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Empirical Cross-Sectional Asset Pricing

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  • Stefan Nagel

    (Ross School of Business and Department of Economics, University of Michigan, Ann Arbor, Michigan 48109
    National Bureau of Economic Research, Cambridge, Massachusetts 02138
    Centre for Economic Policy Research, London, EC1V 3PZ, United Kingdom)

Abstract

I review recent research efforts in the area of empirical cross-sectional asset pricing. I start by summarizing the evidence on cross-sectional return predictability and the failure of standard (consumption) capital asset pricing models (CAPMs) and their conditional versions to explain these predictability patterns. Part of the recent literature focuses on ad hoc factor models, which summarize the cross section of expected returns in parsimonious form, or on production-based approaches, which suggest links between firm characteristics and expected returns. Without imposing restrictions on investor preferences and beliefs, neither one of these two approaches can answer the question of why investors price assets the way they do. Within the rational expectations paradigm, recent research that imposes such restrictions has focused on the intertemporal CAPM (ICAPM), long-run risks models, as well as frictions and liquidity risk. Approaches based on investor sentiment have focused on the development of empirical proxies for sentiment and for the limits to arbitrage that allow sentiment to affect prices. Empirical work that considers learning and adaptation of investors has worked with out-of-sample tests of cross-sectional predictability.

Suggested Citation

  • Stefan Nagel, 2013. "Empirical Cross-Sectional Asset Pricing," Annual Review of Financial Economics, Annual Reviews, vol. 5(1), pages 167-199, November.
  • Handle: RePEc:anr:refeco:v:5:y:2013:p:167-199
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    Cited by:

    1. Xu, Zhiwei & Wang, Xuefei & Zhang, Teng, 2024. "The international natural gas price and its cross-sectional pricing implication: Evidence from Chinese stock market," Energy, Elsevier, vol. 313(C).
    2. DeMiguel, Victor & Martin-Utrera, Alberto & Nogales, Francisco J. & Uppal, Raman, 2017. "A Portfolio Perspective on the Multitude of Firm Characteristics," CEPR Discussion Papers 12417, C.E.P.R. Discussion Papers.
    3. Elias Albagli & Christian Hellwig & Aleh Tsyvinski, 2023. "Imperfect Financial Markets and Investment Inefficiencies," American Economic Review, American Economic Association, vol. 113(9), pages 2323-2354, September.
    4. Bianconi, Marcelo & Esposito, Federico & Sammon, Marco, 2021. "Trade policy uncertainty and stock returns," Journal of International Money and Finance, Elsevier, vol. 119(C).
    5. Joel M. Vanden, 2021. "Equilibrium asset pricing and the cross section of expected returns," Annals of Finance, Springer, vol. 17(2), pages 153-186, June.
    6. Jacobs, Heiko, 2015. "What explains the dynamics of 100 anomalies?," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 65-85.
    7. Chen, Zilin & Chu, Liya & Liang, Dawei & Tu, Jun, 2022. "Far away from home: Investors’ underreaction to geographically dispersed information," Journal of Economic Dynamics and Control, Elsevier, vol. 136(C).
    8. Park, Dojoon & Kang, Yong Joo & Eom, Young Ho, 2024. "Asset pricing tests for pandemic risk," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1314-1334.
    9. Delao, Ricardo & Han, Xiao & Myers, Sean, 2025. "The return of return dominance: Decomposing the cross-section of prices," Journal of Financial Economics, Elsevier, vol. 169(C).
    10. Hanauer, Matthias X. & Lesnevski, Pavel & Smajlbegovic, Esad, 2023. "Surprise in short interest," Journal of Financial Markets, Elsevier, vol. 65(C).
    11. Xu, Zhiwei & Gou, Xinyi & Zhang, Teng, 2025. "Have the Chinese crude oil futures prices made a progress towards becoming the regional oil pricing benchmark? Empirical analysis from the asset pricing perspective," Energy Economics, Elsevier, vol. 145(C).
    12. Elias Albagli & Christian Hellwig & Aleh Tsyvinski, 2017. "Imperfect Financial Markets and Shareholder Incentives in Partial and General Equilibrium," NBER Working Papers 23419, National Bureau of Economic Research, Inc.
    13. Matias D. Cattaneo & Richard K. Crump & Weining Wang, 2022. "Beta-Sorted Portfolios," Papers 2208.10974, arXiv.org, revised Nov 2024.
    14. Morana, Claudio, 2014. "Insights on the global macro-finance interface: Structural sources of risk factor fluctuations and the cross-section of expected stock returns," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 64-79.
    15. Jiho Park, 2024. "Heterogeneous Beliefs Model of Stock Market Predictability," Papers 2406.08448, arXiv.org.
    16. Minshuo Chen & Renyuan Xu & Yumin Xu & Ruixun Zhang, 2025. "Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure," Papers 2504.06566, arXiv.org, revised Jan 2026.
    17. Fletcher, Jonathan, 2018. "Betas V characteristics: Do stock characteristics enhance the investment opportunity set in U.K. stock returns?," The North American Journal of Economics and Finance, Elsevier, vol. 46(C), pages 114-129.
    18. Hongyi Liu, 2025. "Deep Learning for Conditional Asset Pricing Models," Papers 2509.04812, arXiv.org.
    19. Adam Zaremba, 2019. "The Cross Section of Country Equity Returns: A Review of Empirical Literature," JRFM, MDPI, vol. 12(4), pages 1-26, October.
    20. Byrne, Joseph P & Ibrahim, Boulis Maher & Sakemoto, Ryuta, 2017. "The Time-Varying Risk Price of Currency Carry Trades," MPRA Paper 80788, University Library of Munich, Germany.
    21. Hahn, Jaehoon & Yoon, Heebin, 2016. "Determinants of the cross-sectional stock returns in Korea: evaluating recent empirical evidence," Pacific-Basin Finance Journal, Elsevier, vol. 38(C), pages 88-106.
    22. Maio, Paulo & Philip, Dennis, 2018. "Economic activity and momentum profits: Further evidence," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 466-482.

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    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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