North American Journal of Economics and Finance最新文献

With the continuous development of the global digital economy, digital transformation has become an important path for enterprises. The management discussion and analysis (MD&A) section of listed companies’ annual reports has progressively incorporated detailed information on their ongoing digital transformation efforts, reflecting the growing significance of this trend in corporate reporting. This paper uses data from 2011 to 2022 of A-share listed companies in China to investigate whether and how the MD&A digital transformation information affects the stock price synchronicity. The frequency of keywords related to digital transformation within the MD&A section of the annual report is analyzed and measured to quantify digital transformation information. The results show that digital transformation information in MD&A can significantly reduce stock price synchronicity. Further mechanism tests show that analyst and media act as information intermediaries to disseminate more firm-specific information to the market. In addition, heterogeneity tests show that this effect is mainly reflected in state-owned companies and larger companies. These findings provide valuable insights for policymakers, investors, and regulators in planning regulations.

This study conducts a comprehensive analysis of the static correlation between the Chinese green bond market and key capital markets-including the stock, money, foreign exchange, and gold markets—using daily data spanning from 2013 to 2022. Utilizing maximum likelihood estimation methods, our findings indicate that the Student’s t Copula model is the most suitable for capturing these relationships, revealing a relatively low static correlation among these markets. Furthermore, for dynamic dependence analysis and cross-validation, the Student’s t-GAS Copula model is applied, which corroborates the initial findings. Consequently, this suggests that the Chinese green bond market could become one of the potentially diversification options for investing in the Chinese financial landscape.

Abbreviations: ICMA, International Capital Market Association; GB, green bonds; CB, China Bond Government Bond Total Return Index; HS300, CSI 300 Index; DR7, 7-day interbank pledged repo rate; CNY, Onshore RMB to USD exchange rate; AU95, AU9995 gold price.

This paper investigates the tail risk spillover effects among the stock and foreign exchange markets of G20 economies, as well as the oil and gold markets by constructing a tail event driven network. Adjacency matrices indicate time-varying connectedness between network nodes. The systemic risk decomposition results highlight the predominant contribution of stock markets to the aggregate risk level, while oil, gold, and specific currencies such as JPY, USD, and CNY contribute to diversifying systemic risk. Moreover, tail event driven network quantile regression analysis demonstrates the asymmetry and market heterogeneity of risk spillover effects. Our findings have instructive implications for financial regulators and institutional investors.

This paper builds upon previous research findings that show macro sentiment data-augmented models are better at predicting the yield curve. We extend the dynamic Nelson–Siegel model with macro sentiment data from either Twitter or RavenPack. Vector autogressive (VAR) models and Markov-switching VAR models are used to predict changes in the shape of the yield curve. We build bond butterfly trading strategies that exploit our yield curve shape change predictions. We find that the economic returns from our trading strategies based upon models exploiting macro sentiment data do not statistically significantly differ from those which do not rely on it.

In this paper, we present a two-step methodology designed to elucidate the extent of co-movement in the behavior of asset bubbles across a selected group of developed and emerging economies, with observations extending from the year 2000 onwards. We characterize the behavior of these bubbles, conceptualized as the disparity between fundamental and market values, by examining their responses to monetary policy shocks. Our investigation into their co-movement dynamics post-2000 involves the estimation of a Bayesian Dynamic Factor Model that incorporates the reactions of asset bubbles to monetary policy shocks, both in the short term and the long term. Notably, our findings reveal a convergence in short-term responses among these bubbles prior to the onset of the financial crisis. However, as the crisis unfolded, our results indicate a shift towards divergence. Concerning the long-term response of bubbles to monetary policy, divergence was observed before the crisis, but once the crisis had taken hold, there was a noticeable trend towards convergence. An influential idiosyncratic factor was identified for countries like the United States, South Korea, and Japan, impacting both short-term and long-term behavior. Conversely, the long-term behavior of bubbles in most emerging economies appears to be influenced by idiosyncratic factors. Furthermore, our results largely maintain their robustness even when utilizing an alternative measure of monetary policy stance during the period of the zero lower bound. Nevertheless, this secondary analysis suggests heightened volatility in the factors affecting both emerging and advanced markets.

