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The article examines the role of stakeholders in shaping firm behavior in the digital era. It acknowledges that social media has made it easier for individuals to express their concerns to companies, but argues that this has not necessarily resulted in increased influence. The authors propose the concept of "heterogenous convergence," where stakeholders with diverse perspectives can find common ground through the use of generic hashtags. However, they express skepticism about the effectiveness of this approach in driving substantive changes. The article also highlights the challenges posed by disinformation and information overload on social media platforms. [Extracted from the article]

By employing a dynamic model with two limit order books, we show that fragmentation is associated with reduced competition among liquidity suppliers and lower picking-off risk of limit orders. Due to these countervailing channels, the impact of fragmentation on liquidity and welfare differs with asset volatility: When volatility is high (low), liquidity and aggregate welfare in a fragmented market are higher (lower) than in a single market. However, fragmentation always shifts welfare away from agents with exogenous trading motives and toward intermediaries. We empirically corroborate our model's predictions about liquidity. Our model reconciles the mixed results in the empirical literature. [ABSTRACT FROM AUTHOR]

This article investigates spillovers from foreign economies to the U.S. through changes in long-term Treasury yields. We document a decline in the contribution of U.S. domestic news to the variance of long-term Treasury yields and an increased importance of overnight yield changes, a proxy for foreign shocks' contribution to U.S. yields. A model that identifies U.S., Euro area, and U.K. shocks that move global yields suggests that foreign shocks account for at least 20% of the daily variation in long-term U.S. yields in recent years. We also document the predictability of long-term U.S. yields by the U.S.–foreign yield spread. [ABSTRACT FROM AUTHOR]

U.S. equity exchanges have experienced a dramatic increase in competition from new entrants, resulting in the fragmentation of trading across venues. While market quality has generally improved over this period, we show most of the improvements have accrued to the largest stocks. We then show this bifurcation in market quality is related to the fragmentation of trading. Theoretically, more exchange competition should reduce trading costs, yet it may also increase adverse selection for liquidity providers, leading to higher spreads. We document evidence of both effects (fragmentation improves market quality for large stocks while small stocks experience relatively worse quality). [ABSTRACT FROM AUTHOR]

We present evidence that several popular low-frequency measures of effective spread suffer from a volatility-induced bias and that volatility is the primary driver of the variation of these liquidity proxies. Using data for U.S. equities and major foreign exchange rates, we show that the bias arises when the effective spread is small relative to volatility. We document that the bias has become more acute over time and show that volatility-biased measures fail to replicate some well-known results in empirical finance. We conclude by providing guidance on the choice of low-frequency measures in empirical applications. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

This study delves into the intra-industry effects following a firm-specific scandal, with a particular focus on the Facebook data leakage scandal and its associated events within the U.S. tech industry and two additional relevant groups. We employ various metrics including daily spread, volatility, volume-weighted return, and CAPM-beta for the pre-analysis clustering, and subsequently utilize CAR (Cumulative Abnormal Return) to evaluate the impact on firms grouped within these clusters. From a broader industry viewpoint, significant positive CAARs are observed across U.S. sample firms over the three days post-scandal announcement, indicating no adverse impact on the tech sector overall. Conversely, after Facebook's initial quarterly earnings report, it showed a notable negative effect despite reported positive performance. The clustering principle should aid in identifying directly related companies and thus reducing the influence of randomness. This was indeed achieved for the effect of the key event, namely "The Effect of Congressional Hearing on Certain Clusters across U.S. Tech Stock Market," which was identified as delayed and significantly negative. Therefore, we recommend applying the clustering method when conducting such or similar event studies. [ABSTRACT FROM AUTHOR]

The jump in the value of the stock may result in a corresponding escalation of risk associated with the stock market. When a jump occurs in any particular market, such a risk can spread to other markets. This paper examines realized volatility and risk contagion of the jump between the Chinese mainland and Hong Kong market utilizing realized volatility and three realized jump separation methods. Correspondingly, the Markov regime‐switching approach is employed to exhibit the contagion effect of the jump risk in two economic states. This study employs the Shanghai–Shenzhen 300 Index and the Hang Seng Index as empirical research subjects. The results show that the Hong Kong market has a higher frequency of jumps compared to the mainland market. The MedRV method is found to be effective in identifying more jumps, while the mainland market displays a unidirectional contagion effect on Hong Kong. Under such circumstances, the risk associated with a continental market jump exhibits an unevenly contagious effect on the Hong Kong market. In conclusion, China needs to enhance its financial market risk management in order to avert the spread of escalating risk across markets. [ABSTRACT FROM AUTHOR]

