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Little is known about how executive gender shapes the inherent conflict of interest between shareholders and bondholders. Using a sample of almost 100,000 unique bond‐year observations, this study investigates how the appointment of female chief executive officers (CEOs) lowers the default outlook. Our evidence indicates that bond yield and bond volatility are significantly lower after a female takes the helm at a firm. This executive gender effect remains highly statistically and economically significant across various robustness checks and after addressing endogeneity concerns. Female CEOs lower the default risk component of the bond yield but have no material impact on the liquidity component. Subsample analysis substantiates the conditional effect of female CEOs on bond yield and bond volatility. Our evidence indicates that female CEOs' risk‐averse attributes pass through the credit risk and information asymmetry channels. [ABSTRACT FROM AUTHOR]

This study investigates the determinants of trading activity in the U.S. corporate bond market, focusing on the effects of Seasonal Affective Disorder (SAD) and macroeconomic announcements. Employing the General-to-Specific (Gets) Autometrics methodology, we identify distinct behavioral responses between retail and institutional investors to SAD, noting a significant impact on retail trading volumes but not on institutional trading or bond returns. This discovery extends the understanding of behavioral finance within the context of bond markets, diverging from established findings in equity and Treasury markets. Additionally, our analysis delineates the influence of macroeconomic announcements on trading activities, offering new insights into the market's reaction to economic news. This study's findings contribute to the broader literature on market microstructure and behavioral finance, providing empirical evidence on the interplay between psychological factors and macroeconomic information flow within corporate bond markets. By addressing these specific aspects with rigorous econometric techniques, our research enhances the comprehension of trading dynamics in less transparent markets, offering valuable perspectives for academics, investors, risk managers, and policymakers. [ABSTRACT FROM AUTHOR]

This paper explores the informational role of the Loan Only Credit Default Index (LCDX) on the pricing of syndicated loans. Despite an extensive body of research on credit indices and loan pricing, limited studies have comprehensively assessed the complex relationship between the LCDX and individual loan spreads. Contrary to indices like the CDX, which are largely linked to corporate bonds, the LCDX directly pertains to the syndicated secured loan market, offering valuable insights about the overall credit default market and the cost of credit risk insurance. Preliminary results reveal a pronounced positive correlation between the LCDX spread and the syndicated loan spread, particularly noticeable amongst borrowers with lower credit quality. The paper highlights the LCDX's pivotal role in conveying secondary credit market information, with critical implications for credit risk management and financial regulations. [ABSTRACT FROM AUTHOR]

This study uses disaggregated establishment-level data to identify a firm's exposure to physical climate risk and examines investors' reaction to natural disasters in both the U.S. corporate bond and stock markets. We find that, when a firm is exposed to disasters, investors overreact by depressing the current bond and stock prices, causing future returns to be higher. However, firms with a strong environmental profile experience lower selling pressure on their bonds and stocks, although their fundamentals weakened following disasters. The evidence suggests that corporate investment in improving environmental profiles pays off when climate change risk is materialized. This paper was accepted by Colin Mayer, Special Section of Management Science on Business and Climate Change. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2021.4018. [ABSTRACT FROM AUTHOR]

With the aim of effectively preventing and controlling systemic risk, by stimulating the advancement of the green bond market, it is significant and imperative to help investors and policymakers adopt more effective measures, which will ensure them to maximize profit. We construct VAR, DCC-GARCH and Copula-CoVaR models, and study the spillover effect between the green bond market and traditional bond market from the three perspectives of mean spillover, volatility spillover and extreme risk spillover using the data on daily closing prices of green bond market and traditional bond market indices. The research findings of this paper are as follows: (1) There are three spillover effects of mean value, volatility and extreme risk among the green bond market, corporate bond market, enterprise bond market and conventional bond market. (2) From the perspective of mean spillover between markets, only the mean spillover between the conventional bond market and the green bond market is bidirectional, and there is the profoundest impact of spillover from the green bond market to the conventional bond market. (3) As far as the volatility spillover between markets is concerned, the volatility spillover between the three traditional bond market and the green bond markets are all positive. The volatility spillover between the conventional bond market and the green bond market is the largest, which is particularly obvious in the first half of 2018 and the first half of 2020. (4) In terms of inter-market extreme risk spillover, the risk spillover between the green bond market and the traditional bond market is positive. The green bond market contributes more to the risk spillover of the enterprise bond market, and it has a time-varying risk spillover effect on the traditional bond market. [ABSTRACT FROM AUTHOR]

