Explainable AI in Request-for-Quote

ArXiv ID: 2407.15038 “View on arXiv”

Authors: Unknown

Abstract

In the contemporary financial landscape, accurately predicting the probability of filling a Request-For-Quote (RFQ) is crucial for improving market efficiency for less liquid asset classes. This paper explores the application of explainable AI (XAI) models to forecast the likelihood of RFQ fulfillment. By leveraging advanced algorithms including Logistic Regression, Random Forest, XGBoost and Bayesian Neural Tree, we are able to improve the accuracy of RFQ fill rate predictions and generate the most efficient quote price for market makers. XAI serves as a robust and transparent tool for market participants to navigate the complexities of RFQs with greater precision.

Keywords: Request-For-Quote (RFQ), Explainable AI (XAI), Bayesian Neural Tree, XGBoost, Market Making, Fixed Income / Less Liquid Assets

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced mathematical and statistical concepts (Bayesian Neural Trees, probabilistic modeling, data generation via functions) but relies entirely on a simulated dataset for its analysis, lacking real-world data or backtesting results.

  flowchart TD
    A["Research Goal: Predict RFQ Fill Probability<br>and Optimize Quote Prices"] --> B
    subgraph B ["Data & Inputs"]
        B1["Historical RFQ Data"]
        B2["Market Conditions<br>Liquidity & Volatility"]
    end
    B --> C
    subgraph C ["Key Methodology: Machine Learning Models"]
        C1["Logistic Regression"]
        C2["Random Forest"]
        C3["XGBoost"]
        C4["Bayesian Neural Tree"]
    end
    C --> D["Computational Process:<br>Train Models & Evaluate Performance"]
    D --> E
    subgraph E ["Key Findings & Outcomes"]
        E1["Improved Prediction Accuracy<br>for RFQ Fulfillment"]
        E2["Generated Efficient<br>Quote Prices for Market Makers"]
        E3["XAI Provides Transparency &<br>Explanations for Market Participants"]
    end