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New Orders and Asset Prices

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  • Christopher S. Jones
  • Selale Tuzel

Abstract

We investigate the asset pricing and macroeconomic implications of the ratio of new orders (NO) to shipments (S) of durable goods. NO/S measures investment commitments by firms, and high values of NO/S are associated with a business cycle peak. We find that NO/S proxies for a short-horizon component of risk premia not identified in prior work. Higher levels of NO/S forecast lower excess returns on equities and many types of bonds, at horizons from one month to one year. These effects are generally robust to the inclusion of common return predictors and are significant on an out-of-sample basis as well. We also address the term structure of risk premia by constructing a similar ratio to measure longer-term investment commitments, which predicts returns primarily at longer horizons. The Author 2012. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Suggested Citation

  • Christopher S. Jones & Selale Tuzel, 2013. "New Orders and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 26(1), pages 115-157.
    • Handle: RePEc:oup:rfinst:v:26:y:2013:i:1:p:115-157
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    File URL: http://hdl.handle.net/10.1093/rfs/hhs098
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    Citations

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    Cited by:

    1. Christoph Görtz & Christopher Gunn & Thomas Lubik, "undated". "What Drives Inventory Accumulation? News on Rates of Return and Marginal Costs," Carleton Economic Papers 19-09, Carleton University, Department of Economics.
    2. Li, Jun & Wang, Huijun & Yu, Jianfeng, 2018. "Aggregate Expected Investment Growth and Stock Market Returns," ADBI Working Papers 808, Asian Development Bank Institute.
    3. Christoph Görtz & Christopher Gunn & Thomas Lubik, 2018. "Taking Stock of TFP News Shocks: The Inventory Comovement Puzzle," Carleton Economic Papers 18-05, Carleton University, Department of Economics, revised 14 Jul 2018.
    4. Christoph Gortz & Christopher Gunn & Thomas A. Lubik, 2024. "The Changing Nature of Technology Shocks," Working Paper 99119, Federal Reserve Bank of Richmond.
    5. Scott Cederburg & Travis L Johnson & Michael S O’Doherty, 2023. "On the Economic Significance of Stock Return Predictability," Review of Finance, European Finance Association, vol. 27(2), pages 619-657.
    6. Kothari, Pratik & O’Doherty, Michael S., 2023. "Job postings and aggregate stock returns," Journal of Financial Markets, Elsevier, vol. 64(C).
    7. Jing-Zhi Huang & Zhan Shi, 2023. "Machine-Learning-Based Return Predictors and the Spanning Controversy in Macro-Finance," Management Science, INFORMS, vol. 69(3), pages 1780-1804, March.
    8. Shi, Zhan, 2019. "Time-varying ambiguity, credit spreads, and the levered equity premium," Journal of Financial Economics, Elsevier, vol. 134(3), pages 617-646.
    9. Christoph Gortz & Christopher Gunn & Thomas Lubik, 2022. "Split Personalities: The Changing Nature of Technology Shocks," Carleton Economic Papers 22-06, Carleton University, Department of Economics.
    10. Yuan Liao & Xinjie Ma & Andreas Neuhierl & Zhentao Shi, 2023. "Economic Forecasts Using Many Noises," Papers 2312.05593, arXiv.org, revised Dec 2023.
    11. Favilukis, Jack & Lin, Xiaoji, 2016. "Does wage rigidity make firms riskier? Evidence from long-horizon return predictability," Journal of Monetary Economics, Elsevier, vol. 78(C), pages 80-95.
    12. Pyun, Sungjune, 2019. "Variance risk in aggregate stock returns and time-varying return predictability," Journal of Financial Economics, Elsevier, vol. 132(1), pages 150-174.
    13. Li, Jun & Wang, Huijun & Yu, Jianfeng, 2021. "Aggregate expected investment growth and stock market returns," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 618-638.

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