Cognitive Limitations and Behavioral Biases in the Asset Pricing Context

Cognitive Limitations and Behavioral Biases In The Asset Pricing Context MAER Thesis Asbtract Giorgi Chavchanidze I incorporate behavioral and bounded rationality elements into a single asset-pricing frame- work by setting up a two-period consumption-based portfolio selection problem in which a representative agent has biased priors, does not observe the current state and thus has in- complete information about future state probabilities. He forms posterior beliefs using signals that he selects according to the rational inattention discrete choice framework of Matějka and McKay (2015), where the precision of the beliefs depend intuitively on the priors and the cost of information λ. In the case of log-utility, the optimal portfolio is a convex combination of the N portfolios the investor would have selected in each of the N states if they were fully observable, where the weights reflect the subjective posterior likelihood of time-zero states. The posterior beliefs are induced by parsimonious reweighing of priors, where the weights depend on λ, discount factor β and the relative entropies of the future state distributions induced by different time-zero states. Using a two-state example, I demonstrate how the cost of information and biases can be jointly analyzed in this framework and discuss implied..