Jungshik Hur

Aggregate Idiosyncratic Volatility, Dynamic Aspects of Loss Aversion, and Narrow Framing

We test the dynamic aspects of the loss aversion feature of Kahneman and Tversky (Prospect theory: an analysis of decision under risk. Econometrica 47:263–291, 1979) and find that idiosyncratic volatility is negatively associated with unrealized gains of stock returns. Moreover, we show that this negative relationship is stronger for stocks with high individual investors’ holdings. Finally, we show that controlling for firm age as defined by Fink et al. (What drove the increase in idiosyncratic volatility during the internet boom? J Financ Quant Anal 45:1253–1278, 2010) eliminates the significance of retail trading proportions as a driver of idiosyncratic volatility. These findings are robust to price, sentiment, and IPO dates. Bivariate vector auto-regression confirms the causality of unrealized gains of stock returns on idiosyncratic volatility.

The Relation between Market Value, Past Performance and Extreme Returns of Common Stocks in the United States, 1926-2012

We study the interrelation between the size and winner-loser effects in U.S. stock returns, including their response to extreme returns. We find that size effect and winner-loser effect are present in data up to 2012. These are related but separate effects. However these effects are due to presence of a small number of extreme return observations in the sample. The size effect and winner-loser effects are non-existent after extreme returns are removed from the sample. Our results question the existence of size and winner-loser anomalies in the market efficiency literature.

Cross-sectional regression of returns on betas and portfolio grouping procedures

This paper shows that the deviation of the estimated coefficient of beta from the market risk premium in cross-sectional regression of returns on betas is a direct consequence of the cross-sectional relation between the estimated alphas and betas. Therefore, the portfolio grouping procedure results in systematic cross-sectional relationship between the alphas and betas, causing a deviation in the estimated coefficient of beta in either direction. When firm size is used as the only portfolio grouping variable (Table AI in Fama and French, 1992), the estimated alphas and betas across portfolios are positively related, causing the estimated coefficient of beta to be upwardly biased. However, when beta is used as the only portfolio grouping variable (Table 2 in Kothari et al., 1995), the estimated alphas and betas across portfolios are negatively related, causing the estimated coefficient of beta to be downward biased. We show that forming portfolios on alphas and betas independently can adequately control for this deviation.