Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies.

ABSTRACT: Stock trend prediction is a challenging task due to the market's noise, and machine learning techniques have recently been successful in coping with this challenge. In this research, we create a novel framework for stock prediction, Dynamic Advisor-Based Ensemble (dynABE). dynABE explores domain-specific areas based on the companies of interest, diversifies the feature set by creating different "advisors" that each handles a different area, follows an effective model ensemble procedure for each advisor, and combines the advisors together in a second-level ensemble through an online update strategy we developed. dynABE is able to adapt to price pattern changes of the market during the active trading period robustly, without needing to retrain the entire model. We test dynABE on three cobalt

SUBMITTER: Dong Z 

PROVIDER: S-EPMC6386270 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature