Yeon Sik Eric Cho
PDF Versions Resume
KIS Pricing - Seoul, Korea (June 2017 - June 2020)
Alternative Investment Valuation R&D Project Leader
- Lead team of 5 developers to develop valuation platform responsible for 70%+ automation.
- Build and maintain valuation models for financial derivatives
- Replicate academic market anomalies in Korean Market (PEAD, FF5 Factors)
- Develop factor based discount rate model (FF5 - Size, Profitability, Investment)
Working Paper (2020)
Abstract: Despite a prominent role that financial databases play in information dissemination, the extent of information dissemination effect has largely been unexplored. Using data on update timing of one of the most prominent company fundamentals database in South Korea, KISLINE, I explore the question of whether information dissemination role played by financial databases affects the financial markets. Using exogenous variation on update timing due to human factors, I find that daily stock returns on average increase by 0.1-0.2\% following database update. The increase in daily stock returns appears to last for roughly a month and is most prevalent for group of stocks that experienced modest return decline prior to earnings report. I argue databases do indeed affect the stock market through lowering information costs.
Abstract: The residential housing market is dominated by households that are often inefficient at processing information. This paper presents evidence that media plays an information-providing role that affects short-term housing price dynamics. A large volume of housing news can create differing views on the housing market among households. Coupled with the shorting constraint in the housing market, differing views on the housing market leads to short-term increase in house prices. I provide evidence for this hypothesis in two ways. I first provide a causal evidence using plausibly exogenous variation on housing news around FOMC meeting dates. Second, I show using county-level panel time-series regression that an increase in housing news is associated with a 7-10 bp (annualized) increase in housing return over the next 4 to 6 months. Using deep learning to classify housing news into positive and negative, I argue that the media effect is mostly driven by positive news, which is consistent with shorting constraint argument.
Part of PhD Dissetation (2017)
Abstract: Do local media affect how individual traders trade? The effects of local media on the markets have remained largely unexplored despite the relatively large role of local media in informing households and the heterogeneity of local media across regions. This study investigates one facet of the relationship between local media and the markets: the incremental market influence of local media over nationwide media. I find that local media predict individual investors’ trading patterns. In particular, I find that cities in which local media mention a particular stock see substantial increases in the proportion of households that trade the stock over subsequent periods. Using earnings-announcement-related news as a plausibly exogenous news shock, I also present evidence that this relation may be causal. Lastly, I present evidence that the local media effect may work through a salient channel.
Ph.D. & M.S. Management, Finance ‘17 (GPA 3.53/4.0)
- Interests: Market Anomalies, Information Friction, Behavioral Finance
B.A. Economics & Mathematics ‘12 (Magna Cum Laude – GPA 3.87/4.3)
- Thesis: Endowment Effect and Projection Bias on Clothes Disposal Behavior (pdf)
High School ’08 (Cum Laude)
- MBA Behavioral Finance, Professor Avanidhar Subrahmanyam (Spring 2016, 2017)
- MFE Introduction to Stochastic Calculus and Derivatives, Professor Stavros Panageas (Winter 2017)
- Real Estate Economics, Capital MaArkets, and Securitization, Professor Stuart Gabriel (Fall 2016)
- MFE Behavioral Finance, Professor Avanidhar Subrahmanyam (Fall 2014, 2015)}
- FEMBA Foundations in Finance, Professor Barney Hartman Glaser (Spring 2014, 2015)