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High Frequency Trading Volatility, Market Microstructure Noise and Institutional Investors

thesis
posted on 01.12.2020, 00:00 by Yuting Tan
As stocks being traded more intensely daily, many recent studies show that the efficiency and accuracy of some measures developed from low frequency data may not be well guaranteed. It is worth investigating the relationship between institutional investor's behavior and stock return volatility in this new circumstance. We estimate stock return volatility with the two-scales realized volatility (TSRV) estimator which corrects the effect from market microstructure noise on stock prices. Using all transactions of S\&P 500 constituent stocks over the most recent 10 years, we find a positive association between the levels of institutional ownership and daily stock return volatility. This association is not constant over time. Institutional investors are more conservative while the market is more volatile, and they become more aggressive when market calms down. We also analyze quarterly changes in stock return volatility and institutional ownership ratio, and we find evidence that past increase in institutional ownership changes will positively impact the changes in volatility for the future, and past increases in volatility changes will decrease the institutional ownership in the future. But there is no causality relationship between institutional ownership and market microstructure noise. Most institutional investors act similarly except banks. We also find evidence for institutional herding and that stocks with higher institutional ownership ratios attract more high turnover traders, which could be the possible channels through which institutional investors affect stock return volatility.

History

Advisor

Zhang, Lan

Chair

Zhang, Lan

Department

Business Administration

Degree Grantor

University of Illinois at Chicago

Degree Level

Doctoral

Degree name

PhD, Doctor of Philosophy

Committee Member

Bassett, Gilbert W Chen, Hsiu-Lang Sclove, Stanley L Yang, Jie

Submitted date

December 2020

Thesis type

application/pdf

Language

en

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