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Technological Links and Predictable Returns

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  • Lee, Charles M. C. Lee

    (Stanford University)

  • Sun, Stephen Teng

    (Peking University)

  • Wang, Rongfei

    (Peking University)

  • Zhang, Ran

    (Peking University)

Abstract

This paper finds evidence of return predictability across technology-linked firms. Employing a classic measure of technological closeness between firms, we show that the returns of technology-linked firms have strong predictive power for focal firms' returns. A long-short strategy based on this effect yields monthly alpha of 117 basis points. This effect is distinct from industry momentum, and is more pronounced for more innovative firms, firms with higher investor inattention, and firms with higher costs of arbitrage. We find a similar lead-lag relation between the earnings surprises, analyst revisions, and innovation-related activities (such as patent and citation counts) of technology-linked firms. Our results are broadly consistent with sluggish price adjustment to more nuanced technological news.

Suggested Citation

  • Lee, Charles M. C. Lee & Sun, Stephen Teng & Wang, Rongfei & Zhang, Ran, 2017. "Technological Links and Predictable Returns," Research Papers repec:ecl:stabus:3605, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:repec:ecl:stabus:3605
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    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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