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As for loan characteristics, we find that uncertainty words are more frequent for loans that are larger, have longer maturity, have collateral, and are revolving. Overall, our determinants model has a good explanatory power with an adjusted R 2 of Table 3 , Panel A provides descriptive statistics for the variables used in Model 1 and our subsequent analyses. On average, a loan agreement contains uncertainty words as defined by Loughran and McDonald , and monitoring words from our dictionary in Appendix B.

An average contract is about 1, sentences long. Regarding the dependent variables of interest, the initial loan spread has a mean median of basis points. On average, only about 1. The loan and firm control variables are largely consistent with prior literature. Table 3 , Panel B presents the Pearson correlations for the variables used in our sample. We find some univariate evidence consistent with our expectations. In addition, there is a strong positive association 0. Table 4 reports estimation results of Model 2. In addition, we find that the control variables relating to the loan characteristics are mostly significant in directions that are consistent with prior literature.

For example, we find that loans with longer maturities, collateral, and greater number of covenants are associated with higher spreads. In contrast, loans that have performance-pricing provisions as well as revolver loans are associated with lower spreads. Further, we find that the traditional proxies for credit risk, which we use as control variables, also load in the predicted directions.

As such, these results support our predictions insofar as soft information appears to reflect important credit risk elements known to privately informed lenders and revealed publicly by linguistic characteristics of the loan agreement. Control variables are in directions that are consistent with our expectations.

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For example, we find that the use of dynamic covenants is negatively positively associated with firm size, ROA, tangibility, probability of default, analyst following, and financial reporting quality leverage, loan amount, maturity, number of covenants, and loan spread. Finally, we note that the pseudo R 2 is Overall, the results in Table 5 demonstrate that a greater degree of lender uncertainty is associated with a higher likelihood of using both dynamic covenants and performance-pricing provisions in debt contracts.

These results corroborate the initial loan spread results and thus add credence to the idea that soft information in loan contracts captures important credit risk elements known to privately informed lenders at contract inception. In Table 6 we examine whether soft information in debt contracts is predictive of future loan rating downgrades and loan amendments. The effects are also economically significant. Again, fit statistics suggest that our model has high explanatory power. For example, the loan downgrades analysis Panel A has a pseudo R 2 of These findings suggest that firms whose debt contracts contain more uncertain language are more likely to be downgraded or amended in the future.

Thus, the evidence again supports our conjecture that the language in debt contracts is reflective of information about future realizations of credit risk, incremental to traditional measures of risk used in prior literature. As such, the linguistics-based contract characteristics that reflect credit risk relevant information known to privately informed lenders at contract inception appear to also be informative regarding the creditworthiness of the borrower throughout the life of the loan.

In addition, control variables are significant in the directions consistent with prior literature.

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For example, similar to Wittenberg-Moerman , we find that bid-ask spread is negatively associated with ROA, firm age, the availability of credit rating, the number of market makers, and the time to maturity, while it is positively associated with revolver and distress indicators. As such, the secondary loan market trading results add to the body of evidence provided in further corroborating the notion that linguistic-based contract characteristics are informative at loan inception and throughout the life of the loan. In this section, we examine whether greater uncertainty at loan initiation is also associated with stronger monitoring efforts as revealed in loan agreements.

We conjecture that lenders will engage in more monitoring activities when faced with greater uncertainty at loan inception. For example, lenders could demand more forward-looking information such as budgets and forecasts. In addition, lenders can also request the borrowers to provide more timely information such as monthly financial statements or written communications with the auditor before the audit report is publicly available. We report the results in Table 8.

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We find in Table 9 that our results remain unchanged. Empirical research on credit risk traditionally captures firm and loan risk characteristics with proxies such as size, leverage, credit rating, or the number of covenants. Research to date has not examined the linguistic properties of private loan contracts and how such properties may publicly inform interested parties about the riskiness of those contracts.

Borrowing from the field of computational linguistics, we extract soft information from private loan agreements to investigate its implications for loan contract design and borrower credit risk. To capture this soft information, we follow Loughran and McDonald and focus on the intensity of uncertain words found in private loan agreements. Hence, our empirical approach allows us to assess whether observable contractual features that arise from private negotiations between borrowers and lenders publicly reveal credit risk relevant information that may be of use to other external stakeholders of the firm.

We first examine how uncertainty relates to initial contract terms and find evidence that the spread at loan inception is positively associated with uncertainty. This relation persists even after controlling for several potential confounding factors, including traditional credit risk proxies, such as the distance to default and borrower credit rating, and employing a two-stage model to mitigate endogeneity concerns.

We also find that uncertainty is positively associated with the use of dynamic and performance-pricing covenants. These results support the view that soft information appears to capture important credit risk elements known to privately informed lenders at contract inception. We then examine how uncertainty relates to realized credit risk. We find that uncertainty is positively associated with future loan downgrades and future loan amendments, as well as with the bid-ask spreads of loans trading on the secondary market. These results further support the view that linguistic-based contract characteristics that reflect credit risk relevant information known to privately informed lenders at contract inception appear to also be informative throughout the life of the loan.

As we provide evidence that the linguistic features of loan contracts publicly convey soft information about the riskiness of the borrower, our article has the potential to add to the literature whose goal is to predict future negative credit-related events. Specifically, incorporating information that is embedded in the contract by negotiating parties holds promise for improving the current set of credit risk models e. These avenues are left for future research. Below is the list of keywords used to identify sentences that are likely to contain private information exchange or references to future exchanges of private information.

Without limiting the foregoing, the Parent will furnish to the Agent, in sufficient copies for distribution by the Agent to each Lender, in such detail as the Agent or the Lenders shall request, the following. As discussed in Henry and Leone , word-based measures of linguistic content perform at least as well as those using Bayesian methods. See Appendix B for the list of keywords, which we develop after reading a random subsample of loan agreements from the sample population.

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We thank one of our reviewers for suggesting this alternate interpretation of the measure. As an alternative to the OLS specification, we also use a negative binomial model to estimate the first stage residuals. Under this approach, the inferences from our second stage results remain unchanged. We thank one of the reviewers for this suggestion. DealScan treats amendments that require unanimous consent i. Skip to main content. Article Menu. Download PDF. Open EPUB.

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Cite Citation Tools. How to cite this article If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Download Citation If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Share Share. Recommend to a friend. Sharing links are not available for this article. I have read and accept the terms and conditions. Copy to clipboard. Request Permissions View permissions information for this article. Soft Information in Loan Agreements. Lin Cheng. Tzachi Zach. Article information. Article Information Volume: 33 issue: 1, page s : Article first published online: February 1, ; Issue published: January 1, Email: bozanic.

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Keywords private information , loan contracts , textual analysis , credit risk , contract design. Literature Review and Empirical Predictions. Literature Review. Empirical Predictions. Collection of Private Loan Agreements. View larger version. Matching Credit Agreements to DealScan. Measuring Soft Information in Credit Agreements.