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PurposeThis study has two primary objectives. First, it analyzes the information content of confidentiality strictness in corporate loan credit agreements. Second, it examines how confidentiality strictness impacts covenant design, lending syndicate structure and loan pricing.Design/methodology/approachUsing a sample of 6,327 loan credit agreements originated by US public firms in the period of 1996–2017, this study measures the confidentiality strictness in loan contracts using textual analyses that capture the appearance of confidentiality-related words and the length of confidentiality provision. All regressions include relevant loan characteristics, firm-specific accounting variables, industry and year fixed effects. To address the endogeneity concern, the paper uses borrowing firms' rival cash holdings and R&D expenditures to instrument for confidentiality strictness in two-staged least square regressions.FindingsBorrowers which have higher R&D and operate in more competitive product markets have tighter confidentiality policies. Furthermore, this study reveals that confidentiality strictness is negatively associated with the imposition of financial covenants, especially performance covenants. Loan contracts for borrowers with stricter confidentiality on average have more relaxed covenant intensity, measured by the number of covenants. The study also shows that stricter confidentiality attracts finance companies, which have strong expertise in product markets of their parent firms, into the lending syndicate. However, confidentiality-conscious borrowers with higher degree of information asymmetry are subject to higher loan spreads.Originality/valueThis study provides the first examination of confidentiality policies in loan contracts and supports the idea that loan provisions are not simply made of “boilerplate” language. The results suggest that, for confidentiality-sensitive borrowers, the greater exposure to product market competition helps control managerial slack and substitute monitoring from financial markets.

Computing the matching statistics of patterns with respect to a text is a fundamental task in bioinformatics, but a formidable one when the text is a highly compressed genomic database. Bannai et al. gave an efficient solution for this case, which Rossi et al. recently implemented, but it uses two passes over the patterns and buffers a pointer for each character during the first pass. In this paper, we simplify their solution and make it streaming, at the cost of slowing it down slightly. This means that, first, we can compute the matching statistics of several long patterns (such as whole human chromosomes) in parallel while still using a reasonable amount of RAM; second, we can compute matching statistics online with low latency and thus quickly recognize when a pattern becomes incompressible relative to the database. Our code is available at https://github.com/koeppl/phoni .

Let a text T [1..n] be the only string generated by a context-free grammar with g (terminal and nonterminal) symbols, and of size G (measured as the sum of the lengths of the right-hand sides of the rules). Such a grammar, called a grammar-compressed representation of T , can be encoded using essentially G lg g bits. We introduce the first grammar-compressed index that uses O(G lg n) bits and can find the occ occurrences of patterns P [1..m] in time O((m 2 + occ) lg G). We implement the index and demonstrate its practicality in comparison with the state of the art, on highly repetitive text collections.

PurposeThis paper investigates the significant increase in S corporation banks converting to C corporations following the 2017 Tax Cuts and Jobs Act (TCJA) and the shift in motivations behind these conversions.Design/methodology/approachThe paper uses bank-level panel data from Federal Deposit Insurance Corporation (FDIC) Call Reports to analyze the determinants of S bank conversions after the TCJA, comparing post-TCJA conversion trends with pre-TCJA trends utilizing an ordinary least squares (OLS) and logistics model.FindingsThe study finds that post-TCJA conversions are primarily driven by financially stable banks seeking improved tax conditions and relaxed shareholder restrictions as C corporations. This contrasts with pre-TCJA conversions, which were predominantly driven by financially distressed S corporation banks seeking new equity capital to maintain solvency.Research limitations/implicationsThe findings necessitate a comprehensive reconsideration of the Subchapter S status' sustained relevance for smaller institutions, especially in light of the comparative benefits now offered by the C corporation status post-TCJA. The results underscore the importance of ongoing academic investigation to deepen the understanding of the evolving fiscal landscape's effects on community banks, thereby contributing to the knowledge of the resilience and health of the US economy.Practical implicationsThis research nudges policymakers and regulators to contemplate the ongoing relevance and advantages of the S corporation status. Given the substantial benefits conferred by the C corporation status in the post-TCJA environment, this study suggests that retaining the S corporation status may not offer the same appeal for smaller community banks as it once did.Originality/valueThis paper contributes to the broader understanding of the impact of tax policy on businesses' organizational choices, particularly in the banking industry and emphasizes the need for a comprehensive review of the S corporation status to assess its ongoing applicability in fostering small and community-focused financial institutions in light of the evolved corporate tax landscape.

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