康奈尔金融学讲席教授做客清华五道口 | 活动报名

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2019年04月17日10:00-11:30

清华大学五道口金融学院3号楼300教室

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嘉宾介绍


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       Maureen O’Hara is Robert W. Purcell Professor of Finance at the Johnson Graduate School of Management, Cornell University. Professor O'Hara is an expert on market microstructure, and she publishes widely in banking and financial intermediaries, law and finance, and experimental economics. She is the author of numerous journal articles as well as the books Market Microstructure Theory (Blackwell: 1995), and High Frequency Trading: New Realities for Traders, Markets, and Regulators (Risk Books: 2013), and Something for Nothing: Arbitrage and Ethics on Wall Street (WW Norton:2016). A past President of the American Finance Association, the Western Finance Association and the Financial Management Association, she was Executive Editor of the Review of Financial Studies. A member of the CFTC-SEC Emerging Regulatory Issues Task Force (the “flash crash” committee), she has also served on the Global Advisory Board of the Securities Exchange Board of India (SEBI), the Advisory Board of the Office of Financial Research, U.S. Treasury, and the SEC Equity Market Structure Advisory Committee. She was named to Institutional Investors Trading Technology Top 40 and she is currently an Advisor to Symbiont, a company focusing on blockchain and smart securities.

讲座内容


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清华论坛第八十六讲暨清华五道口全球名师大讲堂

Tsinghua Forum & Tsinghua PBCSF Global Academic Leader Forum


演讲主题: 机器时代的微观结构

Microstructure in the Machine Age

演讲嘉宾: 莫林・奥哈拉 Maureen O’Hara

康奈尔大学罗伯特・W・珀塞尔金融学讲席教授

Robert W. Purcell Professor of Finance, Cornell University

时间:2019年4月17日 上午 10:00-11:30

地点:清华大学五道口金融学院3号楼300教室

主办单位:清华大学学术委员会、清华大学五道口金融学院、清华大学国家金融研究院

演讲语言:英语


报告摘要:

Understanding modern market microstructure phenomena requires large amounts of data and advanced mathematical tools. In this paper, we demonstrate how a machine learning algorithm can be applied to microstructural research. We find that simple microstructure measures designed to reflect frictions in a simpler market continue to provide insights into the process of price adjustment. We find that some of these microstructure features with apparent high explanatory power can exhibit low predictive power, and vice versa. We also find that some microstructure-based measures are useful for out-of-sample prediction of various market statistics, leading to questions about the efficiency of markets. Our results are derived using 87 of the most liquid futures contracts across all asset classes.


参会条件:清华大学师生、清华大学五道口金融学院校友及合作伙伴


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