Abstract
This paper contributes a new machine learning solution for stock movement prediction, which aims
to predict whether the price of a stock will be up or
down in the near future. The key novelty is that we
propose to employ adversarial training to improve
the generalization of a neural network prediction
model. The rationality of adversarial training here
is that the input features to stock prediction are typically based on stock price, which is essentially a
stochastic variable and continuously changed with
time by nature. As such, normal training with static
price-based features (e.g., the close price) can easily overfit the data, being insufficient to obtain reliable models. To address this problem, we propose
to add perturbations to simulate the stochasticity of
price variable, and train the model to work well under small yet intentional perturbations. Extensive
experiments on two real-world stock data show that
our method outperforms the state-of-the-art solution [Xu and Cohen, 2018] with 3.11% relative improvements on average w.r.t. accuracy, validating
the usefulness of adversarial training for stock prediction task