An uninformed sender designs a mechanism that discloses information about her type to a privately informed receiver, who then decides whether to act. I impose a single-crossing assumption, so that the receiver with a higher type is more willing to act. Using a linear programming approach, I...

Algorithms play an increasingly important role in economic situations. Often these situations are strategic, where the artificial intelligence may or may not be cooperative. We study the determinants and forms of algorithmic cooperation in the infinitely repeated prisoner's dilemma. We run a...

While common machine learning algorithms focus on minimizing the mean-square errors of model fit, we show that genetic programming, GP, is well-suited to maximize an economic objective, the Sharpe ratio of the usual spread portfolio in the cross-section of expected stock returns. In contrast to...

Concerns about the fragility of the financial system caused by the OTC derivatives market has encouraged the increased use of counterparty risk mitigation techniques including the use of market compression. In this process groups of market participants share position information via a third...

We consider second, third, fourth and fifth order stochastic dominance (SSD, TSD, FOSD and FISD, respectively) as well as decreasing absolute risk aversion (DARA) stochastic dominance (DSD). For comparison with DSD we also consider stochastic dominance (ESD) based on CARA utility functions....

This paper examines how volatility positions can be optimally constructed by modeling the selection process as a linear discrete ill-posed problem with box constraints. We show how this framework allows for a priori investor expectations and risk parameters to be applied in the optimization...

We clarify that the widely used estimation method for the LOT liquidity model in the market microstructure literature is improper in the sense of econometric inference. Based on an extensive simulation study and a real data analysis, we show that this method not only overestimates the true...