fireworks algorithm

Devaluation of assets in the future is one of the most important investment concerns that has led investors to choose the set of assets that have the lowest risk and highest return. The present study deals with the problem of stock portfolio optimization according to the Conditional Value at Risk based on the new and intelligent fireworks algorithm and compares it with genetic algorithm with the historical simulation method using MATLAB software. The parameters of meta-heuristic algorithms were adjusted by Taguchi method using MINITAB software. Not suspended, used. For reliability of the study, generalized Dickey-Fuller test and Phillips-Prone test were used. To evaluate the accuracy of the Conditional Value at Risk model, the kupiec proportion of failure test, Christoffersen independence test and Conditional coverage test are used.  A comparison was also made between the models by Lopez test. Findings showed that at %95 and %99  confidence levels, the conditional risk value model using the fireworks algorithm has a suitable and reliable validity for measuring market risk and optimizing the stock portfolio.

The nature of business and investment activities is such that earning a return requires risk tolerance. Choosing a stock portfolio is a difficult and difficult task that the investor sees in the face of the many and varied choices that she must choose as one of the best methods. The present study deals with the problem of stock portfolio optimization according to the Value at Risk based intelligent fireworks algorithm and compares it with Particle Swarm Optimization algorithm with the historical simulation method using MATLAB software. The parameters of meta-heuristic algorithms were adjusted by Taguchi method using MINITAB software. Not suspended, used. For reliability of the study, generalized Dickey-Fuller test and Phillips-Prone test were used. To evaluate the accuracy of the Conditional Value at Risk model, the kupiec proportion of failure test, Christoffersen independence test and Conditional coverage test are used.  A comparison was also made between the models by Lopez test. The execution time of the Particle Swarm Optimization was less than that of the fireworks algorithm at all three levels of confidence, but the convergence speed of the fireworks algorithm was faster than that of the Particle Swarm Optimization at all levels. Findings showed that the Value at Risk model using the fireworks algorithm, despite the longer execution time due to better convergence speed and higher rank of Lopez test has a more appropriate validity for stock portfolio optimization.