Koustav De is Assistant Professor in Finance at the Gatton College of Business & Economics, University of Kentucky.
Area of Interest:
Behavioral Finance (investor behavior)
Gatton College of Business & Economics,
University of Kentucky.
Using trading data from Finland and the US, I empirically show that investors tend to buy riskier stocks following realized losses. The measure of risk that the investors seem to pay attention to is the market beta of a stock. This behavior of buying higher beta stocks after a realized loss is observed in institutional as well as individual investors, but is more pronounced among individual investors with lower expertise, who on an average buy a new stock with up to 15% higher beta than that of the old stock they were holding. For an agent with utility consistent with prospect theory, this behavior emerges as the optimal response to her problem of maximizing utility within a mental account. Furthermore, this behavior can aggregate up during market downturns and cause return predictability in high beta stocks. With this insight, I suggest a modification to the betting against beta trading strategy that can improve the Sharpe ratio more than twofold.
Estimating the Reference Points of Investors with the Disposition Effect [link]
We estimate the reference points of individual investors who exhibit the disposition effect. We use proportional hazard models to fit their stock selling decisions. We find that while the likelihood function jumps significantly around a reference point of zero, the average maximum likelihood reference point is a positive number, generally around 60 to 190 basis points. We also find that quarterly reference points are correlated with past stock market return, bond yields, and individual experience of previously realized profits. There is heterogeneity in reference points among individuals where some individuals consistently have higher reference points than others and this heterogeneity is correlated with individual attributes like age and the average size of transactions.
National identity predicts public health support during a global pandemic [link]
(Nature Communications 13, 517 (2022): 1-14)
Understanding collective behavior is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviors and support for public health policies in the context of the pandemic.
Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning
(Forthcoming PNAS Nexus)
We applied machine learning on the multi national data collected by the International Collaboration on Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the prediction of COVID-19 attitudinal and behavioral responses. The results from the machine learning approach suggest internalization of moral identity to have the most consistent predictive contribution, followed by that of morality as cooperation, moral identity symbolization, self-control, open-mindedness, collective narcissism, and endorsement of conspiracy theories. We also found a non-negligible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in determining adherence to public health recommendations during the pandemic.