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

TL;DR

This paper introduces dynABE, a dynamic ensemble framework for stock trend prediction that adapts to market changes without retraining, demonstrating superior performance on cobalt-related stocks.

Contribution

The paper presents a novel dynamic ensemble method, dynABE, that effectively adapts to market shifts and outperforms traditional models in stock trend prediction.

Findings

Achieved 31.12% misclassification error on test data.

Generated an annualized return of 359.55%.

Outperformed baseline models like SVM, neural network, and random forest.

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-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.