This paper studies market selection in an Arrow-Debreu economy with complete markets where agents learn over misspecified models. Under model misspecification, standard Bayesian learning loses its formal justification and biased learning processes may provide a selection advantage. However,...

The idiosyncratic (microscopic) and systemic (macroscopic) components of market structure have been shown to be responsible for the departure of the optimal mean-variance allocation from the heuristic 'equally-weighted' portfolio. In this paper, we exploit clustering techniques derived from...

We consider a market economy where two rational agents are able to learn the distribution of future events. In this context, we study whether moving away from the standard Bayesian belief updating, in the sense of under-reaction to some degree to new information, may be strategically convenient...

The behavioural finance literature attributes the persistent market misvaluation observed in real data to the presence of deviations from rational thinking of the actors involved. Cognitive biases and the use of simple heuristics can be described using expected utility maximising agents that...

Empirical evidence suggests that asset returns correlate more strongly in bear markets than conventional correlation estimates imply. We propose a method for determining complete tail-correlation matrices based on Value-at-Risk (VaR) estimates. We demonstrate how to obtain more efficient...

Most socially responsible investment funds combine a sustainability objective with a tracking error constraint. We characterize the impact of a sustainability constraint on the mean-tracking error efficient frontier and illustrate this on a universe of US stocks for the period 2003–2010.