  With the improvement of computer performance and the development of
GPU-accelerated technology, trading with machine learning algorithms has
attracted the attention of many researchers and practitioners. In this
research, we propose a novel portfolio management strategy based on the
framework of Deep Deterministic Policy Gradient, a policy-based reinforcement
learning framework, and compare its performance to that of other trading
strategies. In our framework, two Long Short-Term Memory neural networks and
two fully connected neural networks are constructed. We also investigate the
performance of our strategy with and without transaction costs. Experimentally,
we choose eight US stocks consisting of four low-volatility stocks and four
high-volatility stocks. We compare the compound annual return rate of our
strategy against seven other strategies, e.g., Uniform Buy and Hold,
Exponential Gradient and Universal Portfolios. In our case, the compound annual
return rate is 14.12%, outperforming all other strategies. Furthermore, in
terms of Sharpe Ratio (0.5988), our strategy is nearly 33% higher than that of
the second-best performing strategy.
