Liquidity takers behavior representation through a contrastive learning approach

ArXiv ID: 2306.05987 “View on arXiv”

Authors: Unknown

Abstract

Thanks to the access to the labeled orders on the CAC40 data from Euronext, we are able to analyze agents’ behaviors in the market based on their placed orders. In this study, we construct a self-supervised learning model using triplet loss to effectively learn the representation of agent market orders. By acquiring this learned representation, various downstream tasks become feasible. In this work, we utilize the K-means clustering algorithm on the learned representation vectors of agent orders to identify distinct behavior types within each cluster.

Keywords: Self-Supervised Learning, Triplet Loss, Agent Behavior Analysis, Clustering, Market Microstructure, Equities

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts like contrastive learning with triplet loss and vector space embeddings, which indicates high math complexity, while its use of labeled LOB data, specific clustering algorithms (K-means), and detailed data preprocessing suggests solid empirical implementation.

  flowchart TD
    A["Research Goal<br>Represent Liquidity Taker Behavior"] --> B["Data Input<br>Labeled Orders CAC40 Euronext"]
    B --> C["Self-Supervised Learning<br>Triplet Loss Architecture"]
    C --> D["Computation<br>Learned Order Representation Vectors"]
    D --> E["Downstream Task<br>K-means Clustering"]
    E --> F["Outcome<br>Identified Distinct Agent Behavior Types"]
    F --> G["Conclusion<br>Effective Behavior Representation & Analysis"]