Machine learning and macro trading strategies

February 1, 2020

Machine learning can improve macro trading strategies, mainly because it makes them more flexible and adaptable, and generalizes knowledge better than fixed rules or trial-and-error approaches. Within the constraints of pre-set hyperparameters machine learning is continuously and autonomously learning from new data, thereby challenging or refining prevalent beliefs. Machine learning and expert domain knowledge are not rivals but complementary. Domain expertise is critical for the quality of featurization, the choice of hyperparameters, the selection of training and test samples, and the choice of regularization methods. Modern macro strategists may not need to make predictions themselves but could provide great value by helping machine learning algorithms to find the best prediction functions.

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The predictive superiority of ensemble methods for CDS spreads

January 11, 2020

Through ‘R’ and ‘Python’ one can apply a wide range of methods for predicting financial market variables. Key concepts include penalized regression, such as Ridge and LASSO, support vector regression, neural networks, standard regression trees, bagging, random forest, and gradient boosting. The latter three are ensemble methods, i.e. machine learning techniques that combine several base models in order to produce one optimal prediction. According to a new paper, these ensemble methods scored a decisive win in the nowcasting and out-of-sample prediction of credit spreads. One apparent reason is the importance of non-linear relations in times of high volatility.

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