Prior studies document the role social media information plays in the stock market and the important dissimilarities between the bond and stock markets. Bridging these two types of literature, we examine the role of social media information in the corporate bond market. Analyzing a broad sample of messages by Twitter individual users, posted just prior to earnings announcements, containing bond, credit risk, and fundamental information, we find that aggregate Twitter opinion (OPI) predicts upcoming announcement bond returns and changes in credit default swap (CDS) spreads, and is associated with future changes in bond yield spreads and credit ratings, thereby providing economically important information to the bond market. This interpretation is bolstered by results from a variety of cross-sectional analyses. Finally, we document an association between OPI and future changes in default risk, which casts light on the nature of the Twitter information underlying our findings. Overall, our findings demonstrate that Twitter appears to disseminate potentially economically important information to even the presumably sophisticated bond and CDS investors, as well as information intermediaries. This paper was accepted by Suraj Srinivasan, accounting. Funding: P. Mohanram acknowledges financial support from the Social Sciences and Humanities Research Council (SSHRC) of Canada. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4589. [ABSTRACT FROM AUTHOR]

The current study explores the problem of illiquidity in the corporate bond market globally and proposes a solution to enhance liquidity by studying various dimensions of liquidity. Purpose: The purpose of this paper is to propose a solution to the global issue of illiquidity in the corporate bond market. The problem has been identified by many researchers and this paper attempts to find a viable solution in the "fungibility route" as an alternative to the "liquidity route". Design/Methodology: An analysis of a sample size of 234,772 trade data of corporate bonds and a sample size of 2,00,607 trade data of G-securities is performed to identify the problem. Findings/Solution proposed: A mathematical model based on the credit risk differential and sensitivity differential is proposed to find out the fair value at which an illiquid bond can be exchanged with a liquid bond. To arrive at the fair value of the illiquid bond, we have calculated the risk-adjusted yield (RAY) using the modified duration and a credit risk differential. Originality: This research is a pioneering effort in addressing the worldwide issue of corporate bond illiquidity by proposing a novel solution. The proposed strategy aims to improve the liquidity of the bond indirectly, by utilizing the fungibility route. [ABSTRACT FROM AUTHOR]

This article studies the issues related to the economic development of the Lower Angara region located in Krasnoyarsk Territory, analyzes the state programs for the development of the Soviet and post-Soviet era territory, and makes an attempt to determine the reasons for the unsuccessful implementation of it. Based on the conclusions made, a new approach to the socio-economic development of the area has been proposed. It consists in the application of a number of specialized industrial clusters on the territory. It is proposed to use bond financing for the financial basis of its formation. To substantiate it, the analysis of the current state of the national bond market in terms of volumes of issuance, maturity has been made and segments of the corporate bond market in the country have been considered. It is suggested that the use of an appropriate financial infrastructure would make the bond financing mechanism operation more effective.

We derive a firm's debt issuance policy when managers have an informational advantage over creditors and face debt restructuring costs. In our model, regardless of how poor their private signal is, managers of firms that can access the credit market avoid default by issuing new debt to service existing debt. Therefore, only bonds of firms that have exhausted their ability to borrow are subject to jump-to-default risk because of incomplete information and, in turn, command a jump-to-default risk premium. We document that our model captures many salient features of the corporate bond market. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4529. [ABSTRACT FROM AUTHOR]

