Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500

Christopher Krauss, Xuan Anh Do, Nicolas Huck · 2017 · European Journal of Operational Research

IntermediateLFTEmpirical

Daily cross-sectional ML on lagged returns: strong early returns that fade in recent years. An honest look at alpha decay.

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At a glance

Level: Intermediate: Comfortable with statistics, regressions and basic probability models
Horizon: LFT
Data needed: Daily bars Set what data you have
Themes: Stat arb Machine learning
Assets: Equities
Market: US

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@article{krauss2017deep,
  author = {Krauss, Christopher and Do, Xuan Anh and Huck, Nicolas},
  title = {{Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S\&P 500}},
  journal = {European Journal of Operational Research},
  year = {2017},
  doi = {10.1016/j.ejor.2016.10.031}
}

Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2016.10.031