We study the problems of consistency and the existence of finite-dimensional realizations for multi-curve interest rate models of Heath–Jarrow–Morton type, generalizing the geometric approach developed by T. Björk and co-authors for the classical single-curve setting. We characterize when a multi-curve interest rate model is consistent with a given parameterized family of forward curves and spreads and when a model can be realized by a finite-dimensional state process. We illustrate the general theory in a number of model classes and examples, providing explicit constructions of finite-dimensional realizations. Based on these theoretical results, we perform the calibration of a three-curve Hull–White model to market data and analyse the stability of the estimated parameters. [ABSTRACT FROM AUTHOR]

Agents' beliefs regarding future monetary policy changes influence their current decisions. However, these expectations may not always materialize in the future. This study shows that the monetary fundamental shocks (exogenous changes consistent with expectations) stabilize output and inflation, while the noise shocks (biased beliefs that fail to be realized in the future) increase economic fluctuations. Moreover, factors that amplify financial frictions—the spread between the capital return of entrepreneurs and the risk‐free interest rate of a central bank—can increase anticipation effects associated with these two types of monetary policy shocks. [ABSTRACT FROM AUTHOR]

In this paper, we explore the applications of fractional stochastic volatility (FSV) models within the realm of market microstructure theory and optimal execution strategies. FSV models extend traditional stochastic volatility frameworks by incorporating fractional differentiation, allowing for more flexible and realistic representations of asset price dynamics over time. Our investigation begins with an introduction to FSV models, highlighting their ability to capture longmemory effects and volatility clustering observed in financial markets. These models provide a robust framework for understanding market microstructure dynamics, including order flow behavior, price impact functions, and liquidity provision mechanisms. Furthermore, we discuss recent advancements and empirical findings using FSV models, emphasizing their role in uncovering intraday volatility patterns and their implications for trading strategies under varying market conditions. By incorporating these nuanced volatility dynamics, FSV models contribute to the development of optimal execution algorithms that enhance transaction cost efficiency and market stability. The FSV model, when the Hurst exponent H is set to 0.5, effectively reduces to a standard stochastic volatility model. THis nested relationship can be formally demonstrated by considering the FSV model's general form and showing that for H = 0.5, the fractional Brownian motion BH(t) becomes a standard Brownian motion W (t), thereby aligning the FSV model with traditional models. Overall, our analysis underscores the significance of FSV models in advancing both theoretical insights and practical applications in modern finance, offering new avenues for research in high-frequency trading strategies and market efficiency. [ABSTRACT FROM AUTHOR]

This study examines received and transmitted volatility spillovers of Credit Default Swap (CDS) and Asset-Swap Spread (ASW) for Brazil and Turkey. The empirical analysis is implemented using two country-based (stock markets and exchange rates) and two global (volatility index and global economic activity index) variables to account for the impact of integration into global markets. Empirical results suggest that both countries display distinctive features in their spillover networks. While exchange rates and the stock market figure prominently in Brazil as a source of spillovers, for Turkey, the primary element in spillovers appears to be credit risk indicators. Time-varying analysis results show that the European Debt Crisis of 2010-2011 and the global liquidity crunch of 2018-2019 are two critical periods in volatility spillovers that occurred toward credit risk indicators. Brazil displays more sensitivity to the developments of the pandemic than Turkey, likely due to its dependence on global economic activity and energy prices. Finally, for both countries, the leading variable in spillovers to credit risk indicators during financial turbulence episodes appears to be foreign exchange markets. This result highlights both economies’ fragility and vulnerability to foreign exchange market-based shocks. Thus, we suggest effective and solid measures in this regard. Otherwise, those shocks could potentially induce a higher cost of financing in both economies due to the negative impacts on CDS and ASW spreads. [ABSTRACT FROM AUTHOR]

