This work presents a new approach to maximize financial market investment returns. It incorporates two \acp{EA} combined with fundamental and technical investment strategies.
The first \ac{EA} (simple) maintains its evolutionary parameters static during evolution. The second (self-adaptive) introduces the variation operators’ parameters’ values in the representation for them to evolve. The \ac{EA} is responsible for optimizing the weight that financial ratios from the F-Score have on composing static/dynamic portfolios. Furthermore, it is also responsible for defining the importance that selected technical indicators have on revealing the best timing for market positions placement.
A fundamental and a technical case study was created employing companies from the \ac{SP500}. These were trained/tested in a sliding window scheme between 01/2012 and 12/2018.
Results showed that both case studies surpassed the \ac{SP500} returns, performing their best results using a self-adaptive \ac{EA} combined with a static portfolio and a sliding window of 2 years of train/test.
The technical case study showed better results in bear markets since it predicted some market declines. Its best subtest achieved returns on average 2.2x and in its best 3.5x higher than the benchmark. Its Sharpe Ratio achieved, on average, 4.9x and in its best 9x higher results than the benchmark.
Better performance in the bull market is displayed in the fundamental case study where it achieves high market prices. Its best subtest achieved returns on average 2.4x and in its best 3.2x higher than the benchmark. Its Sharpe Ratio achieved, on average, 4.4x and in its best 6.5x higher results than the benchmark.