Oh, Simon Sangmin

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2016 - 20173
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  • Publication
    News Coverage Premium: A Tale of Two Papers
    (2017-01-01) Oh, Sangmin Simon
    This study attempts to reconcile two papers that provide contradictory findings on the cross-sectional relation between news coverage and expected stock returns. I first identify elements of their research designs that may be responsible for their discrepancies and then directly examine the existence of news coverage premium using Ravenpack data. I find that stocks with little news coverage earn higher returns than stocks with high news coverage even after controlling for well-known risk factors, but stocks with zero news coverage do not seem to outperform stocks with high news coverage. My findings suggest that the news coverage premium may be sensitive to the time period of the analyses as well as the presence of market crashes.
  • Publication
    Assessing Models using Monte Carlo Simulations
    (2017-01-01) Oh, Simon
    We establish a framework for assessing the validity of a given model using Monte Carlo simulations and inferences based on sampling distributions. Using this framework, we show that geometric brownian motion alone cannot generate a majority of the patterns in the distribution of stock returns and wealth creation. Our paper represents an often overlooked departure from the traditional way of validating asset pricing models, in which implications are derived, parameters calibrated, and magnitudes compared to empirical data. Instead, we seek to leverage the power of large numbers by conducting numerous simulations and assessing the probability that they contain our realized stock market.
  • Publication
    ImpactScore: A Novel Credit Score for Social Impact
    (2016-01-01) Oh, Simon Sangmin; Lee, Jade Pooreum; Meehl, April I
    Socially motivated lenders pursue lending that considers both financial return and social good, yet they lack a systematic tool to incorporate such considerations into their decisions. This paper proposes the application of credit scoring mechanisms not only to the likelihood of default but also to the likelihood of happiness. Using the existing data on microcredit loan applicants in Bosnia and Herzegovina, we construct a full credit scoring model that involves the construction of outcome variables to accurately capture borrower’s change in subjective well-being, the classification of input variables depending on the ease of information acquisition, and the selection of the model based on different criteria. We also find that the variables on the household’s level of consumption have significant explanatory power in predicting future subjective well-being of loan applicants.