A persistent difficulty in monitoring private-credit risk is that narrative and quantitative information in periodic filings are produced jointly but evaluated separately. This leaves open the question of whether disclosure language is a useful signal of risk management behaviour or merely an echo of conditions already visible in published data. For business development companies (BDCs), this separation carries a particular cost: the sector sits at the intersection of private credit, fair-value accounting, and floating-rate funding, where filing language about portfolio conditions and the macro environment may reflect the cycle itself rather than add to what published rate and spread data already reveal. This paper asks two questions. First, do aggregate BDC text measures of macro and portfolio-credit language co-move with key macro series over time? Second, does cross-sectional text intensity relate in a stable, linear way to the same BDC’s reported ratios and their volatility? Using dictionary-based filing scores linked to over 590 BDC observations and macro series from 2010 to 2025, we find macro text in filings correlates strongly with variables such as the Federal funds rate and the two-year Treasury yield. Portfolio-credit text lines up with corporate spreads and the unemployment rate. At the firm-year level, associations between text and balance-sheet outcomes are weak. This indicates that BDC narratives are linked with the macro cycle, but there is not a tight mapping to risk metrics in reported financials from year to year, consistent with a degree of insulation in private credit from prevailing macro conditions. For creditors, investors, and supervisors of private-credit vehicles, this asymmetry of macro co-movement without firm-level signal has direct implications for how narrative disclosure should be weighted in risk monitoring and governance frameworks. The aggregate regression results are based on sixteen annual observations and should be interpreted accordingly.
In this paper, we examine the impact of credit rating distance between acquirers and targets on mergers and acquisitions, using a sample of 409 U.S. domestic deals. We find that deals involving greater credit rating distance have significantly higher synergy returns, larger abnormal returns for both acquirers and targets around deal announcement, and stronger long-run performance. These findings underscore the potential for financial synergies when merging firms with differing capacities to access external debt markets. Through borrowing capability transfer, the pooled financing capacity of the combined entity can be more efficiently utilized, thereby mitigating both overinvestment associated with excess liquidity and underinvestment driven by financial constraints. We further document that greater credit rating distance increases the likelihood of cash payment, raises the probability of deal completion, and on average, reduces the time taken to complete the transaction. These results are robust after controlling for a range of firm and deal level characteristics and are consistent across alternative measurements and model specifications. Overall, this is the first comprehensive study using a formal event study framework to demonstrate the multi-dimensional impact of credit rating distance on M&A outcomes.
This study explores whether the intensity of AI (artificial intelligence) related language used during the Spring 2026 earnings calls is associated with short-term abnormal stock returns and whether this relationship differs between technology-oriented and comparison firms. Using a cross-sectional event study framework, this study analyzes a sample of 50 publicly traded large-cap U.S. firms, consisting of 25 technology-oriented firms and 25 comparison firms from non-technology industries. AI disclosure intensity was measured as the proportion of AI-related terminology relative to total transcript word count. In contrast, market reactions were measured using cumulative abnormal returns (CAR) over a three-day [-1,+1] event window surrounding each earnings announcement. The results indicated that technology-oriented firms exhibited higher average AI disclosure intensity and greater dispersion in cumulative abnormal returns than firms in the comparison cohort. Regression analysis suggested that the association between AI disclosure intensity and short-term abnormal returns varies across industry groups, with AI-related communication appearing to have a stronger relationship with market reactions, particularly among technology-oriented firms. However, these findings should be interpreted as statistical associations rather than any evidence of causality. Moreover, other earnings-related information released simultaneously may also influence stock-price movements. Overall, the study provides exploratory evidence that investors may evaluate AI-related corporate disclosures differently depending on industry context. These findings are consistent with theories of corporate signaling and information credibility, although additional research using larger samples, longer time periods, and more comprehensive control variables is required to establish the underlying mechanisms.
This paper focuses on whether adopting International Financial Reporting Standards (IFRS) reduces financial risk and accounting fraud. Using 9,194 firm-year observations from Thai-listed non-financial firms between 2011 and 2022, we apply the Beneish M-Score and Altman Z-Score models to detect earnings manipulation and financial distress. The evidence shows that firms reporting under IFRS display lower M-scores, suggesting reduced earnings manipulation. However, IFRS adoption does not have a significant effect on financial distress. This is likely because while IFRS improves transparency and reduces opportunistic reporting, it does not address fundamental financial issues. Overall, our paper provides new evidence on the role of IFRS in enhancing reporting quality in emerging markets and offers implications for financial reporting policy in countries such as Viet Nam.
This paper aims to examine the associations between inflation expectations and earnings management, accrual-based and real activities manipulation, using a sample of firms listed and incorporated in euro area countries. Drawing on survey-based measures of inflation expectations, along with the models developed by Kothari et al. (2005) and Roychowdhury (2006) to estimate accrual and real earnings management, and a sample spanning from 2005Q1 – the year when most euro area countries adopted IFRS – to 2023Q4, the authors perform fixed-effects regressions. The authors find that higher firms’ inflation expectations are associated with greater accrual-based earnings management and real earnings management through overproduction, whereas sales manipulation exhibits a negative association. In addition, in recessionary periods, when there is a broad decline in economic activity, these relationships become more pronounced, with upward accrual-based earnings management and real earnings management through sales manipulation being particularly evident. Finally, when the effective lower bound (ELB) is binding, the positive relation between earnings management and inflation expectations generally weakens. Replicating the analysis using consumers’ inflation expectations as the primary explanatory variable, the authors further find that downward accrual-based earnings management and overproduction are more prominent during recessions, whereas firms appear to rely on sales manipulation when the ELB is being approached. To the best of the authors’ knowledge, this is the first study to investigate the associations between inflation expectations and earnings management. The results of this study may have implications mainly for investors, auditors and central banks.
