Optimal algorithms and lower partial moment: ex post results

Portofolio management in the finance literature has typically used optimization algorithms to determine security allocations within a portfolio in order to obtain the best trade-off between risk and return. These algorithms, despite some improvements, are restrictive in terms of an investor's risk aversion (utility function). Since individual investors have different levels of risk aversion, this paper proposes two portfolio-optimization algorithms that can be tailored to the specific level of risk aversion of the individual investor and performs ex postevaluation tests of the algorithm performance.