Corpus ID: 235446989

Time Series Momentum Predictability via Dynamic Binary Classification

@inproceedings{Levy2021TimeSM,
  title={Time Series Momentum Predictability via Dynamic Binary Classification},
  author={B. Levy and H. Lopes},
  year={2021}
}
Time series momentum strategies are widely applied in the quantitative financial industry and its academic research has grown rapidly since the work of Moskowitz, Ooi, and Pedersen (2012). However, trading signals are usually obtained via simple observation of past return measurements. In this article we study the benefits of incorporating dynamic econometric models to sequentially learn the time-varying importance of different look-back periods for individual assets. By the use of a dynamic… Expand

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