CISTF: A Causal Inference Method for Stock Trend Forecasting

Stock trend forecasting, which aims to predict future fluctuations of stock price, has garnered significant attention in recent years. However, it is quite hard to train a model that can consistently and accurately forecasts the future movements of stocks. Since stock data is influenced by various unobservable factors, such as market sentiment and industry conditions, its distribution is unstable and can change over time. As a result, models trained on stock data often exhibit poor performance in prediction. Causal inference is an effective method that has been widely used to address distribution shift. However, how to conduct causal inference for stock trend forecasting still remains under-explored. To this end, we propose CISTF, a method based on causal inference to explore the causal dependencies between the input historical data and the future trend of stocks. We first construct a causal graph for the stock trend forecasting task, in which we use confounders to represent unobservable factors that affect both the input data and the future trend of stocks. Such time-varying confounders contribute to the distribution drift in the stock data. Then we use front-door adjustment, an intervention strategie that allow we to intervene the input data, to eliminate the spurious correlations brought by the unobserved confounders. Additionally, we develop a deep architecture to implement front-door adjustment, which consists of a feature extractor, a mediator estimation module and a conditional probability estimation module. We evaluate the effectiveness of CISTF on real-world stock data, and experiments on three public stock datasets demonstrate that our model achieves state-of-the-art performance.