Stock market prediction and portfolio composition using a hybrid approach combined with self-adaptive evolutionary algorithm

Abstract

This work presents a new approach to maximize financial market investment returns. It incorporates two Evolutionary Algorithms (EAs) combined with fundamental and technical investment strategies. The first 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 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. Results showed that both cases surpassed the S&P500 returns, performing their best results using a self-adaptive 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. The fundamental case study displayed best in the “bull market”, achieving 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.