China Finance Review International最新文献

Purpose

Prior studies have found that managers adjust operational activities to tackle climate risk. However, the effects of climate risk on accounting practices are largely ignored in the literature. This paper investigates whether and how climate risk influences managers’ decision-making on the level of accounting conservatism and explains the results based on two competing channels: valuation demand and contracting demand.

Design/methodology/approach

Using firm level climate risk measures, we build a modified Basu (1997) model to conduct our econometric tests. In the baseline model, we use earnings before extraordinary items as the dependent variable, referred to as the earnings model. We control for different levels of fixed effect to identify the shocks of climate risk and mitigate potential concerns on endogeneity and bias in the model. A series of robustness tests provide supporting evidence for our baseline results and our explanation.

Findings

Using a sample of 35,832 firm-year observations on listed US firms over the period 2002 to 2019, we find that the perception of climate risk drives managers to choose the less conservative accounting policies. We conclude that the results are consistent with the valuation demand explanation but inconsistent with the contracting demand explanation.

Originality/value

The study provides additional evidence on how managers respond to climate risk by adjusting their corporate polices, specifically accounting policies. Our findings contradict the results of prior studies. We explain our results from a unique perspective. Overall, the study provides valuable insights for academics, investors, managers and policymakers.

Purpose

We phrase our analysis around the connectedness effects and portfolio allocation in the “Carbon-Energy-Green economy” system.

Design/methodology/approach

This paper utilizes the TVP-VAR method provided by Antonakakis et al. (2020) and Chatziantoniou et al. (2021), and portfolio back-testing models, including bivariate portfolios and multivariate portfolios.

Findings

Firstly, the connectedness within the “Carbon-Energy-Green economy” system is strong, and is mainly driven by short-term (weekly) connectedness. Notably, the COVID-19 pandemic leads to a vertical increase in the connectedness of this system. Secondly, in the “Carbon-Energy-Green economy” system, most of the sectors in the green economy stocks tend to be the transmitters of shocks to other markets (particularly the energy efficiency sector), while the carbon and energy markets are always the recipients of shocks from other markets (particularly the crude oil market). Thirdly, Green economy sector stocks have satisfactory hedging effects on the market risk of carbon and energy assets. Interestingly, hedging risks in relatively “dirty” assets requires more green economy stocks than in relatively “clean” assets. Finally, the results indicate that portfolios that include green economy stocks significantly outperform portfolios that do not contain green economy stocks, further demonstrating the crucial role of green economy stocks in this system.

Originality/value

Understanding the interactions and portfolio allocation in the “Carbon-Energy-Green economy” system, especially identifying the role of the green economy performance in this system, is important for investors and policymakers.

Purpose

This study aims to investigate the impact of climate risk disclosure by listed companies on the entry of green investors. It seeks to understand how proactive climate risk disclosure can attract green investment and the underlying mechanisms that facilitate this process.

Design/methodology/approach

Textual analysis is employed to assess the extent of climate risk disclosure in annual reports. The research constructs indicators for green investor entry and applies regression analysis to examine the relationship between climate risk disclosure and green investment, considering various mediating variables such as positive online news coverage, ESG scores, and corporate reputation.

Findings

Green investors are more likely to invest in companies with higher levels of climate risk disclosure. This relationship is robust across different types of firms, with non-state-owned, non-high-tech, large-scale firms, and those in the Eastern region showing a stronger attraction to green investors. Climate risk disclosure promotes green investment through the “signal transmission” mechanism, enhancing corporate reputation and ESG performance.

Originality/value

This paper extends the traditional theory of external incentives for corporate green development to include autonomous incentives through active climate risk disclosure. It provides new insights into the theory of corporate sustainable development and offers practical recommendations for enhancing corporate green development pathways. The study’s comprehensive approach and use of extensive data contribute valuable knowledge to the field of green investment and corporate sustainability.

Purpose

This study attempts to examine the time-varying volatility spillovers between environmentally sustainable assets and quantify the value-at-risk of the portfolios across various frequencies.

Design/methodology/approach

To accomplish these objectives, this paper utilizes a connectedness index-based TVP-VAR model and applies the wavelet-based VaR ratio to daily data spanning from January 2018 to September 2023.

Findings

The empirical findings reveal a notable increase in the connectedness index between green stocks and green bonds during the COVID-19 crisis, signifying evidence of a contagion effect. The portfolio’s risk ratio also exhibited a sharp rise amid the pandemic, particularly over medium and long-term horizons, driven by increased spillover among green assets. Notably, our analysis indicates that green bonds influence the connectedness system between green stocks and the value-at-risk ratio, reducing volatility spillover and portfolio risk ratios across various investment horizons. These results highlight the role of green bonds as an effective diversification asset against the risks associated with green equities.

Originality/value

This research investigates the dynamic connectedness and value-at-risk ratio between eight green sectoral renewable energy and non-energy equities and green bonds. We put forward some portfolio implications for green investors with an environmental consciousness who desire to decarbonize their portfolios and mitigate environmental issues.

Purpose

This study aims to establish an analytical framework to help investors accommodate their environmental, social, and corporate governance (ESG) preferences. The analytical solutions were complemented by empirical analyses to shed light on their benefits and tractability.

Design/methodology/approach

This study proposes an expected multi-attribute utility analysis for ESG investors in which stocks can be treated as more green or less green (brown) than the market, represented by an index, all modeled in a one-factor structure. The solution is found via the Hamilton-Jacobi-Bellman (HJB) equation with proper treatment of various sources of risk. For the empirical analysis, we use the RepRisk Rating of US stocks from 2010 to 2020 to select companies that are representative of various ESG ratings.

