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Without precedent, green bonds confront, for the first time since their emergence, a twofold crisis context, namely the Covid-19-Russian–Ukrainian crisis period. In this context, this paper aims to investigate the connectedness between the two pioneering bond market classes that are conventional and treasury, with the green bonds market.

In their forecasting target, authors use a Support Vector Regression model on daily S&P 500 Green, Conventional and Treasury Bond Indexes for a year from 2012 to 2022.

Authors argue that conventional bonds could better explain and predict green bonds than treasury bonds for the three studied sub-periods (pre-crisis period, Covid-19 crisis and Covid-19-Russian–Ukrainian crisis period). Furthermore, conventional and treasury bonds lose their forecasting power in crisis framework due to enhancements in market connectedness relationships. This effect makes spillovers in bond markets more sensitive to crisis and less predictable. Furthermore, this research paper indicates that even if the indicators of the COVID-19 crisis have stagnated and the markets have adapted to this rather harsh economic framework, the forecast errors persist higher than in the pre-crisis phase due to the Russian–Ukrainian crisis effect not yet addressed by the literature.

This study has several implications for the field of green bond forecasting. It not only illuminates the market participants to the best market forecasters, but it also contributes to the literature by proposing an unadvanced investigation of green bonds forecasting in Crisis periods that could help market participants and market policymakers to anticipate market evolutions and adapt their strategies to period specificities.

This study aims to analyze the efficient market hypothesis (EMH) of cryptocurrencies on multiple platforms by observing whether there is a discrepancy in the levels of efficiency between different exchanges. Additionally, EMH is tested in a multivariate way: whether the prices of the same cryptocurrencies traded on different exchanges are temporally related to each other. ADF and KPSS tests, whereas the vector autoregression model of order p – VAR(p) – for multivariate system.

Both Bitcoin and Ethereum show efficiency in the weak form on the main platforms in each market alone. However, when estimating a VAR(p) between prices among exchanges, there was evidence of Granger causality between cryptocurrencies in all exchanges, suggesting that EMH is not adequate due to cross information.

It is essential to assess the cryptocurrency market in a multivariate way, not only to favor its maturation process, but also to promote a broad understanding of its inherent risks. Thus, it will be possible to develop financial products that are actively managed in a more sophisticated cryptocurrency market.

There is a possibility of performing arbitrage on different exchanges and market assets through cross-exchanges. Thus, emphasizing the need for regulation of exchanges in the digital asset market, as an eventual price manipulation on a single platform can impact others, which generates various distortions.

This study is the first to find evidence of cross-information for the same (and other) cryptocurrencies among different exchanges.

This study is motivated in part by the fact that the unfolding 2022 bear market, which has reached the −25% drawdown, has not been preceded by the inverted 10Y-3 m spread or an inverted near-term forward spread.

The authors develop a three-factor probit model to predict/explain the deep stock market drawdowns, which the authors define as the drawdowns in excess of 20%.

The study results show that (1) the rising credit risk predicts a deep drawdown about a year in advance and (2) the monetary policy easing precedes an imminent drawdown below the 20% threshold.

This study three-factor probit model shows adaptability beyond the typical recessionary bear market and predicts/explains the liquidity-based selloffs, like the 2022 and possibly the 1987 deep drawdowns.

The research documented in this paper aims to examine multiple factors related to borrowers' default in peer-to-peer (P2P) lending in the USA. This study is motivated by the hypothesis that both P2P loan characteristics and macroeconomic variables have influence on loan performance. The authors define a set of loan characteristics, borrower characteristics and macroeconomic variables that are significant in determining the probability of default and should be taken into consideration when assessing credit risk.

The research question in this study is to find the significant explanatory variables that are essential in determining the probability of default for LendingClub loans. The empirical study is based on a total number of 1,863,491 loan records issued through LendingClub from 2007 to 2020Q3 and a logistic regression model is developed to predict loan defaults.

The results, in line with prior research, show that a number of borrower and contractual loan characteristics predict loan defaults. The innovation of this study is the introduction of specific macroeconomic indicators. The study indicates that macroeconomic variables assessed alongside loan data can significantly improve the forecasting performance of default model. The general finding demonstrates that higher percentage change in House Price Index, Consumer Sentiment Index and S&P500 Index is associated with a lower probability of delinquency. The empirical results also exhibit significant positive effect of unemployment rate and GDP growth rate on P2P loan default rates.

The results have important implications for investors for whom it is of great importance to know the determinants of borrowers' creditworthiness and loan performance when estimating the investment in a certain P2P loan. In addition, the forecasting performance of the model could be applied by authorities in order to deal with the credit risk in P2P lending and to prevent the effects of increasing defaults on the economy.

