Before revision:  We test dynABE on three cobalt-related companies, and it achieves the best-case misclassification error of 31.12\% and excess return of 477\% compared to the stock itself in a year and a half. In addition to presenting an effective stock prediction model with decent profitabilities, this research further analyzes dynABE to visualize how it works in practice, which also yields discoveries of its interesting behaviors when processing time-series data .
After revision:  We test dynABE on three cobalt-related companies, and it achieves the best-case misclassification error of 31.12\% and an annualized absolute return of 359.55\% with zero maximum drawdown. dynABE also consistently outperforms the baseline models of support vector machine, neural network, and random forest in all case studies .