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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Exploring the Use of Meta-labeling in Financial Markets |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2022 |
| Abstract Text | Marcos Lopez de Prado introduced meta-labeling, a unique machine-learning technique for improving algorithmic trading tactics, in 2018. Meta-labeling is a term that refers to the process of assigning a binary label to past trades of a trading system based on their outcome (win or loss), constructing a set of time series features that are temporally aligned with the labels, and fitting a machine learning classification model to the features and labels. The learned classifier is then used to evaluate the probability of profitability for each new, unopened trade. Before the trade is opened, the position size for each new transaction is computed using the corresponding probability estimate. The objective is to increase the position size of trades with a high projected probability of profitability. This thesis aims to determine whether or not meta-labeling improves trading performance by adjusting the bet size for each trade based on the probability of profitability predicted by secondary meta-labeled machine learning layers when applied to real-world trading systems and to draw critical conclusions about the concept’s practical implementation. Three different meta-labeled machine learning layers are built on top of three existing investment expertise, which could be fundamental or systematically driven. As a result, the three systems’ performance is enhanced. The findings indicate that meta-labeling really improves trading system performance and thus should be considered by all traders. |
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