Daily returns of the European corporate bond market are predicted using a penalized Lasso regression or Random forests. Predictions are utilized in investment strategies that allocate resources into risky position: interest-rate hedged corporate bonds or unhedged corporate bonds and risk-free position proxied by the Euro-Bobl futures that track the German government bonds. The strategies are more profitable with a lower risk than their passive alternatives, which only invest in corporate bonds (hedged or unhedged), but the daily rebalancing can be costly. Therefore, we examine the break-even transaction costs and suggest two approaches to lower the overall costs. Overall, even with costs, active strategies based on prediction can achieve higher returns with a lower risk. [ABSTRACT FROM AUTHOR]

Corporate bonds with large increases in implied volatility over the past month underperform those with large decreases in implied volatility by 0.6% per month. In contrast to existing studies that show implied volatility changes carry information about fundamental news, our evidence suggests that implied volatility changes contain information about uncertainty shocks to the firm. Our results are consistent with the notion that informed traders with new information about firm risk prefer to trade in the option market and the corporate bond market underreacts to this information. This paper was accepted by Haoxiang Zhu, finance. Funding: We thank the Canadian Derivatives Institute for financial support. J. Cao and X. Zhan acknowledge generous financial support of the Research Grant Council of the Hong Kong Special Administrative Region, China [Grants GRF 14501720, 14500919]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4379. [ABSTRACT FROM AUTHOR]

How does uncertainty affect the costs of raising finance in the bond market and via bank loans? Empirically, this paper finds that heightened uncertainty is accompanied by an increase in corporate bond spreads, whereas spreads on bank loans remain unchanged. This finding can be explained with a model that includes costly state verification and in which banks maintain long‐term relationships with borrowers and acquire information beyond what is publicly available. After an unexpected increase in uncertainty, the probability of borrower default increases. Banks leave the loan spread unchanged to maintain the relationship. In contrast, bond spreads increase because investors demand compensation for the increased default risk. [ABSTRACT FROM AUTHOR]

The green bond market helps to mobilize financial sources toward sustainable investments. Green bonds are similar to conventional bonds but are specifically designed to raise money to finance environmental projects. The feature of green bonds is the existence of greenium, or the lower yield compared to "conventional" bonds of the same risk. The relevance of the paper is underpinned by the mixed evidence on the existence of 'greenium', especially in corporate green bond markets; there has been limited research on the topic and a narrow focus on global, US, or Chinese green bond markets. Instead, the greenium in European debt markets remains underexplored. The objective of this study is to investigate the existence of greenium and its key determinants in European corporate debt capital markets, including the local markets of the United Kingdom (UK), France, Netherlands, and Germany. The sample included 3851 corporate bonds, both green and conventional ones, between 2007 and 2021 from 33 European countries. Linear regression was applied for the analysis. The results show that the climate corporate bonds in Europe are priced at a discount to the same-risk conventional corporate bonds. The magnitude of greenium is around 3 bps. Determinants of greenium include the presence of an ESG rating and belonging to the utility and financial industry. The remaining drivers of bond yields in the European corporate debt market are the credit quality (expressed by the level of credit rating), the coupon size, the bond tenor, the market liquidity, and macroeconomic variables (growth of gross domestic product and consumer price index). For the local corporate debt markets, our results are controversial. In all markets under consideration except for the UK and the Netherlands, we did not find sustainable evidence of greenium. The results of the research lead to a better understanding of the green bond market for investors, researchers, regulators, and potential issuing companies. [ABSTRACT FROM AUTHOR]

The green bond market shows good potential to help mobilize financial sources towards sustainable investments. Green bonds are similar to conventional bonds but are specifically created to raise money to finance climate or other environmental projects. The relevance of the paper is underpinned by mixed evidence on the existence of 'greenium in various corporate bond markets and lack of a clear understanding of the green bond yields' determinants. The objective of the paper is to explore the existence and determinants of greenium in Europe. Our sample included 3,851 green and conventional bonds in the European debt markets over the period from 2007 to 2021. The results showed that the climate corporate bonds in Europe are priced at discount to the same-risk conventional corporate bonds. The magnitude of greenium in whole European green bond market was around 4 bps. However, we did not find the significant greenium in the green bond markets of the UK, France, Netherlands, and Germany. The conclusions of the research could lead to a better understanding of the green bond market for investors, regulators, and potential issuing companies. [ABSTRACT FROM AUTHOR]