This paper fulfills an identified need to shed light on the association between specific macroeconomic indicators and the default risk from P2P lending within an economy, while the majority of the existing literature investigate loan and borrower information to evaluate credit risk of P2P loans and predict the likelihood of default.

Based on stakeholder, agency and institutional theory, this study aims to examine the role of institutional ownership in the relationship between environmental, social and governance practices and CEO compensation.

Utilizing a fixed-effect panel regression analysis, this research utilized a panel data approach, analyzing data spanning from 2014 to 2021, focusing on US companies listed on the S&P500 stock market index. The dataset encompassed 219 companies, leading to a total of 1,533 observations.

The analysis identified that environmental scores significantly impact CEO equity-linked compensation, unlike social and governance scores. Additionally, it was found that institutional ownership acts as a moderating factor in the relationship between the environmental score and CEO equity-linked compensation, as well as the association between the social score and CEO equity-linked compensation. Interestingly, the direction of these moderating effects varied between the two relationships, suggesting a nuanced role of institutional ownership.

This research makes a unique contribution to the field of corporate governance by exploring the relatively understudied area of institutional ownership's influence on the ESG practices–CEO compensation nexus.

The aim of the study was to analyze the performance of Black-Litterman (BL) portfolios using a views estimation procedure that simulates investor forecasts based on technical analysis.

Ibovespa, S&P500, Bitcoin and interbank deposit rate (IDR) indexes were respectively considered proxies for the national, international, cryptocurrency and fixed income stock markets. Forecasts were made out of the sample aiming at incorporating them in the BL model, using several portfolio weighting methods from June 13, 2013 to August 30, 2022.

The Sharpe, Treynor and Omega ratios point out that the proposed model, considering only variable return assets, generates portfolios with performances superior to their traditionally calculated counterparts, with emphasis on the risk parity portfolio. Nonetheless, the inclusion of the IDR leads to performance losses, especially in scenarios with lower risk tolerance. And finally, given the impact of turnover, the naive portfolio was also detected as a viable alternative.

The results obtained can contribute to improve investors practices, specifically by validating both the performance improvement – when including foreign assets and cryptocurrencies –, and the application of the BL model for asset pricing.

The main contributions of the study are: performance analysis incorporating cryptocurrencies and international assets in an uncertain recent period; the use of a methodology to compute the views simulating the behavior of managers using technical analysis; and comparing the performance of portfolio management strategies based on the BL model, taking into account different levels of risk and uncertainty.

This study is the first to investigate the causal relationship between Bitcoin and equity price returns by sectors. Previous studies have focused on aggregated indices such as S&P500, Nasdaq and Dow Jones, but this study uses mixed frequency and disaggregated data at the sectoral level. This allows the authors to examine the nature, direction and strength of causality between Bitcoin and equity prices in different sectors in more detail.

This paper utilizes an Unrestricted Asymmetric Mixed Data Sampling (U-AMIDAS) model to investigate the effect of high-frequency Bitcoin returns on a low-frequency series equity returns. This study also examines causality running from equity to Bitcoin returns by sector. The sample period covers United States (US) data from 3 Jan 2011 to 14 April 2023 across nine sectors: materials, energy, financial, industrial, technology, consumer staples, utilities, health and consumer discretionary.

The study found that there is no causality running from Bitcoin to equity returns in any sector except for the technology sector. In the tech sector, lagged Bitcoin returns Granger cause changes in future equity prices asymmetrically. This means that falling Bitcoin prices significantly influence the tech sector during market pullbacks, but the opposite cannot be said during market rallies. The findings are consistent with those of other studies that have established that during market pullbacks, individual asset prices have a tendency to decline together, whereas during market rallies, they have a tendency to rise independently. In contrast, this study finds evidence of causality running from all sectors of the equity market to Bitcoin.

The findings have significant implications for investors and fund managers, emphasizing the need to consider the asymmetric causality between Bitcoin and the tech sector. Investors should avoid excessive exposure to both Bitcoin and tech stocks in their portfolio, as this may lead to significant drawdowns during market corrections. Diversification across different asset classes and sectors may be a more prudent strategy to mitigate such risks.

The study's findings underscore the need for investors to pay close attention to the frequency and disaggregation of data by sector in order to fully understand the true extent of the relationship between Bitcoin and the equity market.

The aim of this study is to identify environmental, social and governance (ESG)-focused funds that can effectively uphold ethical principles while also delivering competitive financial returns by evaluating the performance of 24 well-established exchange-traded funds (ETFs). The study also compares the performance of four widely recognized ETFs representing NASDAQ (ticker: QQQ), S&P500 (SPY), Dow Jones (DIA) and Russell 2000 (IWM) with the sample of 24 ESG funds.

This paper utilizes four complementary measures, namely Sharpe, Sortino, Omega and Calmar ratios, to assess the risk-adjusted return performance of ETFs, with a particular emphasis on extreme downside risk.

