Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework

ArXiv ID: 2502.16023 “View on arXiv”

Authors: Unknown

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.

Keywords: Financial Headlines, Natural Language Processing (NLP), Contrastive Learning, Market Prediction, Semantic Embedding

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper employs advanced machine learning concepts like contrastive learning and LLMs, but the mathematical presentation is mostly descriptive and lacks deep derivations or formal proofs. Empirically, it reports specific quantitative results (7% accuracy improvement) and describes a concrete pipeline using WSJ headlines, suggesting a backtest-ready approach.

  flowchart TD
    A["Research Goal<br>Uncover semantic relationships<br>between financial headlines<br>and market movements"] --> B["Input Data<br>WSJ Headlines + Market Data"]
    B --> C["Stage I: Weighted Headline Augmentation<br>Generates augmented headlines<br>with fine-grained similarity scores"]
    C --> D["Stage II: WSSCL<br>Weighted Self-Supervised Contrastive Learning<br>Creates refined embedding space"]
    D --> E["Key Finding 1: 7% Accuracy Boost<br>ContraSim features improve<br>market classification accuracy"]
    D --> F["Key Finding 2: Intrinsic Clustering<br>Similarity spaces cluster days<br>by homogeneous market movements"]
    D --> G["Key Finding 3: Actionable Insights<br>Identifies historical analogs for<br>current headlines to predict trends"]