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Bank loan spread and private information: pending approval patents


This study examines a specific source of lenders’ ex ante information advantage, private information about borrowers’ forthcoming patents. We examine this setting to provide evidence of the impact of such private information on borrowers’ cost of debt. We find evidence consistent with lenders incorporating private information by charging borrowers with forthcoming patents a lower spread than borrowers that lack that private information. We document a negative association between loan spread and the citation count on forthcoming patents, consistent with borrowers providing lenders with detailed information regarding future expected cash flows from forthcoming patents and lenders responding through a reduction in interest costs for those borrowers. We also show that the reduction in loan spreads is related to the expected value of the forthcoming patent and is greater for borrowers with higher initial information uncertainty and default risk, and when the lead lender has greater loan concentration in the borrower’s industry. Our results suggest that forthcoming patents are a significant source of private information useful to borrowers and employed by lenders.

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Fig. 1


  1. 1.

    The “Appendix” R Patent Rules from the USPTO states, “If, on examination, it appears that the applicant is entitled to a patent under the law, a notice of allowance will be sent to the applicant at the correspondence address. … The sum specified in the notice of allowance may also include the publication fee, in which case the issue fee and publication fee … must both be paid within three months from the date of mailing of the notice of allowance to avoid abandonment of the application (emphasis added).” For more details, see

  2. 2.

    Patents give patent holders the exclusive right to use their invention for 20 years after the patent approval date.

  3. 3.

    Lansford (2006) finds that few of his sample firms voluntarily disclose the NOAs. Specifically, Lansford reviews over 10,000 patent-related articles issued by companies from January 1990 to November 2000 and can identify only 203 instances where companies voluntarily disclosed the receipt of an NOA. A total of 430,546 patents were issued during this period. We discuss this issue in more detail in Sect. 3.1.

  4. 4.

    To provide evidence of the private nature of the NOA issuance, we examine abnormal returns during the two days around the public announcement of the receipt of the patent. We document significant positive returns around the patent publication date (i.e., when the patent is officially approved and published at USPTO’s weekly Official Gazette), consistent with the patent being private and valued.

  5. 5.

    Patent-cash cycle is the expected time lag between patent issuance and cash flow realization. In our empirical analyses, we rely on industry to sort firms into shorter/long patent-cash cycle. Please see Sect. 6.2 for a more complete discussion.

  6. 6.

    For example, Gu (2005) argues and finds that the relation between patent/citation count and future cash flow is tenuous for pharmaceutical firms because of the longer innovation cycle and gradual resolution of uncertainty. Biotech and pharmaceutical companies apply for patents at various stages of their long innovation cycle. They apply for patents for new or improved “chemical entities, processes, machine, article of manufacture, or composition of matter,” and the filings of applications start in the early stages of research and development and continue through several stages of clinical testing, even after companies place the products on the market. See

  7. 7.

    Consistent with this time frame, the USPTO states that the average time from “issue fee to publication is four months” in responding to a question in an online chat on its website ( Furthermore, Lerner (1994, p. 324) states that the USPTO issues NOAs “two to eight months before a patent award.” Finally, we contacted a patent lawyer in New York City who confirmed that, in his experience, it takes five to six months for the USPTO to grant a patent once an NOA was issued.

  8. 8.

    We considered but do not include the market reaction on the date the patent was disclosed by the USPTO as a proxy for the expected value of the patent. Employing the firm-specific market reaction to the announcement of the patent would require that the equity market obtain access to the patent-related private information useful in estimating the expected value of the patent at the time the public announcement of that patent is made. The USPTO announcement does not provide that information, however.

  9. 9.

    Differences in citation counts based on the age of the patent might be due to the time for others to learn and cite a patent. Similarly, citations counts of any patent are truncated in time since researchers only observe the citations received so far with newer patents more likely affected by this truncation issue than older patents. Moreover, patents in some categories (e.g., computer, drug, and medical) are cited more often than those in other categories. To address concerns related to differences in citation counts for more recent patents, we use patent citation data updated as of the end of year 2006. This reduces the truncation problem for patents in our sample as all patents in our sample have a minimum of four citation years.

  10. 10.

    In an unreported robustness check, we control for borrowers’ patent records during the three years immediately before the loan initiation. All inferences remain the same.

  11. 11.

    PrPatent is highly correlated with both NOAInd and EV_NOA (our variables of interest), which leads to a potential concern with multicollinearity. We contend that controlling for prior patents is important in our analysis to address for a potential correlated omitted variable problem and so include PrPatent in our models. We do, however, re-estimate our analyses replacing EV_NOA and PrPatent with the residual from a regression of EV_NOA on PrPatent as a robustness test. Our inferences remain the same.

  12. 12.

