Stochastic Control and Games in Intraday Markets

在这个电子市场时代，机构投资者面临着许多新的挑战(例如，养老金计划考虑了大量不同类型的交易者为降低风险而采取的行动；纳入推动市场的潜在信息；以及了解如何处理模型错误说明)。此外，监管机构需要研究如何最好地管理和监管交易员，以避免例如市场操纵和/或小型闪电崩盘。 这项建议旨在通过研究经验错误的代理人、不同的信息集和先前的假设，提供对日内金融市场亟需的洞察。其目标是了解大量相互作用的因素是如何形成市场的，重点将是可用于向交易员和监管机构提供如何降低风险的适用结果。 机器学习在S的行动中试图优化一些目标(如一个风险收益低的交易者)。一般来说，RL产生的结果是监管机构和交易员难以解释的。然而，基于模型的方法产生了财务上合理的结果，但过于僵化。我建议将这两个完全不同的研究项目结合起来，这样监管者和交易员就可以确信，建议是数据驱动的，但财务上是合理的。 预期影响 这一研究议程将对我们对盘中市场的理解产生重大影响，同时也将对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带来的洞察力，因为我的目标是强调哪些规则可以缓解市场操纵和小型闪电崩盘等风险。

In this era of electronic markets, there are a number of new challenges faced by institutional investors (e.g., pension plans 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 eous 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 squo;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.