I/B/E/S removes 6% of one-quarter-ahead earnings forecasts from the calculation of the consensus forecast. This study examines managers' role in these removals. We show optimistic forecasts are removed more often than pessimistic forecasts, after controlling for removal policies that I/B/E/S...

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...

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...

With the advances in time-series prediction, several recent developments in machine learning have shown that integrating prediction methods into portfolio selection is a great opportunity. In this paper, we propose a novel approach to portfolio formation strategy based on a hybrid machine...