Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework

TL;DR

ContraSim is a novel framework that uses contrastive learning to embed financial headlines into a semantic space, improving market forecasting accuracy and providing insights into market dynamics and historical similarities.

Contribution

The paper introduces ContraSim, a new contrastive learning framework that captures semantic relationships in financial headlines and enhances market prediction models.

Findings

Improves classification accuracy by 7% when integrating ContraSim features.

Constructs semantic spaces that cluster days with similar market movements.

Identifies historical news days similar to current headlines for actionable insights.

Abstract

We introduce the Contrastive Similarity Space Embedding Algorithm (ContraSim), a novel framework for uncovering the global semantic relationships between daily financial headlines and market movements. ContraSim operates in two key stages: (I) Weighted Headline Augmentation, which generates augmented financial headlines along with a semantic fine-grained similarity score, and (II) Weighted Self-Supervised Contrastive Learning (WSSCL), an extended version of classical self-supervised contrastive learning that uses the similarity metric to create a refined weighted embedding space. This embedding space clusters semantically similar headlines together, facilitating deeper market insights. Empirical results demonstrate that integrating ContraSim features into financial forecasting tasks improves classification accuracy from WSJ headlines by 7%. Moreover, leveraging an information density analysis, we find that the similarity spaces constructed by ContraSim intrinsically cluster days with homogeneous market movement directions, indicating that ContraSim captures market dynamics independent of ground truth labels. Additionally, ContraSim enables the identification of historical news days that closely resemble the headlines of the current day, providing analysts with actionable insights to predict market trends by referencing analogous past events.