Enhancing 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 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.

Hierarchical Multi-Scale Gaussian Transformer for Stock Movement Prediction

“…proposes a ConvLSTM-based Seq2Seq framework for stock movement prediction. [Qin et al, 2017] proposes an Attentive-LSTM model with an attention mechanism to predict stock price movement and [Feng et al, 2019] further introduces an data augmentation approach with the idea of adversarial training. However, points out that LSTM can only distinguish 50 positions nearby with an effective context size of about 200.…”

Complex Sequential Data Analysis: A Systematic Literature Review of Existing Algorithms

“…• Model reliability and generalisation, is a problem when predictive neural frameworks are used. These associated problems are caused by the stochasticity of stock features in financial stock price datasets [10]. • Precision challenges when forecasting within financial environments.…”

The Use of LSTM Neural Networks to Implement the NARX Model. A Case Study of EUR-USD Exchange Rates

“…At each time-step, the LSTM learns the hidden representation by jointly considering the associate input and the previous hidden representation to capture the sequential dependency. [19] The learning process is accomplished using specific memory blocks located in the recurrent hidden layer. The memory blocks are created from auto-connected cells in which neural network temporal states are saved.…”

“…Several data pre-processing techniques, as for instance feature extraction and feature selection, could decrease the redundancy and noise in time series data and consequently increase the prediction models performances ( [8], [9], [10], [11]). On the other hand, since the exact price of stocks is unpredictable, a series of research works are focused on predicting the price movements, therefore the problem of data forecasting is reduced to a classification problem [12].…”