Findings

This study finds closed-form solutions for optimal allocations, wealth and value functions. Our empirical analysis reveals drastic increases in wealth allocation toward high-rated ESG stocks for ESG-sensitive investors, even as the overall level of pecuniary satisfaction remains unchanged.

Originality/value

This study broadens the existing analytical framework by introducing a market portfolio along with green and brown stocks. As by-products, we first demonstrate that investors do not need to reduce their pecuniary satisfaction to increase green investment. Second, we propose a parameterization to capture investors' preferences for green assets over brown or market assets, independent of asset performance.

Purpose

This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations of these models in handling temporal information in financial time series.

Design/methodology/approach

Employing a robust methodological framework, the study systematically compares a range of Transformer models, including first-generation and advanced iterations like Informer, Autoformer, and PatchTST, against benchmark models (HAR, NBEATSx, NHITS, and TimesNet). The evaluation encompasses 80 different stocks, four error metrics, four statistical tests, and three robustness tests designed to reflect diverse market conditions and data availability scenarios.

Findings

The research uncovers that while first-generation Transformer models, like TFT, underperform in financial forecasting, second-generation models like Informer, Autoformer, and PatchTST demonstrate remarkable efficacy, especially in scenarios characterized by limited historical data and market volatility. The study also highlights the nuanced performance of these models across different forecasting horizons and error metrics, showcasing their potential as robust tools in financial forecasting, which contradicts the findings of Zeng et al. (2023)

Originality/value

This paper contributes to the financial forecasting literature by providing a comprehensive analysis of the applicability of Transformer-based models in this domain. It offers new insights into the capabilities of these models, especially their adaptability to different market conditions and forecasting requirements, challenging the existing skepticism created by Zeng et al. (2023) about their utility in financial forecasting.

Purpose

The present study aims to systematically synthesize the academic and industrial literature on multi-central bank digital currencies (m-CBDCs) arrangements.

Design/methodology/approach

The study adopted a unique multivocal literature review methodology that considers both white and grey literature. For white literature searches, the study relied on Scopus, Web of Science (WOS), and Google Scholar bibliometric databases; for grey literature searches, the study used the Google search engine.

Findings

The findings of the study illustrated that M-CBDC arrangements, through various design options, have the potential to revolutionize the contemporary international payment system. M-CBDC arrangements will lead to more integrated financial systems and promote economic growth. However, m-CBDC arrangements will also have serious macroeconomic implications, such as contagion and currency substitution risks.

Research limitations/implications

The present review is one of the earliest reviews of m-CBDC arrangements. In addition, the findings of the study offer valuable insights for both academicians and policymakers.

Originality/value

The study is also one of the pioneer studies in management studies that apply a multivocal literature review methodology.

Purpose

Stock markets are essential for households for wealth creation and for firms for raising financial resources for capacity expansion and growth. Market participants, therefore, need an understanding of stock price movements. Stock market indices and individual stock prices reflect the macroeconomic environment and are subject to external and internal shocks. It is important to disentangle the impact of macroeconomic shocks, market uncertainty and speculative elements and examine them separately for prediction. To aid households, firms and policymakers, the paper proposes a granular decomposition-based prediction framework for different time periods in India, characterized by different market states with varying degrees of uncertainty.

Design/methodology/approach

Ensemble empirical mode decomposition (EEMD) and fuzzy-C-means (FCM) clustering algorithms are used to decompose stock prices into short, medium and long-run components. Multiverse optimization (MVO) is used to combine extreme gradient boosting regression (XGBR), Facebook Prophet and support vector regression (SVR) for forecasting. Application of explainable artificial intelligence (XAI) helps identify feature contributions.

Findings

We find that historic volatility, expected market uncertainty, oscillators and macroeconomic variables explain different components of stock prices and their impact varies with the industry and the market state. The proposed framework yields efficient predictions even during the COVID-19 pandemic and the Russia–Ukraine war period. Efficiency measures indicate the robustness of the approach. Findings suggest that large-cap stocks are relatively more predictable.

Research limitations/implications

The paper is on Indian stock markets. Future work will extend it to other stock markets and other financial products.

Practical implications

The proposed methodology will be of practical use for traders, fund managers and financial advisors. Policymakers may find it useful for assessing the impact of macroeconomic shocks and reducing market volatility.

Originality/value

Development of a granular decomposition-based forecasting framework and separating the effects of explanatory variables in different time scales and macroeconomic periods.

Purpose

This study explores the contagion of greenwashing strategies among ESG mutual funds. It investigates how the greenwashing behaviors of peer funds within the same family influence a fund’s decision to engage in greenwashing. The research also examines the impact of greenwashing on genuine ESG funds and explores the mechanisms through which greenwashing strategies spread across ESG mutual funds.

Design/methodology/approach

This paper employs a two-stage least squares regression model with cross-fund returns standard deviation as an instrumental variable to disentangle the peer effects of greenwashing from family-level characteristics. The analysis incorporates various fund characteristics and introduces four contagion channels through which greenwashing may influence genuine ESG funds.

Findings

The study finds greenwashing behavior in ESG funds is positively influenced by similar practices within their fund family. Larger assets under management and older funds with higher management fees show resilience against greenwashing influences, while team-managed funds are more susceptible. Additionally, socially responsible investors struggle to distinguish between genuine and greenwashing ESG funds, which may contribute to the persistence of greenwashing practices.

Originality/value

This paper contributes to the literature by delineating the mechanisms of greenwashing contagion within ESG mutual funds. It also examines the demand-side incentives for adopting greenwashing strategies, offering insights into the implications for fund flows and investor behavior. This study is among the first to analyze the contagion effects of greenwashing strategies across an extensive network of ESG funds, enriching our understanding of the broader impacts of greenwashing in the context of socially responsible investing.