiveness. To deal with the second factor, we design a divide-and-conquer framework to learn multiple strategy-wise attention models, which categorizes the whole market periods into a few stable states and jointly learn respective models for each state and then build a state-wise attention model to combine them dynamically for the final task. Extensive experiments on real-world data demonstrate that our 2D-attention framework can significantly outperform several widely-used baselines.<\/p>\n","protected":false},"excerpt":{"rendered":"
In order to maximize the profit, investors usually examine diverse investment strategies based on various information when constructing their portfolios. However, they can hardly always construct a profitable portfolio due to the dynamic performance of the strategies and the dynamics of the market state along with the time. 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