The findings indicate that ESG-focused ETFs can predominantly outperform DIA and IWM in the last five years (1 November 2018–22 March 2023). However, when compared to QQQ and SPY, only ICLN, SUSA and DSI consistently delivered competitive risk-adjusted returns. The performance of DSI and SUSA is almost equivalent to QQQ and SPY even during the last ten years.

The paper conducts a risk-return analysis of alternative ESG investment funds, suggesting that not all ETFs are created equal and that careful selection is vital for achieving different investment objectives. It is imperative to recognize that past performance is not a reliable indicator of future outcomes, requiring consideration of other factors in the post-evaluation phase.

The study provides evidence to support the “doing well while doing good” hypothesis, indicating that competitive returns are achievable while also engaging in socially responsible investment.

This study fills a vital gap in the literature on ESG investment by highlighting that the choice of funds stands as the primary factor responsible for the conflicting findings by previous studies.

We propose an alternative rationale for why some firms employ veterans, driven not solely by benevolence but also by the prospect of enhanced outcomes. Financially, hiring veterans could correlate with improved stock market performance for the hiring company while aligning with corporate social responsibility (CSR) initiatives. Our study centers on the stock market performance of companies hiring veterans. It aims to underscore a lesser-known facet of the veteran employment discourse and its connection to the hiring firm's financial performance.

This paper evaluates the stock market performance of three VETS portfolios (made of companies that hire veterans) compared to the benchmark SPDR S&P 500 ETF. Using a modular approach, we create three VETS passive indices: VETSEW (equal-weighted index), VETSPW (price-weighted index) and VETSVW (value-weighted index). The study analyzes the annual returns, portfolio allocations, risk-adjusted performance metrics and style analysis of the portfolios from January 1, 2020, to December 31, 2022.

The findings indicate that all three VETS portfolios outperformed the benchmark, with higher ending balances and superior risk-adjusted ratios such as the Sharpe and Sortino ratios. Notably, the portfolios demonstrated resilience during challenging periods, including the COVID-19 pandemic, subsequent recovery and an inflationary period.

Limitations include the paper's focus solely on stock returns, suggesting a need for broader financial and management ratios. Moreover, a deeper exploration into how veterans contribute during turbulent times is suggested for further investigation. Although the study touches upon the financial performance of veteran-focused companies during challenging economic times, it does not extensively delve into the specific ways in which veterans add value under such circumstances, presenting an opportunity for further exploration.

Firms that employ veterans amid the COVID-19 pandemic demonstrate favorable risk-adjusted returns, underscoring the potential of veterans as valuable crisis-time assets. Our research further underscores the correlation between veteran hiring and enhanced financial prowess. These insights carry significant policy implications, including CSR initiatives for hiring veterans, skill translation and training and collaboration with veteran organizations.

The paper's findings suggest significant implications: (1) Policymakers could incentivize firms to hire veterans through tax benefits or grants, leveraging their skills for organizational resilience. (2) Collaborative efforts between policymakers and firms can promote responsible hiring, boosting a company's reputation through diversity and inclusion, positively impacting society. (3) Support for skill translation from military to civilian jobs is crucial. Programs certifying skills and tailored education aid veterans' successful transition into the workforce. (4) Collaborations between policymakers, veteran organizations and private sector entities can create networks, job placements and support systems for veterans' employment.

Numerous prior studies within the domain of corporate social responsibility have predominantly neglected the contributions veterans offer to businesses and the underlying reasons behind firms' decisions to employ them. Our research uniquely concentrates on the stock market performance of companies that choose to hire veterans.

The study examines the relationship between Economic Policy Uncertainty (EPU) and stock liquidity, and the mediating role of investor sentiment.

This study draws on a sample of 4,620 firm-year observations covering nonfinancial firms in the United States from 2007 to 2020. We employ multiple regression analysis with panel data and path analysis with Structural Equation Modeling (SEM) to examine the impact of EPU on stock liquidity in detail.

EPU significantly enhances stock liquidity. However, at elevated levels of EPU, this relationship reverses. The path analysis results indicate that EPU positively affects stock liquidity via the investor sentiment channel. This sentiment partially mediates the relationship between EPU and both trading volume and turnover rate, and fully mediates the relationship between EPU and both turnover price impact and illiquidity.

Our findings underscore the importance of liquidity for investors, who may require higher returns for holding more illiquid stocks. Second, they can help the government understand the implications of changes in EPU, highlighting the need for clear communication and the implementation of appropriate capital market policies.

While considerable research focuses on the relationship between EPU and stock market liquidity, the analysis of the channels through which EPU influences stock market liquidity remains largely unexplored. Our study highlights the importance of investor sentiment in explaining this relationship.