Catastrophe (CAT) bonds necessitate a robust construction with regard to the estimated probability measure of their triggering parameter. This article concentrates on earthquakes as the primary natural catastrophe of concern. By leveraging the geometry of active faults for estimating default probability, we utilize seismic event information spanning up to 15,000 years in the past—thereby surpassing the restricted time range of available historical catalogs commonly used in other analyses, which typically cover only a few hundred years. This article introduces the design and pricing methodology of CAT bonds employing autoregressive neural networks, extending the standard VAR Nelson-Siegel model for yield curves. It presents a case study focused on the region of Greece, estimating that an additional spread of approximately 500 basis points over LIBOR constitutes the minimum premium required to attract an investor to undertake the associated risk. This premium could be absorbed by insured parties as an alternative to the conventional insurance process. [ABSTRACT FROM AUTHOR]

Interest rate dependent securities have risks that are best quantified through different duration and convexity measures, such as effective, modified, spread, and partial durations and convexities. For Collateral Mortgage Obligations (CMOs), the convexity measure shows behavior that is confusing and unintuitive to investors. We address these issues by taking a deeper look at CMO pricing using Monte Carlo paths, offering better understanding of the convexity issues, and defining a better convexity measure. [ABSTRACT FROM AUTHOR]

We implement a two‐stage methodology based on the structural vector autoregressive and time‐varying parameter vector autoregressive models to examine the time‐varying effect of distinct types of oil‐price shocks on sovereign credit risks measured by credit default swap (CDS) spreads in Gulf Cooperation Council countries. Using monthly data for the period from May 2011 to February 2022, our results show time‐varying responses to structural oil shocks in the short‐ and medium‐run periods, with more fluctuations in responses detected over the full sample period in the former. Overall, we detect a break in the contagious impacts of oil shocks during and in the aftermath of, the 2014–2015 oil crisis and COVID‐19 crisis. Specifically, the Bahraini market is found to exhibit a positive (negative) reaction to the oil supply shocks (OS) and oil market‐specific demand shocks (OSD) throughout the pandemic period. Furthermore, we uncover a transient response from the Saudi and Qatari CDS spreads to the aggregate demand shocks (ADS) and the OSD over the full sample period, indicating the need for portfolio rebalancing. In the UAE, we detect a positive impact over the three sampled years of OSD since 2011. Moreover, a notable decoupling pattern continues to appear between short‐ and medium‐term innovations in the ADS. Our results suggest adopting more conservative trading in the CDS markets while understanding the oil price and the economic state. The complexity of the trading strategy should also depend on the target Gulf market itself and that seems essential when it comes to investing in Qatar and Saudi Arabia. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak caused a massive setback to the stability of financial system due to emergence of several other risks with COVID, which significantly influenced the continuity of profitable banking operations. Therefore, this study aims to see that how differently the liquidity risk and credit risk influenced the banking profitability during Covid-19 (Q12020 to Q42021) than before COVID (Q12018 to Q42019). The study employs pooled OLS, and OLS fixed & random effects models, to analyze the panel data on a sample of 37 banks currently operating in Pakistan. The results depict that liquidity risk has a positive and significant relationship with return on assets and return on equity, but insignificant relationship with net interest margin. Credit risk has a negative and significant relationship with return on assets, return on equity, and net interest margin. The study also applies quantile regression to address the normality issue in data. The quantile regression results are consistent with pooled OLS, and OLS fixed and random effects results. The study makes valuable suggestions for regulators, policymakers, and others users of financial institutional data. The current study will help to set policies for efficient management of LR and CR. [ABSTRACT FROM AUTHOR]

Understanding liquidity and liquidity risk is essential for effective risk management. We investigate liquidity spillover effects among ETFs that track the S&P sectors. In particular, using COVID-related news shocks as a natural experiment, we estimate the direction and magnitude of two-way net spillovers and their asymmetry across good and bad news regimes, where liquidity is measured by the daily quoted bid–ask spread and the Amihud illiquidity ratio. Our results confirm the liquidity links amongst ETFs and suggest that liquidity spillovers are more pronounced during bad news periods compared to good news periods. In addition, we document the variations in the results obtained using the bid–ask spread and the Amihud ratio, which provide insights into different dimensions of liquidity and liquidity risk, including volatility and trading volume. [ABSTRACT FROM AUTHOR]