This paper aims to assess the dynamic linkages between crude oil futures contracts, renewable energy indices, carbon credit futures indices and several US sector market indices by applying the DECO-GARCH model and the connectedness index of Diebold and Yilmaz (2012). The analysis is conducted on a daily data sample from August 2014 to February 2024 and performed at both the aggregated and disaggregated levels. The disaggregated analysis revealed that the correlation between the variables was lower before periods of stress, such as the COVID-19 crisis and the Russian–Ukrainian conflict as well as a complex correlation structure with a diverse mix of positive and negative values between different pairs of variables. Moreover, the static connectedness results in terms of returns underscore a significant degree of interconnection and transmission of shocks between oil prices and sector markets, renewable energy, and carbon credit futures indices. In addition, the results highlight the responsiveness of the clean energy and carbon credit sectors to global circumstances and economic conditions. The study concludes with a dynamic connectedness analysis, highlighting once again the intricate connections and interactions between all the variables, which are exacerbated during periods of market instability and key events. The net interconnectedness analysis demonstrated that changes in crude oil prices have a significant impact on most of the variables analyzed. The findings of this study have clear implications for a wide range of market participants, policy-makers, and individuals managing portfolios, particularly in terms of diversification opportunities.

This paper employs the quantile autoregressive (QAR) model to examine the forecasting relationship between stock volatility and crude oil volatility. We first utilize the sup-Wald test to evaluate Granger causality across various quantile levels, providing valuable information for forecasting. Our findings reveal that the causal effects between stock volatility and crude oil volatility differ considerably across different quantiles, with a V-shaped relationship evident at the quantile level. Results from out-of-sample forecasts indicate that the forecasting effect of oil volatility on stock volatility has both positive and negative impacts. In contrast, when using stock volatility to forecast crude oil volatility, predictability improves relative to the benchmark, particularly at more extreme quantiles. Further analysis highlights the necessity of forecast combinations to achieve an overall improvement in forecasting tasks.

We examined environmental, social, and governance (ESG) pricing in the Chinese A-share market. The results indicate that, on average, investors holding stocks with high ESG scores do not earn higher abnormal returns. Conversely, stocks with low ESG scores perform better. In terms of ESG components, the ESG discount in the current Chinese A-share market is primarily manifested as a governance discount. On the other hand, we investigated the sources of ESG discounts. The findings reveal that the ESG discount is unrelated to most risk characteristic variables but is associated with size, liquidity, and investors’ ESG preferences, with size having the greatest impact. Based on our results, we suggested that for small-scale companies, investors may view good ESG performance as a signal of risk mitigation; for large-scale companies, good ESG performance may be viewed as a value signal.

This study investigates the relationship between investor sentiment (IS) and the green bond market by employing bootstrap full- and sub-sample rolling-window Granger causality tests. The findings suggest a favourable impact of IS on the Green Bond Index (GBI), indicating that investors maintain an optimistic outlook on the bond market, thereby driving market expansion. This outcome is supported by the intertemporal capital asset pricing model, demonstrating an advantageous effect of IS on GBI. The beneficial and detrimental effects of GBI on IS indicate that investors is appropriate strategically modify their investment portfolios to adapt to marketplace instability. Amidst ongoing global economic and financial market uncertainties, green bonds have emerged as crucial assets for investors. Consequently, when shaping fiscal strategies, governments ought to consider the influence of IS in stimulating investor enthusiasm for green investments.

This paper has explored whether and how climate risk affects corporate ESG performance in China. Using the text analysis, we construct a firm-level climate risk index and find that climate risk can promote corporate ESG performance. Empirical evidence demonstrates that financing constraints and social attention are the potential influence channels. Furthermore, the positive relationship is more pronounced for firms with greater regional government attention to climate change, better information disclosure quality, or a higher asset-liability ratio. Our results shed light on the important role of climate risk in corporate risk management and investor decision-making.