    Compustat converts the S&P’s letter ratings to numerical values such that lower numerical values correspond to better ratings (2 for AAA, 4 for AA+, 5 for AA, 6 for AA−, and so on). In our initial sample with credit data from S&P’s long-term domestic issuer credit rating (964 of 2,655 observations), the mean and median rating is BBB (which translates to a numerical value of 11), with an interquartile range from A (9) to BB (14). When we employ our propensity-matched sample, 408 of 1,268 firm-years have credit ratings, with a mean and median rating of BBB-, which translates to a numerical value of 12 and an interquartile range from BBB+ (10) to BB− (15). For our NOA sample, we have credit ratings for 705 of 1,287 firm-year observations. The mean and median rating is BBB (11) and BBB+ (10), respectively, with an interquartile range from A (8) to BB+ (13).

  13. 13.

    Some of the firms in our sample have never issued public debt, which means that the rating agencies have not assigned them credit ratings. In our sample, these firms are assigned a value of one for NoRate.

  14. 14.

    Hadlock and Pierce (2010) examine the reliability of the KZ index and suggest that their measure better captures financial constraints. We calculate their measure (equal to −0.737 × Size + 0.043 × Size2 − 0.040 × Age, where size is the log of inflation-adjusted book assets and age is the number of years the firm has been on Compustat with a nonmissing stock price) and re-estimate our models using that measure in place of the KZ index. Our results and inferences are substantively similar.

  15. 15.

    To mitigate the concern that loan contract terms may be simultaneously determined, in unreported robustness tests, we use the reduced form equation approach of Dennis et al. (2000) to overcome the simultaneous equation bias and find all inferences remain the same if we exclude endogenous loan contract terms as independent variables.

  16. 16.

    Compustat codes bond ratings such that lower numerical values correspond to better bond letter rating. Thus values above the median have lower ratings than values below the median.

  17. 17.

    Our sample median of 11 corresponds to a BBB rating. S&P ranks corporate bonds in BBB or higher rating categories as investment grade, wherein the rating agency assesses the bonds as having low enough default risks that banks are allowed to invest in them. Corporate bonds with ratings below BBB are rated as speculative grade. Thus splitting the sample at the median is largely consistent with the conventional classification of investment versus speculative grade bonds. According to House Report 110-835 prepared for the Municipal Bond Fairness Act, the cumulative historic default rates for corporate bonds rated investment grade by S&P is 4.14 %, while the cumulative historic default rates for corporate bonds rated speculative grade by S&P is 42.35 %.

  18. 18.

    Bharath et al. (2011) and Sufi (2007) employ NoRate to proxy for borrower information opacity/poor information environment. Demerjian (2010) argues that information uncertainty in the credit market context refers to a lack of information about the borrower and the presence of a credit rating greatly reduces such uncertainty. He validates NoRate as a proxy for information uncertainty and finds that NoRate is correlated with other information uncertainty measures (e.g., firm age and return volatility) used in Jiang et al. (2005) in an equity market context.

  19. 19.

    Restricting our sample to a subset of industries limits the generalizability of our results. Nonetheless, this limitation also provides a setting where there is likely to be a greater number of NOAs and data to provide estimates useful in forming our patent related proxies.

  20. 20.

    Consistent with prior studies (e.g., Ivashina and Sun 2011; Chen and Martin 2011), we choose the earliest loan package because the initial borrowing is likely to generate a substantial amount of private information transfer and there is less ambiguity to attribute the information effect to the first loan than subsequent loans. Our results are robust to selecting the largest loan package for each firm-year.

  21. 21.

    Two hundred seventy of our sample firms have multiple NOAs. We do not control for the number of NOAs in our models as the expected value of the NOA is based on citation counts, rather than simply the number of patents granted. In addition, we re-estimate our models after excluding all observations with multiple NOAs and obtain qualitatively similar results.

  22. 22.

    Specifically, in Panel A the mean/median values of EV_NOA and PrPatent are 16.23/0.00 and 35.35/0.29, respectively. The PSM sample in Panel B presents mean/median values of EV_NOA and PrPatent of 1.82/0.00 and 8.39/2.06, respectively. The NOA sample in Panel C presents mean/median values of EV_NOA and PrPatent of 33.47/4.28 and 72.18/9.43, respectively.

  23. 23.

    Our discussion of credit ratings is based on SPRate, which is only available for firms with credit ratings. As noted in footnote 12, the Compustat numerical values are associated with letter bond ratings such that lower numerical values correspond to better letter ratings. Consistent with prior research, to avoid deleting observations without credit ratings in our regressions, we create SPRate2 and include it in our regressions. SPRate2 equals SPRate for borrowers with ratings and zero when no rating is available. SPRate2 and NoRate are included in the regressions to capture both the variation in ratings and the absence of borrower credit ratings. Thus it is difficult to interpret SPRate2 in univariate analyses (Tables 1 and 2). Instead, we report and discuss SPRate, which captures variation in credit ratings for borrowers with ratings, and NoRate, which captures the average riskiness for borrowers without credit ratings.

