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New Methods for the Cross-Section of Returns

Author

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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. Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2023. "The commodity risk premium and neural networks," Journal of Empirical Finance, Elsevier, vol. 74(C).
    2. 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).
    3. 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).
    4. Karim, Sitara & Shafiullah, Muhammad & Naeem, Muhammad Abubakr, 2024. "When one domino falls, others follow: A machine learning analysis of extreme risk spillovers in developed stock markets," International Review of Financial Analysis, Elsevier, vol. 93(C).
    5. 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.
    6. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
    7. Jiang, Fuwei & Ma, Tian & Zhu, Feifei, 2024. "Fundamental characteristics, machine learning, and stock price crash risk," Journal of Financial Markets, Elsevier, vol. 69(C).
    8. 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.
    9. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    10. Krivenko, Pavel, 2023. "Asset prices in a labor search model with confidence shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    11. Byun, Suk-Joon & Cho, Sangheum & Kim, Da-Hea, 2024. "Can a machine learn from behavioral biases? Evidence from stock return predictability of deep learning models," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).

    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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