This paper proposes a new index of forward looking absolute deviation extracted from option prices. The new index, named absolute deviation index (ADIX), is model‐free and easy to compute using at‐the‐money call and put option prices. It is shown that the spread between volatility index (VIX) and ADIX captures departures from normality in the risk‐neutral distribution and an empirical analysis using S&P 500 options data for the time period 1996–2021 reveals that the spread carries significant forecasting ability with respect to future equity returns at short to medium horizons. Portfolio strategies that use the spread as a predictor of S&P 500 returns outperform buy‐and‐hold strategies in an out‐of‐sample mean‐variance asset allocation exercise. [ABSTRACT FROM AUTHOR]

This paper contributes to the understanding of sovereign credit default swap (CDS) markets by examining the response of CDS spreads to macroeconomic factors and exploring extreme value dependence and its relation to economic cycles. The study focuses on four emerging countries in the Asia–Pacific sovereign CDS markets from 2009 to 2023 and utilises a dynamic quantile autoregressive distributed lag (QARDL) approach to account for statistical stylized facts. The findings reveal significant relationships between economic growth, inflation, volatility index (VIX), interest rates and real effective exchange rate on CDS spreads, with varying effects across quantiles and countries. Additionally, the study explores the impact of economic expansion and contraction on CDS spreads, highlighting the significant negative effects of the expansion in certain countries and the positive impacts of contraction phases. These findings provide valuable insights for policymakers in risk management and policy decision-making, emphasizing the need for policies that promote sustainable growth; manage market risks during volatile periods and consider the implications of interest rates, exchange rates and economic phases on financial stability. The empirical model used is evaluated for dynamic stability, and policy implications are discussed in light of the research outcomes. [ABSTRACT FROM AUTHOR]

Small stocks' time‐varying spreads predict future return gap between small and large stocks. To optimally exploit such predictability, the investor captures current risk premium by purchasing at large spreads with substantially reduced turnover; uses an aim‐in‐front‐of‐the‐target approach to trade‐off between future risk premium and current transaction costs; and meets hedging demand at low costs. Strong interaction between transaction costs and return predictability leads to large losses from myopic trading. Greater variability of the spread is advantageous for investors who trade optimally but detrimental for investors who trade myopically. The spread‐based return predictability significantly increases the investment value of small stocks. [ABSTRACT FROM AUTHOR]

The common goal for investors is to minimise the risk and maximise the returns on their investments. This is often achieved through diversification, where investors spread their investments across various assets. This study aims to use the MAD-entropy model to minimise the absolute deviation, maximise the mean return, and maximise the Shannon entropy of the portfolio. The MAD model is used because it is a linear programming model, allowing it to resolve large-scale problems and nonnormally distributed data. Entropy is added to the MAD model because it can better diversify the weight of assets in the portfolios. The analysed portfolios consist of cryptocurrencies, stablecoins, and selected world indices such as the SP500 and FTSE obtained from Yahoo Finance. The models found that stablecoins pegged to the US dollar, followed by stablecoins pegged to gold, are better diversifiers for traditional cryptocurrencies and stocks. These results are probably due to their low volatility compared to the other assets. Findings from this study may assist investors since the MAD-Entropy model outperforms the MAD model by providing more significant portfolio mean returns with minimal risk. Therefore, crypto investors can design a well-diversified portfolio using MAD entropy to reduce unsystematic risk. Further research integrating mad entropy with machine learning techniques may improve accuracy and risk management. [ABSTRACT FROM AUTHOR]

In this study, we propose the application of the GARCH‐EVT‐Copula model in estimating liquidity‐adjusted value‐at‐risk (L‐VaR) of energy stocks while modeling nonlinear dependence between return and bid‐ask spread. Using the L‐VaR framework of Bangia et al. (1998), we present a more parsimonious model that effectively captures non‐zero skewness, excess kurtosis, and volatility clustering of both return and spread distributions of energy stocks. Moreover, to measure the nonlinear dependence between return and spread series, we use multiple copulas: Clayton, Gumbel, Frank, Normal, and Student‐t. Based on the statistical backtesting and economic loss functions, our results suggest that the GARCH‐EVT‐Clayton copula is superior and most consistent in forecasting L‐VaR compared with other competing models. This finding has several implications for investors, market makers, and daily traders who appreciate the importance of liquidity in market risk computation. [ABSTRACT FROM AUTHOR]

