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Portfolio allocation is crucial for investment companies. However, gettingthe best strategy in a complex and dynamic stock market is challenging. In thispaper, we propose a novel Adaptive Deep Deterministic Reinforcement Learningscheme (Adaptive DDPG) for the portfolio allocation task, which incorporatesoptimistic or pessimistic deep reinforcement learning that is reflected in theinfluence from prediction errors. Dow Jones 30 component stocks are selected asour trading stocks and their daily prices are used as the training and testingdata. We train the Adaptive DDPG agent and obtain a trading strategy. TheAdaptive DDPG's performance is compared with the vanilla DDPG, Dow JonesIndustrial Average index and the traditional min-variance and mean-varianceportfolio allocation strategies. Adaptive DDPG outperforms the baselines interms of the investment return and the Sharpe ratio.

Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation