2019/11/28 by Mahmoud Mahfouz, Angelos Filos, Mahfouz, Mahmoud +14
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Engineering · Psychology · #Adversary #Artificial Intelligence in Games #Auction Theory and Applications #Business #Computational Finance (q-fin.CP) #Computer science #Computer security #Dynamics (music) #Economics #Engineering #FOS: Economics and business #Financial market #Management science #Microeconomics #Order (exchange) #Psychology #Reinforcement Learning in Robotics #Risk analysis (engineering) #Trading and Market Microstructure (q-fin.TR) #Work (physics) #q-fin.CP #q-fin.TR
paper · pdf · doi:10.48550/arxiv.1911.12816
arxiv created 2019/11/28 · openalex publication_date 2019/11/28 · arxiv updated 2019/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The dynamics of financial markets are driven by the interactions between participants, as well as the trading mechanisms and regulatory frameworks that govern these interactions. Decision-makers would rather not ignore the impact of other participants on these dynamics and should employ tools and models that take this into account. To this end, we demonstrate the efficacy of applying opponent-modeling in a number of simulated market settings. While our simulations are simplified representations of actual market dynamics, they provide an idealized "playground" in which our techniques can be demonstrated and tested. We present this work with the aim that our techniques could be refined and, with some effort, scaled up to the full complexity of real-world market scenarios. We hope that the results presented encourage practitioners to adopt opponent-modeling methods and apply them online systems, in order to enable not only reactive but also proactive decisions to be made.