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[2407.01566] A Parametric Contextual Online Learning Theory of Brokerage
Summary
This paper presents a parametric contextual online learning theory focused on brokerage, where brokers suggest trading prices based on traders' secret valuations and contextual data. It explores algorithm design and optimal regret guarantees in this sequential trading problem.
Why It Matters
Understanding the dynamics of brokerage through contextual online learning can enhance trading strategies and improve market efficiency. This research contributes to the fields of computational finance and machine learning, providing theoretical foundations that can be applied in real-world trading scenarios.
Key Takeaways
- The study introduces a framework for brokers to suggest prices based on contextual information.
- Optimal theoretical regret guarantees are established under standard assumptions.
- The algorithms developed can potentially improve trading outcomes between traders.
Quantitative Finance > Computational Finance arXiv:2407.01566 (q-fin) [Submitted on 22 May 2024 (v1), last revised 19 Feb 2026 (this version, v3)] Title:A Parametric Contextual Online Learning Theory of Brokerage Authors:François Bachoc, Tommaso Cesari, Roberto Colomboni View a PDF of the paper titled A Parametric Contextual Online Learning Theory of Brokerage, by Fran\c{c}ois Bachoc and 2 other authors View PDF Abstract:We study the role of contextual information in the online learning problem of brokerage between traders. In this sequential problem, at each time step, two traders arrive with secret valuations about an asset they wish to trade. The learner (a broker) suggests a trading (or brokerage) price based on contextual data about the asset and the market conditions. Then, the traders reveal their willingness to buy or sell based on whether their valuations are higher or lower than the brokerage price. A trade occurs if one of the two traders decides to buy and the other to sell, i.e., if the broker's proposed price falls between the smallest and the largest of their two valuations. We design algorithms for this problem and prove optimal theoretical regret guarantees under various standard assumptions. Subjects: Computational Finance (q-fin.CP); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2407.01566 [q-fin.CP] (or arXiv:2407.01566v3 [q-fin.CP] for this version) https://doi.org/10.48550/arXiv.2407....
