Stochastic Control and Games in Intraday Markets

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In this era of electronic markets, there are a number of new challenges faced by institutional investors (e.g., pension plans & mutual funds, and hence individuals), including how to: efficiently utilize large data feeds for making trading decisions; account for actions from a large number of heterogeneous traders to mitigate risks; incorporate latent information that drives markets; and understanding how to deal with model misspecification. As well, regulators need to study how to best manage and regulate traders to avoid, e.g., market manipulation and/or mini-flash crashes.*** This proposal aims to provide much needed insight into intraday financial markets by looking at empirical & computational aspects, and by studying mathematical problems arising in the context of intraday trading.***Large Stochastic Games*** Electronic markets are essentially large uncooperative games. The mean-field game (MFG) approach solves such problems by approximating the large finite game with the limit of infinite number of players. This proposal aims to generalize MFGs to make the results applicable to real intra-day markets by including features such as latent factors, heterogeneous agents, differing information sets, and prior assumptions. The goal is to understand how large number of interacting agents form markets, and the focus will be on applicable results that can be applied to inform traders, as well as, regulators on how to mitigate risks.***Machine Learning & Games*** Important inter-relationships across markets and assets, as well as the role of latent states, have been largely ignored in the academic literature. I propose to develop data-driven approaches by applying techniques from, and developing new ones in, machine learning. Specifically, I aim to develop reinforcement learning (RL) approaches that combine computational approaches with model-based approaches taken by financial mathematicians. RL uses the reaction of a system to an agent's action in an attempt to optimize some objective (such as a risk-return trader off). Generally, RL produces results that are difficult for regulators and traders to interpret. Model-based approaches, however, produce financially sound results, but are too rigid. I propose to combine these two completely separate lines research so that regulators and traders can be sure that recommendations are data-driven, but financially sound.***Anticipated Impact *** This research agenda will have significant impact in our understanding of intraday markets and, simultaneously, developments in MFGs, model uncertainty, and reinforcement learning. Industrial practitioners, PhD students, and other academics, will benefit from the research agenda I propose here. Regulators will benefit from the insights stemming from the results, as I aim to highlight what rules can mitigate risks such as market manipulation and mini flash crashes.