Correlated stock identification in pairs trading using extreme gradient boosting algorithm
| dc.contributor.author | Muhia, C. N. | |
| dc.date.accessioned | 2025-07-07T13:07:37Z | |
| dc.date.available | 2025-07-07T13:07:37Z | |
| dc.date.issued | 2024 | |
| dc.description | Full - text thesis | |
| dc.description.abstract | Pairs trading is a well-known market-neutral trading strategy that aims to exploit market inefficiencies by identifying and trading pairs of highly correlated stocks. This research addresses the pressing problem of accurately identifying correlated stock pairs for pairs trading strategies, recognizing the potential for reducing risk and generating profits in financial markets. While traditional statistical and deep learning methods have provided valuable insights, there exists a notable research gap in assessing the effectiveness of advanced machine learning algorithms like Extreme Gradient Boosting (XGBoost) in this context. To bridge this gap, the study meticulously compares the performance of the XGBoost algorithm with conventional techniques through quantitative analysis. Leveraging historical stock price data and machine learning methodologies, the research explores the intricacies of stock pairing accuracy and profitability. The findings reveal that the tuned XGBoost model demonstrates superior accuracy, precision, and recall in identifying profitable stock pairs, outperforming traditional statistical methods and other machine learning algorithms. Specifically, the XGBoost model achieved an accuracy of 95.50% and a precision of 95.34% in identifying profitable stock pairs. These results underscore the potential of XGBoost to enhance pairs trading strategies and optimize trading decisions in dynamic financial environments. However, while the XGBoost model showcases remarkable performance, it is not without limitations. Susceptibility to overfitting and reliance on input feature quality and quantity present challenges that need to be addressed. Nonetheless, the study provides valuable insights for investors and traders, suggesting avenues for optimizing trading strategies and maximizing profitability. Recommendations include further exploration of XGBoost's capabilities in diverse market conditions and the integration of additional data sources to enhance predictive accuracy. Moreover, the research highlights the need for continued investigation into other advanced machine learning algorithms and ensemble techniques to further improve stock pairing accuracy. Ultimately, this study contributes to advancing pairs trading strategies by providing empirical evidence of XGBoost's effectiveness, while also identifying avenues for future research and development in the field. Key Words: Pairs Trading, Correlated Stocks, Autoencoders, Self-Organizing Maps, Random Forest, Support Vector Machine, Trading Strategy, Sharpe Ratio, Maximum Drawdown, Cointegration, Backtesting, Machine Learning, XGBoost | |
| dc.identifier.uri | http://hdl.handle.net/11071/15717 | |
| dc.language.iso | en | |
| dc.publisher | Strathmore University | |
| dc.title | Correlated stock identification in pairs trading using extreme gradient boosting algorithm | |
| dc.type | Thesis |