Aiding Long-Term Investment Decisions with XGBoost Machine Learning Model

TL;DR

This paper presents a method using XGBoost machine learning to identify long-term stock market trends, aiding investors in making informed buy or sell decisions based on trend detection.

Contribution

The study introduces a two-stage modeling approach with feature engineering and addresses dataset challenges for long-term trend prediction using XGBoost.

Findings

Effective detection of trend endpoints and directions.

Handling imbalanced datasets and label contradictions.

Potential for investment strategy development.

Abstract

The ability to identify stock market trends has obvious advantages for investors. Buying stock on an upward trend (as well as selling it in case of downward movement) results in profit. Accordingly, the start and end-points of the trend are the optimal points for entering and leaving the market. The research concentrates on recognizing stock market long-term upward and downward trends. The key results are obtained with the use of gradient boosting algorithms, XGBoost in particular. The raw data is represented by time series with basic stock market quotes with periods labelled by experts as Trend or Flat. The features are then obtained via various data transformations, aiming to catch implicit factors resulting in a change of stock direction. Modelling is done in two stages: stage one aims to detect endpoints of tendencies (i.e. sliding windows), stage two recognizes the tendency itself inside the window. The research addresses such issues as imbalanced datasets and contradicting labels, as well as the need for specific quality metrics to keep up with practical applicability. The model can be used to design an investment strategy though further research in feature engineering and fine calibration is required.This paper is the full text of the research, presented at the 20th International Conference on Artificial Intelligence and Soft Computing Web System (ICAISC 2021)