Ph.D. Defense by Min Kyu Sim

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  • Date/Time:
    • Tuesday September 30, 2014 - Wednesday October 1, 2014
      3:00 pm - 4:59 pm
  • Location: Academic Area of Groseclose (2nd floor)
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Summary Sentence: Empirical findings in asset price dynamics revealed by quantitative modelling.

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Ph.D. Defense by Min Kyu Sim

Empirical findings in asset price dynamics revealed by quantitative modelling

Advisor:  Dr. Shijie Deng and Dr. Xiaoming Huo 

Committee members:  Dr. Sebastian Pokutta, Dr. Soohun Kim (College of Business) and Dr. Kautilya Raval (Bank of America)

Tuesday, September 30th, 3:00 PM, Academic Area of Groseclose (2nd floor).

This dissertation addresses the fundamental question of what factors moves stock prices and how they work, based on the two different frequency level of financial data. The first part concerns with daily or monthly frequency of data and aims to identify what systematic risk factor affects stock return. The second part concerns with data whose frequency is a second or even a millisecond, and aims to identify how the hidden supply-demand of a stock affects the stock price dynamics. With growing importance of quantitative models on financial data, we expect our study arouse more systematic approaches to financial market dynamics. In the first part, we propose an econometric measure, terms as modularity, for characterizing the cluster structure of the daily price-returns of a universe of stocks. We conduct cluster analysis to establish the modularity measure. A high level of modularity implies that the cluster structure of the universe of stocks is highly evident, and low modularity implies blurred cluster structure. The modularity measure is shown to be related to the cycle of the economy. In addition, individual stock's sensitivity to the measure is shown to be related to its expected return. From 1992 to 2011, the average return of stocks with the lowest sensitivity exceeds that of the stocks with highest sensitivities by approximately 7.6\%. The consideration of modularity as an asset pricing factor expands the investment opportunity set to passive investors. In the second part, we focus on analyzing the effect of the hidden demands/supplies in the high-frequency trading of a stock on its price dynamics. We propose a statistical estimation model for estimating hidden liquidity based on the limit orderbook data. Not only the estimated hidden liquidity can explain the market properties such as orderbook pressure better, the estimated hidden liquidity also refines the existing price impact model and achieves higher explanation power. The enhanced price impact model can serve as a foundation for devising optimal order execution strategies. The significance of our price impact model is tested by a simulated stock trading model. Tested on 62 trading days, our strategy yield significant transaction cost savings over benchmark methods.

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Graduate Studies

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graduate students, isye, Phd Defense
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  • Created On: Sep 11, 2014 - 10:24am
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