Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Introduction to genetic programming
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Forecasting S&P 500 stock index futures with a hybrid AI system
Decision Support Systems
Genetic Programming Prediction of Stock Prices
Computational Economics
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
Generating trading rules on the stock markets with genetic programming
Computers and Operations Research
Expert Systems with Applications: An International Journal
The trading on the mutual funds by gene expression programming with Sortino ratio
Applied Soft Computing
Hi-index | 12.05 |
This paper proposes a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant multiplier in CPPI is generally regarded as the risk multiplier. Since the market changes constantly, we think that the risk multiplier should change according to market conditions. This research identifies risk variables relating to market conditions. These risk variables are used to build the equation tree for the risk multiplier by genetic programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy. In addition, principal component analysis of the risk variables in equation trees indicates that among all the risk variables, risk-free interest rate influences the risk multiplier most.