Paper: arXiv 2306.05987

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.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]