We examine the predictability of stock returns using implied volatility spreads (VS) from individual (nonindex) options. VS can occur under simple no-arbitrage conditions for American options when volatility is time-varying, suggesting that the VS-return predictability could be an artifact of firms' sensitivities to aggregate volatility. Examining this empirically, we find that the predictability changes systematically with aggregate volatility and is positively related to the firms' sensitivities to volatility risk. The alpha generated by VS hedge portfolios can be explained by aggregate volatility risk factors. Our results cannot be explained by firm-specific informed trading, transaction costs, or liquidity. [ABSTRACT FROM AUTHOR]

We characterize the unique equilibrium in an economy populated by strategic CARA investors who trade multiple risky assets with arbitrarily distributed payoffs. We use our explicit solution to study the joint behavior of illiquidity of option contracts. Option bid-ask spreads are proportional to risk aversion and risk-neutral variances of option payoffs. Spreads may decrease in risk aversion, physical variance, open interest, and increase after earnings announcements in a result contrary to conventional wisdom. All these predictions are confirmed empirically using a large panel data set of U.S. stock options. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

We zero in on the expected returns of long-short portfolios based on 204 stock market anomalies by accounting for i) effective bid–ask spreads, ii) post-publication effects, and iii) the modern era of trading technology that began in the early 2000s. Net of these effects, the average anomaly's expected return is a measly 4 bps per month. The strongest anomalies net, at best, 10 bps after controlling for data mining. Several methods for combining anomalies net around 20 bps. Expected returns are negligible despite cost mitigations that produce impressive net returns in-sample and the omission of additional trading costs, like price impact. [ABSTRACT FROM AUTHOR]

This study estimates the monetary policy reaction function (MPRF) in a Dynamic Stochastic General Equilibrium (DSGE) framework using Bayesian analysis for the emerging economies. DSGE models are suitable for the policy analysis because of their simplicity and prominent role of forward-looking variables. This is a pioneer study investigating the combined effects of credit spreads, fiscal imbalances, and monetary autonomy on interest rates for BRICS member countries. Using real data for the period 1970–2021, the posterior estimates confirm that both credit spread and fiscal imbalance significantly contribute to fluctuations in output, inflation, and interest rates in all the sample economies. The estimates show that fluctuations in the inflation rate are due to supply shocks. The empirical estimates also reveal that fiscal imbalances shock significantly affect output in Brazil, India, and South Africa, whereas, based on real data inflation and interest rate are significantly affected by fiscal imbalance shocks in China and South Africa. Yet, the findings suggest that the effects of various shocks on output and interest rates vary across countries. [ABSTRACT FROM AUTHOR]

Modeling implied volatility (IV) is important for option pricing, hedging, and risk management. Previous studies of deterministic implied volatility functions (DIVFs) propose two parameters, moneyness and time to maturity, to estimate implied volatility. Recent DIVF models have included factors such as a moving average ratio and relative bid-ask spread but fail to enhance modeling accuracy. The current study offers a generalized DIVF model by including a momentum indicator for the underlying asset using a relative strength index (RSI) covering multiple time resolutions as a factor, as momentum is often used by investors and speculators in their trading decisions, and in contrast to volatility, RSI can distinguish between bull and bear markets. To the best of our knowledge, prior studies have not included RSI as a predictive factor in modeling IV. Instead of using a simple linear regression as in previous studies, we use a machine learning regression algorithm, namely random forest, to model a nonlinear IV. Previous studies apply DVIF modeling to options on traditional financial assets, such as stock and foreign exchange markets. Here, we study options on the largest cryptocurrency, Bitcoin, which poses greater modeling challenges due to its extreme volatility and the fact that it is not as well studied as traditional financial assets. Recent Bitcoin option chain data were collected from a leading cryptocurrency option exchange over a four-month period for model development and validation. Our dataset includes short-maturity options with expiry in less than six days, as well as a full range of moneyness, both of which are often excluded in existing studies as prices for options with these characteristics are often highly volatile and pose challenges to model building. Our in-sample and out-sample results indicate that including our proposed momentum indicator significantly enhances the model's accuracy in pricing options. The nonlinear machine learning random forest algorithm also performed better than a simple linear regression. Compared to prevailing option pricing models that employ stochastic variables, our DIVF model does not include stochastic factors but exhibits reasonably good performance. It is also easy to compute due to the availability of real-time RSIs. Our findings indicate our enhanced DIVF model offers significant improvements and may be an excellent alternative to existing option pricing models that are primarily stochastic in nature. [ABSTRACT FROM AUTHOR]

