Enhancing Stock Movement Prediction with Adversarial Training

TL;DR

This paper introduces an adversarial training approach to enhance the robustness and accuracy of neural network models for stock movement prediction by simulating price stochasticity.

Contribution

It proposes a novel adversarial training method that adds perturbations to input features to improve model generalization in stock prediction.

Findings

Outperforms state-of-the-art methods by 3.11% in accuracy

Demonstrates robustness of models under price stochasticity

Validates effectiveness of adversarial training for financial data

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 with 3.11% relative improvements on average w.r.t. accuracy, validating the usefulness of adversarial training for stock prediction task.