  24. 24.

    The associations between the control variables and SPREAD sometimes vary between model 1 and model 2. For example, NoRate is not statistically significant in model (1) but is in model (2). This positive coefficient is consistent with our PSM sample improving the ability to detect significant relations through the matching process. In general, differences across the model are likely due to the PSM procedure either (1) enhancing the ability of our model to detect significant relations, (2) reducing the variability of some of the control variables, or (3) both.

  25. 25.

    Jiang et al. (2005) and Zhang (2006) also include firm age as a proxy for information uncertainty. We re-estimated our models using the inverse of firm age (INVAGE) as an alternative proxy for information uncertainty. The results are qualitatively similar to using NoRate. The interaction (EV_NOA*INVAGE) is significantly negative, and NOAInd*INVAGE is negative but insignificant.

  26. 26.

    Rejecting H3A while providing evidence that supports H3B is consistent with general information about upcoming patents (i.e., the presence or absence of an upcoming patent) being associated with lower loan spreads, although the average benefit of such general information does not differ for firms with high versus low information uncertainty. Information related to the fact that a patent will be provided, without an estimate of the expected value of that patent, does not appear to be sufficient to resolve uncertainty for firms with high information uncertainty. These firms are inherently more difficult to understand and require more specific evidence to secure price concession from banks.

  27. 27.

    Specifically, Specialist equals one if the lead bank’s loan concentration within a three-digit SIC code industry in yeart-1, calculated as its total loans to the industry as a percentage of its grand total loan amounts for the year, is greater than the sample median and zero otherwise.

  28. 28.

    In Panel A, the coefficient on Specialist (the main effect) is 21.71, while the interaction term (NOAInd × Specialist) is −18.95.

  29. 29.

    We exclude event dates that are the same as, immediately before, or immediately after firms’ quarterly earnings announcement dates to ensure the market reaction is not driven by earnings announcement.

  30. 30.

    We rely on prior research to construct implied cost of capital (e.g., Baginski and Rakow 2012; Botosan et al. 2011). Specifically, we estimate of the cost of equity capital r PEG as the square root of (E 0 (EPS2) − E 0 (EPS1)/P 0). The firm-specific factors included in the model (e.g., market beta, market value of equity) are consistent with the proxies employed in Botosan et al. (2011).

  31. 31.

    Revolvers are borrowers who can borrow, repay, and re-borrow during a stated period. As a result, the maturity of loans classified as revolvers is not as well defined as the loan maturity of loans classified as term loans. Some revolvers, like Evergreen Revolver, allow the loan to be automatically renewed unless the lender stipulates otherwise prior to the maturity date.

  32. 32.

    Our sample includes observations from five two-digit SIC industries: SIC 28 (chemicals and allied products manufacturing), 35 (industrial and commercial machinery manufacturing), 36 (electronic and other electrical equipment manufacturing), 37 (transportation equipment manufacturing), and 38 (measuring and analyzing instruments manufacturers). The two-digit SIC industry 28 includes several four-digit SIC industries in the pharmaceutical area, including 2833 (medicinal chemicals and botanical products), 2,834 (pharmaceutical preparations), and 2,835 (in vitro and in vivo diagnostic substances). According to Managing Intellectual Property (Oct 2008), it takes an average of 8–12 years from inventing to commercializing a new drug for the US pharmaceutical industry. As our patent-cash cycle is based on industry, it could also be argued that differences across our shorter/long patent-cash cycles are due to industry, not the length of the patent-cash cycle.

  33. 33.

    A total of 247 NOA firms based on this window were excluded from this analysis relative to our earlier tests.


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The authors would like to thank two anonymous reviewers, Anne Beatty, Neil Bhattacharya, Patricia Dechow (editor), Peter Demerjian, Hemang Desai, Laura Gonzalez, John Jiang, Christian Leuz, Wayne Shaw, Pervin Shroff, Gregory Sommers, K. R. Subramanyam, Jayanthi Sunder, Andrew Winton, Hong Xie, Jieying Zhang, and participants at the 2009 FARS mid-year meeting, 2010 AAA annual meeting, University of Minnesota seminar, and the fifth annual IECG research symposium at University of Texas at Dallas for their helpful comments and suggestions. We are grateful to Benjamin Lansford for kindly sharing his hand-collected data of voluntary patent disclosure and for several helpful discussions.

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Correspondence to Marlene Plumlee.



See Table 9.

Table 9 Pr(NOAInd = 1) = α + β1 PrPatent + β2 RD + β3 FirmSize + β4 Q + Industry Fixed Effects + ε

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Plumlee, M., Xie, Y., Yan, M. et al. Bank loan spread and private information: pending approval patents. Rev Account Stud 20, 593–638 (2015).

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  • Private information
  • Information uncertainty
  • Information advantage
  • Patent pending
  • NOA
  • Cost of debt

JEL Classification

  • M41
  • G21