Abstract
Aiming at the NP hard problem of portfolio optimization, an improved differential evolution algorithm is proposed. In this algorithm, the mutation operator and crossover operator are set up adaptively, and then according to the characteristics of the mutation itself, two kinds of mutation operators with global search ability and local search ability are improved .The improved algorithm can improve the convergence speed and ensure the precision of the algorithm. Through five stocks of the same type and 20 different types of stocks for empirical analysis, the results show that the proposed algorithm has a certain guiding role in solving the problem of portfolio optimization.
Highlights
With the rapid economic development, emerging assets constantly emerging financial markets, more and more families, companies keen on capital investment, the inherent risks of financial markets and the resulting revenue has always been one of the focuses of the investors
In order to find the optimal investment scheme, we need to make a trade-off between costs and benefits
Once the model is put forward, which is widely applied to portfolio selection and asset allocation
Summary
With the rapid economic development, emerging assets constantly emerging financial markets, more and more families, companies keen on capital investment, the inherent risks of financial markets and the resulting revenue has always been one of the focuses of the investors. In actual operation process, due to the uncertainty of market factors, the risk and return of investment are often difficult to predict. It is proved that no matter what kind of model of portfolio optimization problem is a NP hard problem, with the continuous expansion of market investment, the traditional method can not get satisfactory results. Some scholars have applied the algorithm to portfolio optimization problems, such as the literatures [8,9,10,11,12]. These methods are based on actual situation to some improvement after solving the model. The empirical analyses show that the proposed algorithm is effective
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