The Chinese bond market has achieved rapid development over the years. However, since the "rigid payment" in China was broken in 2014, the number and size of bond defaults have climbed up promptly and caused huge volatility in bond and even stock markets. To better manage and control the risk of the Chinese corporate bonds market, deep learning can be used as a helpful tool to predict the corporate default risk. This paper constructs a security warning model based on deep neural networks after a reasonable selection of characteristic indicators. By comparing Multi-Layer Perceptron (MLP) with the logistic regression method, it is found that MLP is more suitable in the security warning model to predict the default risk. And, hyper-parameter analyses and ablation study are conducted to explore the performance and accuracy of the best MLP settings. The experimental results show that the deep learning method accommodating the widely chosen factors in the security warning model is effective in predicting corporate bond defaults in China. [ABSTRACT FROM AUTHOR]

This paper aims to examine the connectedness between green and conventional assets, particularly during the period of economic downturn. Specifically, we examine quantile-based time-varying connectedness between the green bond market and other financial assets using quantile vector autoregression (QVAR) from 9 March 2018 to 10 March 2021. We use daily prices of S&P U.S. Treasury Bond Index, S&P US Aggregate Bond Index, S&P US Treasury Bond Current 10Y Index, S&P 500 Bond Index, S&P 500 Financials index, S&P 500 Energy Bond Index and S&P 500, giving a total of 784 observations, and using Composite Index as a representative of conventional assets classes and S&P Green Bond Index to denote the green bond market. Results shows the connectedness between green bonds and the conventional asset classes intensified during the outbreak of the Coronavirus pandemic (COVID-19) as investors shifted their investment towards fixed income assets due to the plunge in the prices of stocks and commodities. The results also shows that green bonds are strongly connected with treasury bonds, aggregate bonds and bond index, as they share similarities with respect to issuance, risk and governance. Connectedness is weak in the case of composite index and energy bond index, as their prices do not have substantial influence on the green bond market. The study highlights the hedging and diversification benefits of green bonds. We have several implications for portfolio managers, policy makers and researchers. [ABSTRACT FROM AUTHOR]

China's bond market has been ranked third globally; however, China's corporate bonds are significantly less liquid than its stocks. Liquidity risk is an important component in China's corporate bond spreads. In this paper, we propose a stochastic liquidity discount factor model to evaluate the liquidity risk premium and its term structure in China's corporate bond market. The Monte Carlo simulation technique is used to quantify the impact on the liquidity premium of various liquidity factors: the liquidity level, liquidity volatility, liquidity shock, and the liquidity elasticity. Our findings conclude that the liquidity level is the most significant component of a liquidity premium. The impact on the liquidity premium of other liquidity factors is all conditional on the liquidity level. In addition, the impact of liquidity shocks and volatility is also subject to the market's equilibrium mechanism. Further, the term of a bond affects the premium both directly and indirectly through its influence on a bond's liquidity. [ABSTRACT FROM AUTHOR]

This study addresses interest rate sensitivity of emerging market corporate debt. Previous research suggests that interest rate sensitivity of corporate bonds depends on residual maturity of issues, creditworthiness of issuers, embedded options and other idiosyncratic factors. However, the dependence of interest rate sensitivity on phases of the business cycle has not received an appropriate academic attention. This paper provides empirical evidence and theoretical interpretation of a dichotomy of interest rate sensitivity across the phases of the cycle, and sheds light on how credit spreads respond to interest rates. The historical span of the research covers the period of 2004–2016. The findings imply that hedging interest rate risk ought to be a dynamic process and take into consideration where the economy is positioned in the current business cycle. This research provides important insights on the nature of interest rate sensitivity, capable of enhancing financial stability and improving efficiency of financial system. [ABSTRACT FROM AUTHOR]

