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Record W4417359951 · doi:10.1108/ijmf-05-2024-0293

The information advantage of forthcoming patents on debt financing

2025· article· en· W4417359951 on OpenAlexfundno aff
Kelly Nianyun Cai, Steven Zhu, Hui Zhu

Bibliographic record

VenueInternational Journal of Managerial Finance · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCredit ratingDebtEx-anteNoticeBondAllowance (engineering)Private information retrievalInformation asymmetry

Abstract

fetched live from OpenAlex

Purpose The United States Patent and Trademark Office (USPTO) issues a notice of allowance (NOA) prior to a patent being granted, which serves to notify firms of forthcoming patents. An NOA could also serve as a positive signal. Credit rating agencies (CRAs) are exempt from regulation fair disclosure. Firms can disclose credible patent information to CRAs with minimal proprietary costs. When firms seek to fund their innovative activities through corporate bonds, they may benefit from disclosing their forthcoming patents to CRAs. This study focuses on CRAs' ex ante information advantage and examines the impact of private information on borrowers' initial credit rating and the cost of debt. Design/methodology/approach This paper studies how NOAs affect credit ratings and borrowing costs in the corporate bond market. We find that firms with forthcoming patents tend to receive better credit ratings and exhibit a lower cost of debt. Moreover, this informational advantage persists when considering the subsequent economic impact of the patents. Further subsample analyses show that the information advantage of forthcoming patents is more pronounced during non-crisis periods and for investment-grade bonds and bonds issued by large firms, as they are more likely to have a lower cost of debt even after controlling for the credit ratings. Our findings are robust for both the propensity score matching approach and the entropy balancing method. Findings We find that issuing firms with forthcoming patents tend to receive better credit ratings and exhibit a lower cost of debt. This informational advantage is retained even after accounting for the subsequent economic impact of the patents. Further subsample analyses show that the information advantage of forthcoming patents is more pronounced during non-crisis periods and for investment grade bonds and bonds issued by large firms as they are more likely to have a lower cost of debt even after controlling for the credit ratings. Our findings are robust for a propensity score matched approach and the entropy balancing method. Research limitations/implications Our study has some limitations. Our results depend on the data we have for NOA disclosures. There may be hidden factors that affect both patenting and bond issuance that we cannot observe. Our analysis is limited to U.S. firms, so the results may not be applicable in other countries with different regulations or market conditions. We also focus only on NOAs; however, other kinds of private information could matter. Future studies could examine international settings, consider other types of intangible assets or investigate how changes in regulation or CRA practices impact the use of private information in debt markets. Practical implications Our research identifies a gap in the literature concerning the role of NOAs in public debt issuance, providing a comprehensive analysis of how these patents affect credit ratings and borrowing costs – a dimension previously lacking in the existing literature. For managers, this implies a practical pathway to reducing capital costs by sharing innovation milestones with rating agencies. Investors can better interpret bond market signals by understanding when and how private information reaches CRAs. For policymakers, our work highlights the importance of transparency and the potential consequences of regulatory exemptions for CRAs with regard to selective information access. Social implications Our findings add to existing theories on information asymmetry and signaling by demonstrating that CRAs play a central role in bringing private information about innovation into the public bond market. By highlighting this process, our study connects research on rating agencies, innovation and bond pricing and shows how sharing selective information with rating agencies can have a direct impact on a firm's ability to raise capital and the terms it receives. Originality/value We provide new empirical evidence that in the corporate bond market, forthcoming patents can improve credit ratings, which in turn reduces the cost of debt financing at the time of bond issuance. Our study identifies a gap in the literature concerning the role of NOA in public debt issuance, providing a comprehensive analysis of how these patents affect credit ratings and borrowing costs – a dimension previously lacking in the existing literature. For practitioners, our findings suggest that strategically disclosing forthcoming patents to CRAs can be a valuable tool for firms seeking to optimize bond issuance conditions.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.235
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2025
Admission routes1
Has abstractyes

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