康奈爾金融學講席教授做客清華五道口 | 活動報名

1961

2019年04月17日10:00-11:30

清華大學五道口金融學院3號樓300教室

已結束

嘉賓介紹

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.

講座內容

清華論壇第八十六講暨清華五道口全球名師大講堂

Tsinghua Forum & Tsinghua PBCSF Global Academic Leader Forum

演講主題： 機器時代的微觀結構

Microstructure in the Machine Age

演講嘉賓： 莫林?奧哈拉 Maureen O’Hara

康奈爾大學羅伯特?W?珀塞爾金融學講席教授

Robert W. Purcell Professor of Finance, Cornell University

時間：2019年4月17日上午 10:00-11:30

地點：清華大學五道口金融學院3號樓300教室

主辦單位：清華大學學術委員會、清華大學五道口金融學院、清華大學國家金融研究院

演講語言：英語

報告摘要：

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.

參會條件：清華大學師生、清華大學五道口金融學院校友及合作伙伴

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