Abstract Graphical models are a powerful tool to estimate high-dimensional inverse covariance (precision) matrix, which has been applied for portfolio allocation problem. The assumption made by these is sparsity of the precision matrix. However, when stock returns driven common factors, such does not hold. We address this limitation and develop framework, Factor Lasso (FGL), integrates graphica...

We derive a closed-form optimal dynamic portfolio policy when trading is costly and security returns are predictable by signals with different mean-reversion speeds. The optimal strategy is characterized by two principles: 1) aim in front of the target and 2) trade partially towards the current aim. Specifically, the optimal updated portfolio is a linear combination of the existing portfolio an...

We consider optimal portfolio selection problems in a possibilistic setting. Using the possibilistic framework, we can integrate more efficiently the experts’ knowledge and the investors’ subjective opinions into a portfolio selection model. In 2002 Carlsson, Fullér and Majlender considered portfolio selection problems under trapezoidal possibility distributions and presented an algorithm of co...