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.

We find a robust negative relation between skewness/lottery-like features, proxied by maximum return (MAX) over the last month, and future returns for stocks preferred by individual investors. This negative relation is nonexistent for the rest of stocks. We identify stocks preferred by individual investors through bundling ten stock characteristics associated with their stock preferences. The negative relation between MAX and future return is produced by the stocks preferred by individuals that account for less than 5% of the overall market capitalization. Our results are robust to alternative definitions of MAX and lottery-like features such as total, idiosyncratic, and expected skewness.

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.

The opening of equity markets to foreign investment by developing countries appears to generate an enormously large positive growth effect (see Bekaert, Harvey, and Lundblad (2005), Journal of Financial Economics 77, 3–55) in spite of a relatively small role of such markets for financing investment in most economies. We propose a spillover channel from equity market opening to lower costs of bank loans, which helps to explain this puzzle. From analyzing bank loan data associated with China’s introduction of the Qualified Foreign Institutional Investors program, we find significant support for this channel. Furthermore, we show that a reduction in the risk premium in loans is an important mechanism.

We aim to make two contributions to the literature on the effects of transaction costs on financial price volatility. First, by augmenting a double differencing approach with a research design with three ingredients (a common set of companies simultaneously listed on two stock exchanges, binding capital controls, and different timing of changes in transaction costs), we obtain a control group that has identical corporate fundamentals as the treatment group. We apply the research design to Chinese stocks that are cross-listed in Hong Kong and Mainland China. Second, we allow transaction costs to have different effects in markets with different maturity. We find a significantly negative relationship, on average, between stamp duty increase and price volatility. However, this average effect masks some important heterogeneity. In particular, when institutional investors have become a significant part of the traders’ pool, we find an opposite effect. Overall, our results suggest that a Tobin tax could work in an immature market, but can backfire in a more developed market.

By decomposing close-to-close mid-quote returns of ETFs into their overnight and intraday components, we find that the overnight return is significantly positive, whereas the intraday return is negative. This overnight–intraday return differential is ubiquitous across ETFs tracking different asset classes or assets located in different time zones. This phenomenon cannot be explained by differences in overnight and intraday risks, macroeconomic announcements, or information asymmetry. Instead, our analysis reveals that the return pattern is primarily driven by demand shocks from retail investors and limited supply from arbitrageurs. These results indicate that the convenience of buying ETFs during intraday trading hours carries a hidden cost to investors.

Short sellers actively exploit trading opportunities from insider sales. We argue that, in response to concern about potential order flow information leakage, insiders strategically disguise their order flows to escape trading competition. Our model predicts that, when short sellers are sensitive to order flow information, insiders are more likely to adopt a cautious trading strategy, i.e., splitting their trades over time. Empirically, we identify cautious trading by tracking consecutive transactions at the insider-strategy level. We find that, when anticipating intensive short selling potential, (1) insiders tend to trade cautiously; and (2) cautious insiders tend to reduce their initial trades. Overall, we highlight the strategic interaction between insiders and short sellers on the diffusion of order flow information.