EAGER: CIFRAM: Understanding High Frequency Trading Activity on the Nanosecond Time Scale

这个多学科项目由伊利诺伊大学厄巴纳香槟分校的毛野和圣地亚哥超级计算中心的罗伯特·辛科维茨领导，作为OFR-NSF支持金融信息学研究合作伙伴关系的一部分，以审查纳秒(10-9秒)时间框架内交易活动对金融系统的影响。这个探索性项目旨在解决两个关键问题：第一，纳秒级别的速度竞争是创造还是摧毁社会价值？更具体地说，速度的提高是改善了我们通常衡量市场质量的指标，如买卖价差、市场深度和市场效率，还是增加了波动性和不稳定性？其次，在纳秒世界里有没有新类型的市场操纵？该项目利用强大的超级计算机，通过构建具有纳秒分辨率时间戳的金融市场按顺序快照，在纳秒时间尺度上调查异常交易活动和可疑市场事件。该项目旨在开发有效的计算和分析方法，以探索在纳秒范围内发生交易时是否需要新的监管规定，如果需要，如何设计最优监管政策。这是一个具有挑战性的问题，一方面是因为流动性、价格发现、订单取消和速度之间的内生关系，另一方面是因为产生了大量数据。研究团队计划使用外生技术冲击和纳斯达克渠道分配作为识别策略。通过识别数据中的外生技术冲击，该团队旨在建立速度和市场质量指标之间的因果关系。他们还使用随机的纳斯达克通道分配来检查每个通道内的消息流是否存在异常的协同移动，这与引用填充一致。近年来，股票市场经历了许多与高频交易相关的问题，包括2010年5月6日的闪电崩盘，BATS和Facebook的首次公开募股，Knight Capital的亏损，以及最近由Twitter上的一个谣言引发的崩盘。然而，人们对纳秒级交易的市场影响知之甚少。制定有效的政策和警报系统需要了解交易模式及其对金融系统的影响之间的因果关系。该项目如果成功，将有助于识别和描述政策选择和市场纪律，以确保在一个交易以闪电速度进行的世界中建立一个安全、健康和公平的金融体系。该项目在计算机科学、高性能计算和金融的交叉点促进跨学科合作和基于研究的高级培训。它丰富了金融信息学新兴跨学科领域下一代研究人员的课程和培训。

The multidisciplinary project, led by Mao Ye of University of Illinois at Urbana Champaign and Robert Sinkovits of the San Diego Supercomputing Center, funded as part of the OFR-NSF Partnership in Support of Research Collaborations in Finance Informaticsaims to examine the impact of trading activity at the nanosecond (10-9 second) timeframe on financial system. This exploratory project aims to address two key questions: First, does competition in speed at the nanosecond level create or destroy social value? To be more specific, does an increase of speed improve our usual measures of market quality such as bid-ask spread, market depth, and market efficiency or does it increase levels of volatility and instability? Second, are there any new types of market manipulations in the nanosecond world? The project leverages powerful supercomputers to investigate abnormal trading activity and suspicious market events on the nanosecond timescale by constructing an order-by-order snapshot of financial markets with nanosecond-resolution time stamps. The project aims to develop effective computational and analytic approaches to exploring whether new regulations are needed when trading occurs on a nanosecond scale, and if so, how to design optimal regulatory policies. This is a challenging problem due to both the endogenous relationship between liquidity, price discovery, order cancellation, and speed on the one hand and the massive amounts of data generated on the other. The research team plans to use exogenous technology shocks and NASDAQ channel assignment as identification strategy. By identifying exogenous technology shocks in the data, the team aims to establish the casual relationship between speed and market quality measures. They also use random NASDAQ channel assignment to examine whether there is abnormal co-movement in message flow within each channel, evidence consistent with quote stuffing. In the recent years, equity markets experienced a number of problems related to high frequency trading, including the May 6, 2010 Flash Crash, the initial public offerings of BATS and Facebook, the losses by Knight Capital and more recently, the crash which was precipitated by a rumor originating on Twitter. However, little is known about the market impacts of trading on the nanosecond scale. Development of effective policies and alarm systems requires understanding of the causal relationships between trading patterns and their effects on the financial system. The project, if successful, will help identify and characterize policy options and market discipline to ensure a safe, sound, and fair financial system in a world where trades are made at lightening speeds. The project fosters interdisciplinary collaborations and research-based advanced training at the intersection of computer science, high performance computing, and finance. It enriches the curriculum and training of the next generation of researchers in the emerging interdisciplinary field of Finance Informatics.