engleski

Effect of labeling algorithms on financial performance metrics

Machine learning models are increasingly common in predicting financial market movements. Unlike some other research areas, labels of financial time series are not unambiguously determined, which is why multiple labeling algorithms were proposed. The effect of a particular labeling algorithm on a trading strategy is often overlooked as most existing research uses only one type of labels to develop a machine learning model used for trading. However, different labeling algorithms lead to different generalization errors that may impede financial performance of a strategy. This paper examines the relationship between the classification performance of a model and the financial performance of a strategy based on the same model. The relationship is examined for two commonly used labeling algorithms: fixed-time horizon and triple-barrier method. Although the results for both labeling algorithms confirm a statistically significant correlation between classification and financial performance, the correlation coefficient itself has a low value.

financial time series ; stock prediction ; machine learning ; labeling algorithms

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