Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework

2025-02-22

AI Summary

This paper addresses the challenges of modeling semantic associations between financial news headlines and market movements, compounded by scarce labeled data. We propose a two-stage unsupervised learning framework: first, weighted news augmentation generates fine-grained semantic similarity scores; second, weighted self-supervised contrastive learning (WSSCL) constructs a clusterable semantic embedding space. To our knowledge, this is the first unsupervised similarity-space learning paradigm specifically designed for financial headlines—requiring no human annotations yet enabling discovery of market dynamic patterns and retrieval of historically similar trading days. On the Wall Street Journal (WSJ) news-based market movement prediction task, our method improves classification accuracy by 7%. Crucially, the learned embeddings naturally cluster by market direction, supporting interpretable trend reasoning and analogy-based analysis.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Data Mining & Knowledge Management: Semantic Web

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

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.

Problem

• Uncover semantic relationships between headlines and markets
• Improve financial forecasting classification accuracy
• Identify historical news resembling current headlines for insights

Innovation

• Contrastive Similarity Learning
• Weighted Headline Augmentation
• Weighted Self-Supervised Learning