Design and Optimization of Risk-Return Balance of Technology and Financial Investment Portfolio Using Genetic Algorithms

Traditional portfolio design methods can not fully consider the nonlinear relationship in the financial market, incomplete information, can not deal with the dynamic environment, can not rely on subjective judgment. A method based on genetic algorithm is presented. It seeks the optimal solution by choosing, crossover, mutation and so on. A technology-finance-asset portfolio model based on genetic algorithm is studied. The goal of this article is to propose a more efficient technology financing tool, so that it can achieve a balance between risk and return. The study found that at 5000 GB of data capacity, the risk-benefit equilibrium design using genetic algorithms had a relatively low sensitivity with a variation of only nine points over the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$100 \text{Gb}(97)$</tex>. This method has better results in handling the nonlinear relationship, dynamic environment and reducing the dependence on human factors, and provides a more efficient way for the design of technical financial instruments.