Before revision:  Specifically, dynABE first utilizes domain knowledge to group the features into different "advisors , " each advisor dealing with a particular economic sector.Then through ensembles of weak classifiers, each advisorproduces a prediction result, and all the advisors are combined again in a biased online update fashion to dynamically make the final prediction. Based on a misclassification error of 32\% for Jinchuan Group's stock (HKG: 2362), we further test a simple stock trading strategy, which leads to a back-tested return of 296\%, or an excess return of 130\% within one year .
After revision:  Specifically, dynABE first utilizes domain knowledge to group the features into different "advisors , " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy. We test dynABE on three cobalt-related companies, and it achieves the best-case misclassification error of 32\% for Jinchuan Group's stock (HKG: 2362), we further test a simple stock trading strategy, which leads to a back-tested return of 296\%, or an excess return of 130\% within one year .