Mean-Risk Analysis with Risk Associated with Below-Target Returns

TL;DR: An efficient learning-guided hybrid multi-objective evolutionary algorithm (MODE-GL) is proposed to solve mean-VaR portfolio optimization problems with real-world constraints such as cardinality, quantity, pre-assignment, round-lot and class constraints and outperforms two existing techniques for this important class of portfolio investment problems.

TL;DR: There is little difference between the optimal M-V and M-LPM portfolios under nonnormal asset return distributions when asset returns are nearly normally distributed, but when returns are nonnormal with large left tails, significant differences are recorded.

TL;DR: This article presented an axiomatization of subjective risk judgments that leads to a representation of risk in terms of seven free parameters, which is shown to have considerable predictive ability for risk judgments made by 10 subjects.

TL;DR: The monitoring of information acquisition behavior, along with other process tracing measures such as response times, was used to examine how individuals process information about gambles into a decision.

TL;DR: In this article, the problem of selecting optimal security portfolios by risk-averse investors who have the alternative of investing in risk-free securities with a positive return or borrowing at the same rate of interest and who can sell short if they wish is discussed.

TL;DR: In this article, a measure of risk aversion in the small, the risk premium or insurance premium for an arbitrary risk, and a natural concept of decreasing risk aversion are discussed and related to one another.

TL;DR: Preliminary evidence suggests that the relatively few parameters used by the model can lead to very nearly the same results obtained with much larger sets of relationships among securities, as well as the possibility of low-cost analysis.