Enhancing Stock Movement Prediction with Adversarial Training

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.Comment: IJCAI 201

An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods

A multi-channel cross-residual deep learning framework for news-oriented stock movement prediction

Stock market movement prediction remains challenging due to random walk characteristics. Yet through a potent blend of input parameters, a prediction model can learn sequential features more intelligently. In this paper, a multi-channel news-oriented prediction system is developed to capture intricate moving patterns of the stock market index. Specifically, the system adopts the temporal causal convolution to process historical index values due to its capability in learning long-term dependencies. Concurrently, it employs the Transformer Encoder for qualitative information extraction from financial news headlines and corresponding preview texts. A notable configuration to our multi-channel system is an integration of cross-residual learning between different channels, thereby allowing an earlier and closer information fusion. The proposed architecture is validated to be more efficient in trend forecasting compared to independent learning, by which channels are trained separately. Furthermore, we also demonstrate the effectiveness of involving news content previews, improving the prediction accuracy by as much as 3.39%