International Review of Finance最新文献

In this study, we investigate the informativeness of the non-financial environmental, social, and governance (ESG) information provided by various intermediaries including firms, the media, and ESG rating agencies, to financial analysts. By analyzing cross-sectional ESG data from various sources related to 56 countries, we find that ESG information plays a crucial role in shaping analyst forecasts. More importantly, we examine the interaction between internally and externally sourced information on affecting analysts. Our results suggest that while ESG information from the media attenuates the impact of firms' ESG disclosures on reducing analysts' forecast errors and dispersion, information from ESG rating agencies increases this impact. We also find that globally implemented mandatory ESG disclosure regulations significantly increase the effect of ESG information from all three sources on analysts. In countries with a stronger stakeholder orientation, financial analysts tend to derive greater relative benefits from ESG information obtained from various sources. Overall, the findings of this study support the conclusion that both externally and internally sourced ESG information is of significant value for financial analysts, and the implementation of mandatory ESG disclosure requirements in a country increases this significance.

This study examines the transmission of monetary policy shocks on stock market returns, liquidity, expected inflation, and inflation under varying economic policy uncertainty (EPU) levels in the Indian context. Using a Smooth Transition VAR model, we find that contractionary monetary policy increases illiquidity and decreases returns during the high EPU regime but has minimal effects during the low EPU regime. Additionally, monetary policy effectively curtails expected inflation and inflation in a low EPU regime more than in a high EPU regime. The results emphasize monetary policy transmission via expectation channels over asset pricing channels.

This paper examines illegal insider trading in Australian equity markets, focusing on whether such trades leave observable footprints in prices and returns. We compile a hand-collected dataset of identified insider-trading incidents. Using an event-study design, we find minimal footprints for earnings announcements and a small negative price effect for M&A deals. A detection-controlled estimation (DCE) model reveals that while 17.79% of M&A announcements likely involve insider trading, regulators detect only 29.59%. Thus, relying solely on detected trades understates insider trading's broader impact. Our results highlight stealthy trading tactics and the need for enhanced surveillance to combat hidden illegal trades.

We estimate the enduring momentum probabilities of past winners and losers continuing as future winners and losers by incorporating a comprehensive set of firm characteristics. Our results reveal that combining the price momentum signals and enduring momentum probabilities generates returns double those of the traditional price momentum strategy. Furthermore, the robust performance of the enduring momentum strategy cannot be fully attributed to factors such as seasonality, limits to arbitrage, and transaction costs.

We develop a dynamic investment model that incorporates agency conflicts, considering the impact of rare disaster and carbon emission reduction. This model elucidates the effects of carbon emission reduction on capital investment, asset pricing, and welfare. Our findings indicate that optimal carbon emission reduction level increases with disaster risk, volatility, and risk aversion. Furthermore, in comparison to the inaction scenario, carbon emission reduction leads to underinvestment, enhances Tobin's $��q�$, increases risk-free rate, and decreases risk premium. This introduces a non-monotonic relationship among capital investment, risk-free rate, risk premium with disaster risk. Lastly, carbon emission reduction mitigates the cost for the outside shareholder to address agency conflicts.

We compare the impact of broad-based equity incentives and government R&D subsidies on the efficiency of corporate innovation. Chinese corporations that offer broad-based incentives for employees and managers demonstrate greater R&D investment efficiency, as evidenced by a higher ratio of innovation outputs to cost allocation. Conversely, firms receiving government R&D subsidies demonstrate lower efficiency. Accounting for endogenous treatments in a multi-treatment framework, we suggest an information-sharing environment is critical for efficient capital allocation. Government agencies will likely provide subsidies based on information different from what firm headquarters might have, while broad-based incentive programs encourage employee coordination, enhancing efficiency and project quality. To signal project quality, firms with broad-based incentives capitalize a higher proportion of R&D costs than subsidized firms. We also find that combining broad-based incentives and subsidies might not create synergies.

A new measure of investor sentiment is introduced and tested in this study. Different from using search volume of certain macroeconomic terms to capture investor attention/sentiment, this new measure is crafted from analyzing and classifying the sentiment contents of textual comments of retail investors active in a major Chinese stock forum. Different from the traditional sentiment studies linking market-level sentiment to market-wide reactions, this uncomplicated measure is constructed for individual stocks and subsequently, reactions of the same stocks are tracked and examined, offering a more direct and precise correlation test. In our validation tests, we show a significantly positive correlation between investor sentiment and three stock market parameters that is, stock return, price volatility, and information efficiency. Specifically, a positive sentiment is associated with higher stock returns and a higher degree of information efficiency as well as higher price volatility. These associations seem to attenuate with improvement in the information environment, such as better investor protection.

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.