Stochastic Control and Games in Intraday Markets

在这个电子市场时代，机构投资者（例如，养老金计划和共同基金，以及个人）面临着许多新的挑战，包括如何：有效地利用大数据来做出交易决策；考虑到大量不同交易者为降低风险而采取的行动；整合驱动市场的潜在信息；了解如何处理模型规格错误。此外，监管机构需要研究如何最好地管理和监管交易员，以避免市场操纵和/或迷你闪电崩盘。***本提案旨在通过观察经验和计算方面，以及研究日内交易背景下出现的数学问题，为日内金融市场提供急需的洞察力。大型随机博弈电子市场本质上是大型非合作博弈。平均场对策（MFG）方法通过逼近具有无限参与者数量限制的大型有限对策来解决这类问题。本提案旨在通过包含潜在因素、异质代理、不同信息集和先前假设等特征，对mfg进行概括，使结果适用于真实的日内市场。目标是了解大量相互作用的代理如何形成市场，重点将放在可应用的结果上，这些结果可用于告知交易者以及监管机构如何降低风险。***市场和资产之间的重要相互关系，以及潜在状态的作用，在学术文献中基本上被忽视了。我建议通过应用机器学习中的技术和开发机器学习中的新技术来开发数据驱动的方法。具体来说，我的目标是开发强化学习（RL）方法，将计算方法与金融数学家采用的基于模型的方法相结合。强化学习使用系统对代理行为的反应，试图优化某些目标（如风险回报交易者）。一般来说，强化学习产生的结果很难让监管机构和交易员解释。然而，基于模型的方法可以产生财务上合理的结果，但过于僵化。我建议将这两种完全独立的研究结合起来，这样监管机构和交易员就可以确保建议是由数据驱动的，但在财务上是合理的。***预期影响***本研究议程将对我们对日内市场的理解产生重大影响，同时对mfg、模型不确定性和强化学习的发展产生重大影响。工业从业者、博士生和其他学者将从我在这里提出的研究议程中受益。监管机构将受益于这些结果所产生的见解，因为我的目的是强调哪些规则可以减轻市场操纵和迷你闪电崩盘等风险。

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.