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This paper studies how a machine learning algorithm can generate tactical allocation which outperforms returns for a pre-defined benchmark. We use three distinct and diverse data sets to implement the model which tries to forecast the next month’s a selected equity index price. The algorithm used to accomplish this task is Elastic Net. Once the predictions are generated from an out-of-sample subset, we elaborate a tactical portfolio allocation aiming to maximize the return of a different combination of classical allocation between bonds and equity, and a risk parity strategy. Finally, we evaluate those returns by comparing them to the benchmark.
