National Repository of Grey Literature 3 records found  Search took 0.01 seconds. 
Inattentive price discovery in ETFs
Kosar, Mariia ; Mikhalishchev, Sergei
This paper studies the information choice of exchange-traded funds (ETF) investors, and its impact on the price efficiency of underlying stocks. First, we show that the learning of stock-specific information can occur at the ETF level. Our results suggest that ETF investors respond endogenously to changes in the fundamental value of underlying stocks, in line with the rational inattention theory. Second, we provide evidence that ETFs facilitate propagation of idiosyncratic shocks across its constituents.
Essays on Implications of Bounded Rationality to Choice
Mikhalishchev, Sergei ; Matějka, Filip (advisor) ; Zorn, Peter (referee) ; Lian, Chen (referee)
In the first chapter, we introduce a new role of quotas, e.g., labor market quotas: the attentional role. We study the effects of quota implementation on the attention allo- cation strategy of a rationally inattentive (RI) manager. We find that quotas induce attention: a RI manager who is forced to fulfill a quota, unlike an unrestricted RI man- ager, never rejects minority candidates without acquiring information about them. We also demonstrate that, in our model, quotas are behaviorally equivalent to subsidies. We further analyze different goals that the social planner can achieve by implementing quo- tas. First, quotas can eliminate statistical discrimination, i.e., make chances of being hired independent from group identity. Second, when the hiring manager has inaccurate beliefs about the distribution of candidates' productivities, the social planner can make the manager behave as if she has correct beliefs. Finally, we show how our results can be used to set a quota level that increases the expected value of the chosen candidates. In the second chapter, we study the information choice of exchange-traded funds (ETF) investors, and its impact on the price efficiency of underlying stocks. First, we show that the learning of stock-specific information happens at the ETF level. Further, our results suggest...
Optimal menu when agents make mistakes
Mikhalishchev, Sergei
This paper studies a welfare maximization problem with heterogeneous agents. A social planner designs a menu of choices for agents who misperceive either the properties of options or their own preferences. When agents misperceive the true properties of alternatives, it is optimal to limit a menu when the probability of a mistaken choice is moderately high. Additionally, it could be optimal to construct the menu with more distinct alternatives. However, when agents misperceive their own tastes, it is optimal to limit choice only when agents choose randomly, and to propose alternatives that are more similar when there is a greater probability of agents making a mistake.

Interested in being notified about new results for this query?
Subscribe to the RSS feed.