# With artificial intelligence to the optimal portfolio

The theory “Modern Portfolio Theory” was already published by Harry Markowitz in 1952 and provided the basis for the optimization of portfolios – 66 years ago. Today, thanks to enormous computing power and artificial intelligence, much more robust optimization methods can be used. iQ-FOXX uses artificial intelligence based on the theory of evolution. The results are very satisfactory and perfectly meet the given parameters.

In this article we present the results of the optimization process developed by iQ-FOXX. iQ-FOXX uses optimization to determine the most efficient portfolio weighting based on market indices. In a second step, the market indices are replaced by iQ-FOXX smart BETA indices. The result are efficient iQ-FOXX portfolios.

This is how genetic optimisation works

A genetic algorithm is an iterative method for finding the optimal solution. The optimum is defined by the fitness function. iQ-FOXX follows the approach of defining the maximization of the return for a given risk (target volatility) as a fitness function for the optimal combination of a broad range of market indices. The given assets in the form of market indices form the basis, the so-called “population”. The algorithm leads to a competitive phenomenon where the best candidates of the population prevail. As in evolution theory, it is not the strongest or the fastest that win, but the fittest candidates, i.e. the “most efficient” ones.  The fitness of each asset is calculated at each step and backed up by probabilities. At each iteration (generation) a new population is calculated and the one with the highest probability of survival, i.e. the most efficient solution, is selected. The new population is designed by applying genetic operators. As soon as the target criteria are met, the algorithm stops and the genetic optimization delivers the optimal portfolio – the one with the ideal weighting.

Perfect target achievement through genetic optimization

The chart shows the result of the optimization model and confirms the exemplary functioning. The target of 15% volatility was met exactly, which is shown by the horizontal line “Risk Underlying”.

The iQ-FOXX optimization effect

Only by optimizing the individual portfolio components is the efficiency line shifted to the top left. The diagram clearly illustrates the optimization effect. The improvement in performance is the result of natural selection and forms an extremely robust basis for the iQ-FOXX portfolio palette.