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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Pattern and Signal Detection using Machine Learning for Algorithmic Trading |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 88 |
| Date | 2023 |
| Abstract Text | Predicting equity returns is a complex task in finance. This paper examines the volume profile as a predictive tool using machine learning techniques. We process and summarize the volume profile into features, using an XGBoost classifier to forecast stock return direction. Our approach is validated across two equity sets, demonstrating its capability to identify high-return periods. Based on the probability estimates, trading strategies are created and shown to be able to outperform the benchmark on a risk-adjusted and total return basis. Overall, the results indicate the predictive potential of the volume profile leveraged by the model. |
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