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Alliances and Return Predictability

Author

Listed:
  • Cao, Jie
  • Chordia, Tarun
  • Lin, Chen

Abstract

Building on the growing literature on interfirm links and limited attention, we find evidence of return predictability across alliance partners. A long–short portfolio sorted on lagged returns of strategic alliance partners provides a return of 89 basis points per month that is robust to a number of specifications. Investor inattention and limits to arbitrage may be the source of the underreaction of a firm’s returns to that of its partners.

Suggested Citation

  • Cao, Jie & Chordia, Tarun & Lin, Chen, 2016. "Alliances and Return Predictability," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 51(5), pages 1689-1717, October.
    • Handle: RePEc:cup:jfinqa:v:51:y:2016:i:05:p:1689-1717_00
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    Citations

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    Cited by:

    1. Shi, Jinyan & Yu, Conghui & Liu, Xiangkun & Li, Yanxi, 2020. "Predicting firm stock returns with customer stock returns: Moderating effects of customer characteristics," Research in International Business and Finance, Elsevier, vol. 54(C).
    2. Jianping Qi & Ninon K. Sutton & Qiancheng Zheng, 2020. "The value of innovation and the spillover effect on alliance partners," Review of Quantitative Finance and Accounting, Springer, vol. 55(4), pages 1427-1457, November.
    3. Lee, Charles M.C. & Sun, Stephen Teng & Wang, Rongfei & Zhang, Ran, 2019. "Technological links and predictable returns," Journal of Financial Economics, Elsevier, vol. 132(3), pages 76-96.
    4. Chang, Ran & Gonzalez, Angelica & Sarkissian, Sergei & Tu, Jun, 2022. "Internal capital markets and predictability in complex ownership firms," Journal of Corporate Finance, Elsevier, vol. 74(C).
    5. Chenchen Li & Rui Li & Xundi Diao & Chongfeng Wu, 2020. "Market segmentation and supply‐chain predictability: evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(2), pages 1531-1562, June.
    6. Qi Xu & Yang Ye, 2023. "Commodity network and predictable returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(10), pages 1423-1449, October.
    7. Box, Travis, 2018. "Qualitative similarity and stock price comovement," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 49-69.
    8. Xin Wang & Haofei Zhang, 2023. "The cross‐predictability of industry returns in international financial markets," International Review of Finance, International Review of Finance Ltd., vol. 23(4), pages 859-885, December.
    9. Zareei, Abalfazl, 2021. "Cross-momentum: Tracking idiosyncratic shocks," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 177-199.
    10. Chen, Long & Zhang, Gaiyan & Zhang, Weina, 2016. "Return predictability in the corporate bond market along the supply chain," Journal of Financial Markets, Elsevier, vol. 29(C), pages 66-86.
    11. Chava, Sudheer & Hsu, Alex & Zeng, Linghang, 2020. "Does history repeat itself? Business cycle and industry returns," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 201-218.
    12. Jianping Qi & Ninon K. Sutton & Qiancheng Zheng, 0. "The value of innovation and the spillover effect on alliance partners," Review of Quantitative Finance and Accounting, Springer, vol. 0, pages 1-31.
    13. Cao, Jie & Han, Bing, 2016. "Idiosyncratic risk, costly arbitrage, and the cross-section of stock returns," Journal of Banking & Finance, Elsevier, vol. 73(C), pages 1-15.
    14. Huang, Shiyang & Lin, Tse-Chun & Xiang, Hong, 2021. "Psychological barrier and cross-firm return predictability," Journal of Financial Economics, Elsevier, vol. 142(1), pages 338-356.

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