Dutch asset manager turns to decision trees for currency predictions

APG has improved prediction accuracy for G10 currency movements after adopting decision tree-based machine learning.

Forest

Decision trees are among the more popular applications of machine learning in the capital markets. Uses include finding patterns in request-for-quote (RFQ) datasets and predicting stock prices. Decision trees employ a tree-like model to map out decisions, or “nodes,” and the probabilities of consequences branching out from these nodes, to work through a database and calculate outputs.

Amsterdam-based pension fund APG Asset Management, with $690 billion in assets under management, is using

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