Optimizing investment portfolios with a sequential ensemble of decision tree-based models and the FBI algorithm for efficient financial analysis

Abstract

This research presents a comprehensive, sequential ensemble framework meticulously crafted for optimizing investment portfolios, focusing on the construction industry. It employs decision tree-based and metaheuristic optimization algorithms to create an efficient stock-selection framework grounded in financial analysis. This approach offers a profitable investment strategy integrated with portfolio optimization while systematically lowering the portfolio investment threshold. In this framework, a web crawler was deployed to gather daily closing prices of stocks from the Taiwan Stock Exchange, along with monthly revenue data and financial statements. Subsequently, decision tree-based models were utilized to pinpoint the fundamental financial indicators with significant explanatory power over revenues. The stock selection conditions aligned with these indicators were optimized through a newly developed metaheuristic algorithm named the forensic-based investigation (FBI) optimizer. The optimal conditions were subsequently integrated with the equal-weighting scheme, mean-variance method, and hierarchical risk parity to identify the most effective investment portfolio strategies. Backtesting results showed that the proposed stock-portfolio investment strategy, optimized through machine learning and a metaheuristic algorithm, is well-suited for construction and all-stock categories. This study equips professional investment advisors or securities investment institutions with a decision-aid expert system for initial stock selection.