A hybrid stock trading system using genetic network programming and mean conditional value-at-risk
We combine the advantages of evolutionary algorithms and statistical model. The system has generated effective trading strategies for different risk-attitudes. We propose a method to forecast the stock prices in two opposite environments. Model based on maximum Sharpe Ratio portfolio performs the best in bull market. Model based on the global minimum risk portfolio performs the best in bear market.
conditional value-at-risk evolutionary computations investment analysis risk management portfolio optimization