International Review of Financial Analysis最新文献

This study employs novel corporate environmental, social, and governance profiles to investigate the industry effects of environmental and social (ES) scandals in China. The findings reveal a notable decrease in stock prices for rival firms during scandal announcements. Further, we document a significant, positive correlation between rivals' ES performance and the abnormal returns over a five-day period surrounding the scandals. This correlation is more pronounced in rivals that disclose ES information. Additionally, relative to high-performing rivals, those with weaker ES performance significantly enhance their ES performance in the following year, driven by the perceived ES value in industry scandals. The findings also underscore the influence of state ownership, external governance environment, and industry competition on the spillover effects of ES scandals via risk channels.

We investigate the impact of forward-looking disclosures in annual reports on stock liquidity in China. Analysis of the MD&A sections within annual reports demonstrate a strong positive correlation between forward-looking disclosures and a company's stock liquidity. This promotional effect appears more pronounced within high-tech companies and those with lower levels of information transparency. Mechanistic tests indicate that the increase in equity liquidity attributable to forward-looking disclosures can be traced to heightened interest from analysts and media coverage. Further examination of the impact of MD&A textual characteristics reveals that improvements in the readability and tone of the text strengthen the effect of forward-looking information on enhancing stock liquidity. Economic consequence tests show that forward-looking disclosures not only enhance stock liquidity but also contribute to expanding investment scale, reducing financing costs, and improving both future firm performance and market valuation. These findings suggest that augmenting the efficiency of capital market information dissemination could significantly bolster financial support for the real economy.

This paper assesses the utility of machine learning (ML) techniques combined with comprehensive macroeconomic and microeconomic datasets in enhancing risk analysis during stress tests. The analysis unfolds in two stages. I initially benchmark ML’s efficacy in forecasting two pivotal banking variables, net charge-off (NCO) and pre-provision net revenue (PPNR), against traditional linear models. Results underscore the superiority of Random Forest and Adaptive Lasso models in this context. Subsequently, I use these models to project PPNR and NCO for selected bank holding companies under adverse stress scenarios. This exercise feeds into the Tier 1 common equity capital (T1CR) densities simulation. T1CR is the equity capital ratio corrected by some regulatory adjustments to risk-weighted assets. Crucially, findings reveal a pronounced left skew in the T1CR distribution for globally systemically important banks vis-à-vis linear models. By mirroring distress akin to the Great Recession, ML models elucidate intricate macro-financial linkages and enhance risk assessment in downturns.

Extreme natural disasters, such as tropical cyclones, have a low probability of materialising, but a high social and economic impact, including spillover to financial institutions. We propose a framework for performing a climate-stress testing exercise for the default probability of mortgage loans. We estimated a dynamic credit scoring model based on survival analysis with a relative damage index built using the wind speed of tropical cyclones. We considered scenarios involving tropical cyclone wind speeds with different return periods. We analyse a portfolio of approximately 190,000 mortgage loans granted in Louisiana, one of the US states most affected by tropical cyclones. Our findings suggest that coastline areas are most exposed to severe damage from tropical cyclones. If the geographical area is exposed to an event with a very large return period of 1-in-1,000 years, the probability of default increases by approximately nine percentage points compared to a baseline scenario in the absence of tropical cyclones. However, this finding was mitigated by the insurance coverage. This percentage increases to almost 20 percent in the absence of insurance coverage.

We investigate the impact of mixed ownership reform (MOR) intensity on trade credit obtained by state-owned enterprises (SOEs) in China from 2009 to 2021. Drawing on a novel, hand-collected database, we find that MOR intensity has a significantly negative effect on trade credit. The path analyses show that financial constrains and corporate profitability are the channel of our main finding. Moreover, the negative relationship between MOR intensity and trade credit is more pronounced for central SOEs, firms facing weaker market competition or higher supplier concentration. This study explores the causes of an SOE's trade credit demand and the consequences of MOR in China.

