Empirical Asset Pricing via Machine Learning

Shihao Gu, Bryan Kelly, Dacheng Xiu · 2020 · The Review of Financial Studies

IntermediateLFTEmpiricalFree to read

Compares linear, tree and neural models for predicting stock returns. Trees and neural nets win, driven mostly by momentum, liquidity and volatility signals.

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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: Machine learning Factors & momentum
Assets: Equities
Market: US

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Cite

@article{gu2020empirical,
  author = {Gu, Shihao and Kelly, Bryan and Xiu, Dacheng},
  title = {{Empirical Asset Pricing via Machine Learning}},
  journal = {The Review of Financial Studies},
  year = {2020},
  doi = {10.1093/rfs/hhaa009}
}

Gu, S., Kelly, B., & Xiu, D. (2020). Empirical Asset Pricing via Machine Learning. The Review of Financial Studies. https://doi.org/10.1093/rfs/hhaa009