This paper reports the results of an investigation of the impact of digital transformation on debt financing costs. By integrating information asymmetry theory and agency theory, we have developed a threefold risk-theoretic model to demonstrate how corporate digital transformation affects a firm’s debt financing costs. Drawing on a dataset of Chinese listed companies from 2007 to 2022, we measured digital transformation across three dimensions: attention, investment, and outcomes. The findings reveal that corporate digital transformation significantly reduces the cost of debt financing for companies. Mechanism tests indicate that digital transformation reduces debt financing costs by mitigating information risk, agency risk, and earnings risk through enhanced information disclosure quality, strengthened corporate governance, and improved expected earnings. Our paper not only enriches emerging research on the impact of corporate digital transformation on financial accounting but also provides theoretical insights for effectively alleviating the issue of expensive financing.

Using earnings conference calls, we investigate banks’ views of the Troubled Asset Relief Program (TARP) to understand why TARP generated so few loans. We find that banks generally regarded TARP favorably and many mentioned using TARP funds to make loans. However, actual loan growth fell well below expectations based on prior capital ratios, even among banks that publicly committed to lending. Other banks highlighted that the funds would improve their capital ratios. We show that these perspectives are largely unrelated to banks’ ex-ante financial characteristics, but instead reflect the evolving conditions during the crisis period. These shifts are consistent with a large decline in the fraction of banks that commented on the favorable pricing of the preferred stock over time. Our findings suggest that banks primarily used TARP funds to strengthen capital ratios, partly driven by CEO career concerns. Weak loan demand and evolving market conditions also contributed to the sluggish loan growth following the TARP injections.

Using the Troubled Asset Relief Program (TARP) as a laboratory, this paper examines the impacts of bank bailouts on bank-dependent clients. We find that large TARP recipient banks reduce credit supply to dependent borrowers in the post-TARP period. A large fraction of credit supply reduction is due to regulatory uncertainty on account of an increased likelihood of fines. Liquidity hoarding by TARP banks also drives part of the reduction in credit supply. Relationship borrowers experience a valuation loss around the announcements of their main banks’ TARP approvals consistent with a credit supply reduction.

Mutual funds investing in illiquid corporate bonds actively manage Treasury positions to buffer redemption shocks. This liquidity management practice can transmit non-fundamental fund flow shocks onto Treasuries, generating excess return volatility. Consistent with this hypothesis, we find that Treasury excess return volatility is positively associated with bond fund ownership, and this pattern is more pronounced among funds conducting intensive liquidity management. Causal evidence is provided by exploiting the U.S. Securities and Exchange Commission’s 2017 Liquidity Risk Management Rule. Evidence also suggests that the COVID-19 Treasury market turmoil was attributed to intensified liquidity management, an unintended consequence of the 2017 Liquidity Risk Management Rule.

Identifying firms’ bond-market-specific economic links through credit-rating comovement of their corporate bonds, a long-short strategy for stocks based on these links generates a risk-adjusted alpha of 0.45% per month, which cannot be explained by existing economic links in the literature. Market segmentation between the equity and bond markets appears to be the underlying mechanism: (i) The cross-return predictability is muted in the bond market; (ii) The cross-return predictability is mitigated in the presence of cross-holding investors; (iii) Equity analysts slowly incorporate information from rating-comovement links to their forecasts.