Equilibrium Portfolio Strategies in the Presence of Sentiment Risk and Excess Volatility

Abstract

Our objective is to identify the trading strategy that would allow an investor to take advantage of "excessive" stock price volatility and "sentiment" fluctuations. We construct a general equilibrium "difference-of-opinion" model of sentiment in which there are two classes of agents, one of which is overconfident about a public signal, while still optimizing intertemporally. Overconfident investors overreact to the signal and introduce an additional risk factor causing stock prices to be excessively volatile. Consequently, rational investors choose a conservative portfolio; moreover, this portfolio depends not just on the current price divergence but also on their prediction about future sentiment and the speed of price convergence. Copyright (c) 2009 the American Finance Association.

Rational asset pricing bubbles and portfolio constraints

“…3 which plots the relative contribution of the bubbles to the equilibrium prices of the stock and the riskless asset as functions of the model horizon and 15 This figure should be compared to those found in the literature studying the effect of bounded rationality on asset prices, see e.g. [33,7] and [19]. These models show that the consumption share of irrational traders decreases over time but the speed of this decline is usually rather slow.…”

“…The aggregate restriction of Eq. (19) then implies that the sum of these wealth processes coincides with the market portfolio and guarantees that the market for the riskless asset clears at all times. This further implies that the agents' optimal portfolios satisfy σ t (π 1t + π 2t − S t ) = 0 and it follows that the stock market clears since the volatility matrix is assumed to be invertible.…”

“…are Bayesian learners and to models in which they have boundedly rational beliefs (see [33,7] and [19]). …”

A non-local free boundary problem arising in a theory of financial bubbles

“…Assuming that investors agree on the evolution of differences in beliefs amounts to assuming that investors in each group know the model used by the other group and agree to disagree. The model developed in [2] postulates a particular information structure and derives equations (1.11)-(1.13) using results on optimal filtering (see also [6]). …”

Asset pricing with Second-Order Esscher Transforms

“…This asset pricing framework clearly provides an important generalization of the changes of probability measures proposed by the continuous-time and discrete-time literature on Quadratic-Gaussian (Q G) or on Wishart Autoregressive (W A R) asset pricing models where, for tractability reasons, the risk-correction coefficients are given by constant parameters or are specified as deterministic function of time or as affine functions of the factor (see Buraschi et al, 2010;Cheng and Scaillet, 2007;Dumas et al, 2009;Gourieroux et al, 2009;Gourieroux and Sufana, 2011;Leippold and Wu, 2002).…”