International Review of Finance最新文献

Using a unique dataset tracking the career paths of inventors in U.S. public firms, we investigate how inventor-base concentration impacts corporate cash holdings. A concentrated inventor base enhances the efficiency of utilizing inventors' human capital, reducing the transaction motive to hold cash. Conversely, the potential loss due to key inventors' departure may increase the precautionary motive to hold cash. We find a negative impact of inventor-base concentration on cash holdings, supporting the notion of reducing the transaction motive. Firms with a more concentrated inventor base have lower demand for spending cash on labor costs and R&D. Additionally, the negative impact on cash holdings is more pronounced for firms facing financial constraints. Finally, the value of cash holdings is negatively related to the degree of inventor-base concentration. These results highlight the importance of understanding a firm's human capital strategies when evaluating its financing policy.

We conduct a comprehensive analysis of eight machine learning models (partial least squares, scaled principal components, the least absolute shrinkage and selection operator, ridge regression, random forest, gradient boost decision trees, support vector machines, and neural networks) and the forecast combination method to forecast China's inflation. We use an extensive monthly dataset of 28 predictors with the data period covering January 2000 to December 2022. Our empirical outcomes show that these models beat the autoregressive benchmark regarding out-of-sample R squares. We evaluate the gradient boost decision tree (GBDT) and the forecast combination model as the most effective machine learning tools for forecasting China's inflation rate across various forecasting horizons and evaluation criteria. Moreover, our analysis of variable importance (Gu, Kelly, and Xiu 2020) demonstrates that the retail price index of food and the producer price index of total industry products are the two most dominant predictive signals. These outcomes reflect that structural components and cost-push factors primarily influence China's inflation rate. Our conclusions are robust across various settings.

We analyze the relation between initial public offering (IPO) over-issuance and a firm's subsequent acquisition decisions. We find that newly listed firms are more likely to engage in merge & acquisitions (M&As) and initiate more and larger-sized M&As after raising more excess cash in their IPO. Additionally, this IPO over-issuance is negatively associated with the newly listed firm's post-acquisition stock performance. Moreover, Type I (principal-agent) and Type II (principal-principal) agency problems explain those relations. The negative relation between IPO over-issuance and stock performance is exacerbated for companies that have made large and industry-diversifying M&As. Further analyses reveal that acquirers who raised more excess cash in their IPO prefer to pay for their M&As entirely in cash.

We detect jumps with the cubic variation and derive its exact distribution under a generic pure-diffusion model with deterministic time-varying volatility. Our method performs well for not only high- but also low-frequency returns. We use the jump testing method to construct monthly and daily jump indicators from the daily and intraday S&P 500 index returns, and find that they can be significantly and robustly predicted by VIX. Other option-implied and historical moments are either subsumed by VIX or are conditionally useful. Our results support the superior informational role played by the risk-neutral volatility in predicting future price jump events.

We examine the effect of public trust on personal loan grants and the default risk of Chinese regional commercial banks during the bank branch regulation regime changes. Using exogenous shocks of bank branch regulation regimes in quasi-natural experiments, we find that public trust exhibits a more pronounced increase in personal loan grants for regional banks. Additionally, we report a negative relationship between public trust and default risk. Finally, we observe that city commercial banks that open branches in high public trust provinces during the deregulation period lead to an increase (decrease) in personal loan grants and (default risk).

This study examines how institutional investors influence workplace safety. Using data from the Occupational Safety and Health Administration, we find that firms with higher institutional ownership have significantly lower injury and illness rates, especially those with dedicated investors who are geographically proximate to their establishments and high union coverage levels. Our analysis indicates that such firms adopt employee-friendly corporate cultures and invest in organizational capital and workplace safety. Overall, this study offers novel and robust evidence of the monitoring role of institutional investors in shaping workplace safety outcomes.

This paper discusses the impact of regret aversion on Chinese stock market returns from the asset pricing perspective. From the intertemporal investment and consumption analytical framework, a representative investor determines the optimal wealth allocation by perceiving the stock's “expected rate of return” and “regret effect” to maximize utility. The simulation results show that the expected returns present a downward convex shape with the change in regret aversion. Using China's A-share market data, the empirical tests confirm the mechanism and different bull and bear market signals. Our findings reveal regret aversion in the A-share market, and “market return” is an essential measuring indicator, which improves the consumption-based Capital Asset Pricing Model (CCAPM) empirical results by more minor pricing errors and equity premiums. Comparatively, the variations in cross-sectional stock returns during bull markets illustrate the herd behavior prevalent in the Chinese market, and the cross-sectional data observed in bear markets demonstrate a superior fitting effect.

This study investigates the relationship between corporate culture and debt maturity structure. Utilizing a text-based measure of corporate culture derived from a cutting-edge machine learning approach, we uncover a strong positive link between corporate culture and short-term debt usage. We further find that this relationship is particularly pronounced for firms with high information asymmetry and heightened financing constraints. We conduct several sensitivity analyses that confirm the robustness of our findings. Overall, the findings enhance our understanding of how corporate culture influences corporate financing decisions.

We study the pricing implication of the climate change news index proposed by Engle et al. (2020). Specifically, we find the significant risk premium associated with climate change news. The risk premium increases for firms in fossil-fuel and carbon-intensive industries, while decreasing for firms in low-emission industries. Furthermore, we document that the impact of climate change news is more negative for “value” and “big” portfolios compared to “growth” and “small” portfolios, and the impact of climate change news varies for firms headquartered in Democratic states versus Republican states.

We analyze the optimal design of deferred compensation for bank executives under agency conflicts in a continuous time model with a bank liability structure. Our model demonstrates that a well-designed deferred compensation contract can effectively mitigate bank executives' asset substitution problem and significantly enhance the total value of the bank, which aligns with existing empirical studies. Moreover, we find that the motivation for bank executives to shift risk under deferred compensation is diminished when supervision is more stringent, the bank has greater market power, or the write-down ratio of debt is lower.