A DivideandConquer Framework for Attentionbased Combination of Multiple Investment Strategies 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 2Dattention framework to capture the dynamics of the above two factors in this paper. To capture the dynamic of the first factor, we design a strategywise attention model to dynamically combine multiple strategies according to their respective effectiveness. To deal with the second factor, we design a divideandconquer framework to learn multiple strategywise 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 statewise attention model to combine them dynamically for the final task. Extensive experiments on realworld data demonstrate that our 2Dattention framework can significantly outperform several widelyused baselines.

Conservative or Aggressive? ConfidenceAware Dynamic Portfolio Construction Indicatorbased investing is a popular investment strategy driven by technical analysis for the stock market, the key issue of which is to construct portfolios from technical indicators. Due to the high volatility and nonstationary of the stock market, the effectiveness of an indicator, however, varies largely across different periods, which has made it necessary to dynamically adjust indicatorbased investing. In this paper, we propose a confidencebased calibration approach for dynamic portfolio construction. The major intuition behind is to tune a more concentrated portfolio when the indicator yields higher confidence otherwise a relatively equalweighted one. To seek a maximized longterm profit, we further propose to integrate learning the confidence (i.e., future effectiveness) of an indicator into a unified portfolio construction approach powered by a recurrent reinforcement learning framework. Compared with the traditional indicator investing strategies, our confidencebased calibrated indicator of investing can obtain significantly higher returns with lower risks.