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Optimizing Investment Strategies based on Companies Earnings using Genetic Algorithms

Neves, R. ; Horta, N.

Optimizing Investment Strategies based on Companies Earnings using Genetic Algorithms , Proc Genetic and Evolutionary Computation Conf. - GECCO, Amsterdam, Netherlands, Vol. 0, pp. 1 - 3, July, 2013.

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Abstract
This work proposes an investment strategy using Genetic Algorithms applied to the stock market. In order to build a portfolio of promising stocks we look at fundamental analysis by using indicators such as earnings volatility and growth, Price-to-Earnings ratio and Price/Earnings to Growth ratio. Additionally technical indicators such as moving average crossovers and Relative Strength Index are used to adapt the portfolio to the market’s trends. The proposed solution was applied to the S&P500 Index during the period from 2006 to 2011. In order to evolve a robust strategy these are evaluated according to the average return on investment, Drawdown and Sharpe ratio. The results obtained are promising with the solution outperforming the market with a considerable lower level of risk.