Research

In this paper I propose a new theoretical framework that explains both over- and under-reaction of prices to new information. My model incorporates a behavioral investor who selectively chooses whether to incorporate new information. When the agent chooses to ignore information, the price under-reacts. On the other hand, when the agent chooses to incorporate information, the price over-reacts. While existing frameworks in the literature use multiple behavioral biases simultaneously to generate both over- and under-reaction, I rely on a single behavioral bias. My model generates novel empirical predictions. More specifically, over-reaction is stronger when the new information is extreme and when the agent faces higher uncertainty (and vice versa for under-reaction). I provide empirical support for these predictions using winner-loser portfolios. In addition, my model does not produce some existing empirical predictions that have been contested, e.g., the life-cycle hypothesis of momentum and reversal.

Available on SSRN

This paper studies the conjecture that investors prefer derivative markets over equity markets when hedging certain risks. We show theoretically that investors with smooth ambiguity aversion preferences substitute assets with large beta uncertainty (long-short stock portfolios) with derivatives that are not subject to beta uncertainty. More specifically, we show that equilibrium risk premiums for assets with large beta uncertainty decline once derivatives with less beta uncertainty can be traded. In line with this theory, we find that the inflation risk premium decreases significantly when TIPS are introduced.

Available on SSRN.

The Effect of Option Introduction on Stock Prices - the Parameter Uncertainty Channel.

Identification of Over- and Under-reaction in Option Prices.

Demand for Skewness - The Life-cycle Portfolio Choice Channel.

Recursive Ambiguity Aversion - Experimental Setting.

Marginal Conditional Stochastic Dominance (MCSD) states the probabilistic conditions under which, given a specific portfolio, one risky asset is marginally preferred to another by all risk-averse investors. Furthermore, by increasing the share of dominating assets and reducing the share of dominated assets one can improve the portfolio performance for all these investors. We use this standard MCSD model sequentially to build optimal portfolios that are then compared to the optimal portfolios obtained from Chow’s MCSD statistical test model. These portfolios are furthermore compared to the portfolios obtained from the recently developed Almost Marginal Conditional Stochastic Dominance (AMCSD) model. The AMCSD model restricts the class of risk-averse investors by not including extreme case utility functions and reducing the incidence of unrealistic behavior under uncertainty. For each model, an algorithm is developed to manage the various dynamic portfolios traded on the New York, Frankfurt, London, and Tel Aviv stock exchanges during the years 2000-2012. The results show how the various MCSD optimal portfolios provide valid investment alternatives to stochastic dominance optimization. MCSD and AMCSD investment models dramatically improve the initial portfolios and accumulate higher returns while the strategy derived from Chow’s statistical test performed poorly and did not yield any positive return.

The Open Journal of Economics and Finance, 2018, 2, pp. 36-44