This paper uses traditional machine learning methods and deep neural networks based on both firm-specific characteristics and macroeconomic variables to price China’s A-share stock market. The main contributions are as follows: We give the stochastic discount factor a flexible form and compare different models’ performances. Since the Chinese government adopts various policies to maintain financial stability, we borrow the idea from generative adversarial network to find the true SDF by selecting moment condition that minimizes return volatility. Additionally, we compare this model’s performance with Chen’s work and find that this model can obtain higher Sharpe ratio and R2.