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New Methods for the Cross-Section of Returns

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  • Stijn Van Nieuwerburgh

Abstract

The cross-section and time series of stock returns contains a wealth of information about the stochastic discount factor (SDF), the object that links cash flows to prices. A large empirical literature has uncovered many candidate factors—many more than seem plausible—to summarize the SDF. This special volume of the Review of Financial Studies presents recent advances in extracting information from both the cross-section and the time series, in dealing with issues of replication and false discoveries, and in applying innovative machine-learning techniques to identify the most relevant asset pricing factors. Our editorial summarizes what we learn and offers a few suggestions to guide future work in this exciting new era of big data and empirical asset pricing.

Suggested Citation

  • Stijn Van Nieuwerburgh, 2020. "New Methods for the Cross-Section of Returns," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 1879-1890.
    • Handle: RePEc:oup:rfinst:v:33:y:2020:i:5:p:1879-1890.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhaa019
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    Citations

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    Cited by:

    1. Karolyi, G. Andrew & Wu, Ying, 2022. "Understanding the pricing of currency risk in global equity markets," Journal of Multinational Financial Management, Elsevier, vol. 63(C).
    2. Ma, Tian & Leong, Wen Jun & Jiang, Fuwei, 2023. "A latent factor model for the Chinese stock market," International Review of Financial Analysis, Elsevier, vol. 87(C).
    3. Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-learning the skill of mutual fund managers," Journal of Financial Economics, Elsevier, vol. 150(1), pages 94-138.
    4. Krivenko, Pavel, 2023. "Asset prices in a labor search model with confidence shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    5. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
    6. Doron Avramov & Si Cheng & Lior Metzker, 2023. "Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability," Management Science, INFORMS, vol. 69(5), pages 2587-2619, May.

    More about this item

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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