In recent years a growing empirical literature has found that small investor trading behavior can cause market prices to significantly and systematically deviate from their fundamental values. Using a large database of retail investor trades, Kumar and Lee (2006) show that small investors tend to buy (or sell) stocks in concert. Such systematic retail trading explains return comovements for stocks that are particularly costly to arbitrage.
Hvidkjaer (2008) and Barber, Odean and Zhu (2009) show that stocks with intense small-trader buying pressure tend to be overpriced and go through prolonged under performance in the future. Andrade, Chang and Seasholes (2006) document that weekly changes in margin holdings from individual investors are positively correlated with contemporaneous returns and negatively correlated with subsequent returns. Barber and Odean (2007) find that for stocks with high retail trader concentration, the aggregate buy-sell imbalance of individual investors for the stocks is related contemporaneously to their returns.
The New Keynesian DSGE model has become the workhorse model for monetary policy analysis. Derived from microfoundations, the framework is not only suitable for studying how the economy responds to shocks, and the role of various frictions, but it also provides a welfare criterion for evaluating alternative policies. Remarkably, the central banks loss function emerging from this class of models takes the same form as previously assumed in the literature, penalizing variations in the output gap and inflation. A fundamental difference, however, is that while the literature has traditionally assigned about equal weight to inflation and output gap stabilization, the welfare criterion derived from microfoundations gives a much higher weight to inflation stabilization.
While households prefer a balanced consumption basket, inflation causes relative price dispersion between firms, which affects the allocation of consumption among different goods. Calibration of the model to match an empirically plausible markup, implies very elastic demand curves. As a consequence, even a small degree of price dispersion is very costly in welfare terms, as it implies large distortions in households allocation of consumption among different goods. This is not consistent with the sluggish behavior of market shares estimated in the customer market literature. Gottfries (2002) and Lundin et al. (2009), for instance, find short run price elasticities which are smaller than unity.
Swarm intelligence is a new research paradigm that offers novel approaches for studying and solving distributed problems using solutions inspired by social insects and other natural behaviors of vertebrates. In this thesis, we present methodologies for modeling artificial, mobile systems within the swarm intelligence framework. The proposed methodologies provide guidelines in the study and design of artificial swarm systems for the following two classes of experiments: distributed sensing and distributed manipulation.
Event discovery and information dissemination through local communication in artificial swarm systems present similar characteristics as natural phenomena such as foraging and food discovery in insect colonies and the spread of infectious diseases in animal populations, respectively. We show that the artificial systems can be described in similar mathematical terms as those used to describe the natural systems. The proposed models can be classified in two main categories: non-embodied and embodied models. In the first category agents are modeled as mobile bodiless points, whereas the other models take into account the physical interference between agents resulting from embodiment. Furthermore, within each category, we distinguish two subcategories: spatial and nonspatial models. In the spatial models we keep track of the trajectory of each agent, the correlation between the positions occupied by the agents over consecutive time steps, or make use of the spatial distribution resulting from the movement pattern of the agents. In the nonspatial models we assume that agents hop around randomly and occupy independent positions over consecutive time steps.
