Explainable Stock Price Movement Prediction using Contrastive Learning

Predicting stock price movements is a high-stakes task that demands explainability for human decision-makers. A key shortcoming in current methods is treating sub-predictions independently, without learning from accumulated experiences. We propose a novel triplet network for contrastive learning to enhance the explainability of stock movement prediction by considering instances of "integrated textual information and quantitative indicators". We refer to the target past-l-day tweet-price time series as the "anchor instance". Each anchor instance is paired with a "positive instance" characterized by highly correlated return trends yet significant differences across the entire feature space, and a "negative instance" that exhibits similar return trends along with high proximity in the feature space. The model is designed with the objective of (1) minimizing the cross entropy loss between input logits and target, (2) minimizing the distance between the anchor instances and positive instances, and (3) maximizing the distance between the anchor instances and negative instances. Our framework's effectiveness is demonstrated through extensive testing, showing superior performance on stock prediction benchmarks.