This study examines the impact of administrative monopoly on corporate innovation, specifically focusing on the Fair Competition Review System (FCRS) implemented in China. Based on Chinese A-share listed firms from 2012 to 2020, we use the implementation of the FCRS as a natural experiment to conduct a difference-in-difference test. Our findings show that the FCRS significantly increases the level of innovation among SOEs through the mechanisms of resource acquisition and market competition. The incentive effect is not only at the strategic innovation level but also promotes the improvement of the substantive innovation level of SOEs. The heterogeneity test indicates that improvements stemming from the FCRS are more pronounced in specific functional categories, regions with poor business environments, and state-owned enterprises in industries that receive key policy support. Finally, our study also reveals that the FCRS promotes the input-output efficiency of innovation among SOEs. This research contributes to the literature by filling the research gap on the impact of administrative monopoly on corporate innovation, providing novelty evidence on the economic consequences of regulatory administrative monopoly, and offering policy insights regarding the FCRS.

In this paper we develop a framework to analyze high-frequency (HF) financial transaction data focused on estimating a multidimensional intraday liquidity measure and detecting rare events. Many liquidity measures based on Trade and Quote (TAQ) and Limit Order Book (LOB) datasets are consolidated for this purpose through dimensionality reduction techniques. Several outlier methods based on extreme value theory, distance-based outlier methods, and tree-based algorithms are implemented to identify clusters of rare liquidity events. These methods provide insights into the behavior and occurrence of outliers. The methodology is optimized for HF intraday implementation. The framework is applied to transaction level data covering the beginning of COVID-19 outbreak period. We observe that after peak news activity, high-volume stocks experience extreme low-liquidity events almost immediately, while low-volume stocks have a time delayed reaction. The behavior of a select number of tickers is analyzed in detail over the outbreak period. The framework proposed can detect extreme liquidity events in real time and thus can be used to monitor market activity and provide early warnings about liquidity trends. A new intensity indicator measure is developed to assess and visualize extreme liquidity events.

Green bonds attract increasing attention as a new eco-friendly investment product. We explore the heterogeneous impact of low-frequency economic and political uncertainty across high or low uncertainty states on green bond volatility in order to accurately analyze the green bond market risk. To this end, we propose a new Markov regime switching GARCH-MIDAS-Skewed T model, in which the regime switching behavior occurs on the low-frequency long-term volatility. An effective filtering estimation method is put forth by introducing the likelihood of the low-frequency sub-sample set. The evidence supports that there are significant time-varying and state-dependent impacts from uncertainty shocks on the volatility of green bonds, including monetary policy, inflation, and crude oil prices as well as global economic policy and political environment. In addition, we find the counter-cyclical behavior of green bond volatility, which increases in the period of economic recession or financial turbulence with expanding uncertainty. Improving the hedging ability of green bonds against uncertainty risks effectively contributes to low-carbon economic development.

This paper studies how the banking system was affected during the crisis by investigating risk transmission and shock impact mechanisms. After classifying the sample banks in China into state-owned commercial banks (SOCBs), joint-stock commercial banks (JSCBs), and city commercial banks (CCBs), we find that SOCBs exhibit higher systemic risk spillovers, and they are more likely to act as risk transmitters; JSCBs tend to have higher conditional at-risk values, with some assuming the role of risk transmission; CCBs have smaller results for both factors and tend to act as risk takers. We also examine how banks with different ownership structures respond to external shocks and find that SOCBs have the fastest and most accurate judgment, while CCBs are slower and more prone to bias. Finally, we observe that the investor sentiment of bank categories containing risk transmitters is more sensitive to external shocks than those consisting entirely of risk takers.

In recent years, with the emergence of blockchain technology, we have witnessed a remarkable increase in the use of digital currencies. However, investing in the digital currency market carries a high level of risk due to the market's erratic behavior and high price fluctuations. Consequently, the need for an appropriate model for intelligent prediction and risk management is perceived. Motivated by the above subject, we propose a novel approach based on a deep neural network with a focus on error patterns. The proposed approach is based on the theory of non-random walks and assumes that there are predictable components in the price movements of cryptocurrencies. This new approach attempts to improve prediction results by modeling residual values and incorporating their impact on the main predictions. The time scope of this research is from October 31, 2018, to December 30, 2023, on a daily basis, spanning Five years. In this study, we utilized Long Short-Term Memory (LSTM) as the main prediction model and Vector Autoregression (VAR) for forecasting noise in three well-known cryptocurrencies: Bitcoin, Ethereum, and Binance Coin (BNB). The results indicate that the proposed approach has been able to enhance the predictions.