Title: Improving Stock Movement Prediction with Adversarial Training,
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 recurrent neural
network 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 stationary price-based features (e.g. the closing
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 continuous 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 with 3.11% relative improvements on average w.r.t. accuracy, verifying
the usefulness of adversarial training for stock prediction task. Codes will be
made available upon acceptance.