Allocation between factor portfolios can bring significant advantages over traditional portfolio optimization performed among individual assets or asset classes. One such advantage is a substantial dimension reduction when one's attention turns from many assets to few factors. This, however,...

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...

In this paper we outline the Lagrangian constrained optimization method to solve complex problems subject to constraints. Firstly we summarize the Lagrangian constrained optimization routine. Secondly we outline a detailed implementation strategy. Thirdly and finally we provide example and solve...

We analyze a dynamic multi-pollutant problem where abatement costs of several pollutants are not separable. The pollutants can be either technological substitutes or complements. Environmental damage is induced by the stock of accumulated pollution. We find that optimal emission paths are...

We analyze a dynamic multi-pollutant problem where abatement costs of several pollutants are not separable. The pollutants can be either technological substitutes or complements. Environmental damage is induced by the stock of accumulated pollution. We find that optimal emission paths are...