Convertible bonds are an important segment of the corporate bond market, however, as hybrid instruments, convertible bonds are difficult to value because they depend on variables related to the underlying stock, the fixed-income part, and the interaction between these components. Besides, embedded options, such as conversion, call, and put provisions are often restricted to certain periods, may vary over time, and are subject to additional path-dependent features of the state variables. Moreover, the most challenging problem in convertible bond valuation is the underlying stock return process modeling as it retains various complex statistical properties. In this paper, we propose DeepPricing, a novel data-driven convertible bonds pricing model, which is inspired by the recent success of generative adversarial networks (GAN), to address the above challenges. The method introduces a new financial time-series generative adversarial networks (FinGAN), which is able to reproduce risk-neutral stock return process that retains the unique statistical properties such as the fat-tailed distributions, the long-range dependence, and the asymmetry structure etc., and then transit to its risk-neutral distribution. Thus it is more flexible and accurate to capture the dynamics of the underlying stock return process and keep the rich set of real-world convertible bond specifications compared with previous model-driven models. The experiments on the Chinese convertible bond market demonstrate the effectiveness of DeepPricing model. Compared with the convertible bond market prices, our model has a better convertible bonds pricing performance than both model-driven models, i.e. Black-Scholes, the constant elasticity of variance, GARCH, and the state-of-the-art GAN-based models, i.e. FinGAN-MLP, FinGAN-LSTM. Moreover, our model has a better fitting capacity for higher-volatility convertible bonds and the overall convertible bond market implied volatility smirk, especially for equity-liked convertible bonds, convertible bonds trading in the bull market, and out-of-the-money convertible bonds. Furthermore, the Long-Short and Long-Only investment strategies based on our model earn a significant annualized return with 41.16% and 31.06%, respectively, for the equally-weighted portfolio during the sample period. [ABSTRACT FROM AUTHOR]

This paper selects weekly transaction data from the Shenzhen Exchange and Shanghai Exchange for 2012 to 2018 to investigate factors including corporate size, the ration of book-to-market, default risk, term and credit rating that influence the corporate bond spread. It can be concluded that small firms prefer to issue high yield spread bonds while large firms issue bonds with low yield spreads. High (low) book-to-market firms issue high (low) yield spread bonds. The most important factors influencing corporate bond spread yield are default risk, terms, and credit ratings. We suggest that China Securities Regulatory Commission should encourage small firms to issue corporate bonds and there should be more kinds of credit ratings bonds in China bond market. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of multidimension liquidity, credit risk, and the interaction between liquidity and credit risk on corporate bond spreads based on a large transaction data set from July, 2006 to June, 2016, including the monthly data of 3716 bonds in China. Our main findings reveal that liquidity premiums are the main parts of corporate bond spreads. The interaction between liquidity and credit risk plays a significant role in determining corporate bond spreads. In addition, the differences between the interbank market and the exchange market have a significant impact on corporate bond spreads in normal period, and the interaction between liquidity and credit risk has an enhanced impact on corporate bond spreads during financial crisis. We also find that the interaction between liquidity and credit risk will increase with the increase of liquidity risk and credit risk and it is a time-varying dynamic process. [ABSTRACT FROM AUTHOR]

The role of green financing in propelling corporate green transformation and sustainable growth has attracted a lot of interest. Regarding their function in promoting corporate green transformation, green bonds—a crucial part of green finance—pose an important research question. This study, targeting Chinese A-share listed corporates, employing double machine learning models, aims to delve into the impact and mechanisms of green bond issuance on corporate green transformation. The findings indicate that issuing green bonds significantly drives corporate green transformation, especially in state-owned, smaller-scale, and non-manufacturing firms, where the impact is more evident. Further analysis discloses that green bond issuance primarily facilitates corporate green transformation through its functions in green investment and financing, green regulatory compliance, and green governance. This study serves as a theoretical reference, guiding businesses in seizing emerging opportunities in the green bond market and actively undertaking green transformation projects, thereby catalyzing a comprehensive economic transition. [ABSTRACT FROM AUTHOR]

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