International Review of Finance最新文献

This paper proposes a superior default-prediction model using machine-learning techniques. Traditional risk-assessment tools have fallen short, especially for foreign investors who face significant transparency issues. Using detailed financial data on Chinese bond issuers, our model provides much broader coverage than international credit-rating agencies offer. We achieve better than 90% accuracy in predicting credit-bond defaults, significantly outperforming Altman's Z-scores. This study not only advances predictive analytics in financial risk management but also serves as an early warning device and reliable default-risk detector for investors aiming to navigate the complexities of the Chinese bond market.

Exploiting account-level daily stock holding records of over 24,000 retail investors, we show that local investors increase holdings of local stocks more than the nonlocals' in the case of natural disasters. Additional tests suggest that the effect is likely driven by the local investors' information advantage about the intrinsic value of local stocks, navigating them to the underpriced ones and thus achieving superior stock returns. Our study reveals the economic reasoning underlying local biases particularly under natural disasters.

We examine intraday information flows between shares cross-listed in Hong Kong and Shanghai. The relative trading volume in Hong Kong (Shanghai) is positively related to the relative contribution to price discovery, based on Hasbrouck (1995) Information Share. Northbound trading by Hong Kong investors has a greater contribution to price discovery than southbound trading by Mainland Chinese investors. We construct a few measures of intraday market qualities: (1) probability of informed trading; (2) intraday effective spread; (3) pricing error; and (4) intraday volatility ratio. Evidence indicates that northbound trading and institutional southbound trading, but not retail southbound trading, are informed and improve pricing efficiency.

This paper investigates whether the JinHui financial education project (i.e., a large-scale financial education program in China) reduces household vulnerability to poverty. Using data from the China Family Panel Studies and the JinHui Financial Education project data from the China Foundation for Development of Financial Education, we find that the JinHui project significantly reduces household vulnerability to poverty. Our economic channel analyses find that the JinHui project alleviates household vulnerability to poverty through improving household risk management ability. Interestingly, our mechanism analyses find no evidence that increasing household income is a mechanism through which the JinHui project reduces household vulnerability to poverty.

This study examines the benefits of environmental, social, and governance (ESG) disclosure assurance (EDA) in reducing a firm's cost of equity capital. Using data on Chinese listed financial institutions from 2006 to 2022, we find a significant and negative relationship between EDA and the cost of equity capital. When an EDA is provided by an accounting firm, as well as when the auditor is a shared resource for the annual report, the cost of equity decreases significantly. Further analysis shows that the effect of EDA on the cost of equity is more pronounced for institutions with lower information transparency and higher operational risk. Additionally, the impact of EDA is amplified when these institutions choose to disclose their ESG information separately rather than integrating it within their financial reporting, when disclosures align with the Global Reporting Initiative standards, and when the content of the disclosures is more comprehensive and detailed.

This study investigates the relationship between board chairs' early-life experiences during the Great Chinese Famine and the debt maturity choices of Chinese listed firms from 2000 to 2017. The findings reveal that board chairs with famine experience demonstrate a stronger preference for long-term debt usage. Moreover, these board chairs underestimate future corporate earnings, are less prone to overinvestment, adopt more hedging strategies, and ensure higher-quality accounting information. The results are particularly pronounced in firms with lower asset redeployability, higher financial distress risk, the absence of political affiliations, non-state ownership, lower market competition, and heightened economic policy uncertainty. These findings suggest that the observed behavior stems from a risk-averse orientation rather than altruistic motivations among board chairs with famine experience.

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.