International Review of Finance最新文献

This study examines the optimal investment, consumption, and life insurance choices faced by a wage earner with recursive preferences within a finite time horizon. We posit that the financial market comprises a risk-free asset and a risky asset that follows a general stochastic volatility model. The objective of the wage earner is to identify optimal investment, consumption, and life insurance strategies that maximize the expected utility of discounted intertemporal consumption, legacy wealth, and terminal wealth throughout the uncertain lifespan. Using the dynamic programming principle, we derive the Hamilton-Jacobi-Bellman (HJB) equation to describe the optimal investment–consumption–insurance strategy and its corresponding value function. By solving the HJB equations, we derive the analytical solutions for the optimal strategy and value function in the cases of the exponential-polynomial form and the Heston's stochastic volatility model. Through numerical simulations, we investigate the impact of several parameters, providing further economic insights obtained in this study.

We study the long-run aftermarket performance of Emerging Growth Companies (EGCs), a new category of IPO issuers created by the Jumpstart Our Business Startups Act (JOBS Act). We document that, in the long run, EGCs outperform by about 25 percentage points, compared to those firms that would have qualified as EGCs had they gone public after the enactment of the JOBS Act. We further find that institutional investors play an important role in supporting the long-run performance of EGCs—not only do EGCs have greater institutional ownership, but the positive effect of institutional ownership on IPO firms' long-run performance is also more pronounced. The paper represents a first attempt to study the long-run aftermarket performance of EGCs and its determinants. Our findings contribute to the burgeoning literature on the effectiveness of the JOBS Act and evidence of the supporting role of institutional investors on IPO aftermarket performance.

While the determinants of foreign portfolio investment (FPI) flows to India have been extensively analyzed, research has largely failed to document the impact and the relative importance of domestic monetary policy shock vis-à-vis other variables in causing FPI flows to India. This study adds to the literature by empirically examining the impact of the domestic monetary policy shock on FPI flows to India using the structural VAR methodology. It further disaggregates the analysis of FPI flows into portfolio equity flows (PEF) and portfolio debt flows (PDF) to investigate whether a domestic monetary policy shock affects the two flows similarly or differently. The study finds that domestic monetary policy shock (measured through shocks to interest rate differential and domestic money supply growth) significantly influences FPI flows to India, explaining about 10.1% of the total variation in these flows. The disaggregated analysis of FPI also reveals similar results for both portfolio equity flows and portfolio debt flows; however, the impact of the domestic monetary policy shock is greater on the debt component of FPI (portfolio debt flows) than on the equity component of FPI (portfolio equity flows).

Extensive research has documented the benefits of agricultural production loans, yet limited studies address the impacts of liquidity constraints during harvest season due to loan repayment obligations. This study examines how agricultural production loans influence farmers' crop storage decisions. Based on a theoretical two-period household model, we demonstrate that the net effect of such loans on grain storage behavior is ambiguous, contingent upon the relative dominance of liquidity constraint relief versus production enhancement incentives. Utilizing data from the China National Fixed-Point Survey (2004–2014), our empirical analysis using fixed-effect models corroborates the theoretical predictions. Specifically, we find that agricultural production loans negatively affect wheat storage and the storage-to-output ratio for wheat. Conversely, these loans positively correlate with storage levels and storage-to-output ratios for maize and soybeans. Additionally, our results indicate a negative impact on rice storage but no significant effect on the rice storage-to-output ratio. Further analysis reveals that, compared with wheat and rice, maize and soybeans have a higher output return of capital input. Furthermore, we find agricultural production loans raise household income and consumption.

This paper examines the effect of corporate governance on the relationship between performance and market competition. We use changes in import tariffs as exogenous shocks to product market competition to examine how the effect of these shocks on performance varies with corporate governance. We find that elevated product market competition due to import tariff cuts results in significant performance deterioration for firms with strong corporate governance, but little effect or even a significant positive effect on performance for firms with poor governance. We further find that these effects are much stronger for nonexporting firms and small firms because they do not have the opportunity/resources to soften the impact of increased competitive pressure. Our findings suggest that elevated market competition helps to reduce managerial entrenchment/slack, which can offset the adverse effects of increased competitive pressure, especially for nonexporting firms and small firms. The findings lend support to the hypothesis that managerial slack is inversely related to industry competitive pressure and, hence, that product market competition and internal corporate governance are substitutes for each other.

This paper addresses firm fraud detection by synergizing domain knowledge with machine learning through heuristic and explainable feature construction. Unlike traditional approaches that focus on algorithmic improvements, our method introduces a set of features based on earnings management analysis, providing factors influencing firm fraudulent behavior. Empirical results using a firm-year dataset from China demonstrate better classification accuracy of fraud detection compared to machine learning models with raw financial statement features alone. Additionally, the results remain robust with different false positive rates, observation periods, and firm groups. This domain knowledge-enhanced machine learning method, with alternative features for fraud detection, leads to more transparent regulation and the potential for similar counterfeit detection applications in China and beyond.

We propose a model of corporate governance that characterizes the optimal managerial compensation scheme when the board of directors can monitor by auditing. The results reveal a significant substitution effect such that when monitoring is sufficiently weak, the optimal compensation depends on performance, but as it grows stronger, the incentive power of compensation declines. When monitoring is sufficiently strong, however, the compensation gives the manager full insurance. These properties are consistent with the reduction in executive pay-performance sensitivity observed after U.S. regulatory reforms aimed at improving corporate internal control.

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.