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Cited by 346

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Misvaluing Innovation

Open access
Cohen, Lauren, Diether, Karl, Malloy, Christopher·2013·Review of Financial Studies
DOI 10.1093/rfs/hhs183
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Abstract

We demonstrate that a firm’s ability to innovate is predictable, persistent, and relatively simple to compute, and yet the stock market ignores the implications of past successes when valuing future innovation. We show that two firms that invest the exact same in research and development (R&D) can have quite divergent, but predictably divergent, future paths. Our approach is based on the simple premise that while future outcomes associated with R&D investment are uncertain, the past track records of firms may give insight into their potential for future success. We show that a long-short portfolio strategy that takes advantage of the information in past track records earns abnormal returns of roughly 11 percent per year. Importantly, these past track records also predict divergent future real outcomes in patents, patent citations, and new product innovations.

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Crossref · 10.1093/rfs/hhs183

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Citations

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Metadata

Authors
Cohen, L., Diether, K., & Malloy, C.
Venue
Review of Financial Studies
Publisher
Oxford University Press (OUP)
Published
2013-01-21
Volume
26
Issue
3
Pages
635-666
ISSN
  • 0893-9454 (ISSN-L)
  • 1465-7368
DOI
10.1093/rfs/hhs183
OpenAlex
W4244304946
Cited by
346
References
45
FWCI
26.18
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Misvaluing Innovation. https://app.cochat.ai/papers/10.1093/rfs/hhs183
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Cohen, L., Diether, K., & Malloy, C. (2013). Misvaluing Innovation. Review of Financial Studies. https://doi.org/10.1093/rfs/hhs183

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.