Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/166766
Title: Enhancing Stock Movement Prediction with Adversarial Training
Authors: FENG FULI 
Huimin Chen
Xiangnan He 
Ji Ding
Maosong Sun 
CHUA TAT SENG 
Keywords: Stock price
prediction
Adversarial Attentive LSTM
Issue Date: 10-Aug-2019
Publisher: IJCAI 2019
Citation: FENG FULI, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, CHUA TAT SENG (2019-08-10). Enhancing Stock Movement Prediction with Adversarial Training. International Joint Conference on Artificial Intelligence. ScholarBank@NUS Repository.
Rights: Attribution-ShareAlike 4.0 International
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.
Source Title: International Joint Conference on Artificial Intelligence
URI: https://scholarbank.nus.edu.sg/handle/10635/166766
ISSN: 10450823
Rights: Attribution-ShareAlike 4.0 International
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