In the article at hand neural networks are used to model liquidity in financial markets, under conic finance settings, in two different contexts. That is, on the one hand this paper illustrates how the use of neural networks within a two-price economy allows to obtain accurate pricing and Greeks of financial derivatives, enhancing computational performances compared to classical approaches such as (conic) Monte Carlo. The methodology proposed for this purpose is agnostic of the underlying valuation model, and it easily adapts to all models suitable for pricing in conic financial markets. On the other hand, this article also investigates the possibility of valuing contingent claims under conic assumptions, using local stochastic volatility models, where the local volatility is approximated by means of a (combination of) neural network(s). Moreover, we also show how it is possible to generate hybrid families of distortion functions to better fit the implied liquidity of the market, as well as we introduce a conic version of the SABR model, based on the Wang transform, that still allows for analytical bid and ask pricing formulae. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal management of an aggregated defined benefit pension plan in a stochastic environment. The interest rate follows the Ornstein-Uhlenbeck model, the benefits follow the geometric Brownian motion while the contribution rate is determined by the spread method of fund amortization. The pension manager invests in the financial market with three assets: cash, a zero-coupon bond and a stock. Regardless of the initial status of the plan, we suppose that the pension fund may become underfunded or overfunded in the planning horizon. The optimization goal of the manager is to maximize the expected utility in the overfunded region minus the weighted solvency risk in the underfunded region. By introducing an auxiliary process and related equivalent optimization problems and using the martingale method, the optimal wealth process, optimal portfolio and efficient frontier are obtained under four cases (high tolerance towards solvency risk, low tolerance towards solvency risk, a specific lower bound, and high lower bound). Moreover, we also obtain the probabilities that the optimal terminal wealth falls in the overfunded and underfunded regions. At last, we present numerical analyzes to illustrate the manager's economic behaviors. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between a country's political governance and financial market dynamics, with a specific focus on non-U.S. stocks listed on the NYSE. Utilizing an ordinary least squares (OLS) regression model with heteroscedasticity-robust (Huber–White) estimators, we analyze the impact of political governance on stock liquidity and information asymmetry. Our analysis shows that stocks from democracies demonstrate improved liquidity and decreased information asymmetry, contrasting with stocks from autocracies that exhibit the opposite trend. Furthermore, shifts in political regimes dynamically impact stock liquidity and information transparency. These findings offer essential insights for investors, policymakers, and regulators, contributing to informed decision making and the formulation of policies that promote market health and transparency. Additionally, these findings underscore the importance of promoting political stability and transparent governance to foster healthy and efficient financial markets. [ABSTRACT FROM AUTHOR]

We investigate the empirical relation between country governance quality and stock market liquidity, as well as information asymmetry, using a sample of non-U.S. stocks from 17 emerging markets listed on the NYSE between 2004 and 2019. We find that non-U.S. stocks from emerging markets with higher democracy quality tend to have narrower spreads and larger depth, suggesting improved liquidity. Higher autocracy levels, on the other hand, are associated with wider spreads and lower depth, indicating poorer liquidity. Additionally, stronger democracy and polity qualities are linked to reduced price impact, while heightened autocracy levels are associated with increased price impact and a higher probability of informed trading. Moreover, we show that changes in our liquidity and information asymmetry measures significantly relate to changes in the country governance index over time. Our results remain remarkably robust across regions and when using different measures of liquidity and information-based trading. [ABSTRACT FROM AUTHOR]

Using a novel data set comprising bid–ask quotes for foreign exchange swaps from individual dealers, we examine the consequences of the Swiss National Bank's sudden termination of the Swiss franc/euro minimum exchange rate in 2015 on other pegged currencies. Our findings indicate a spillover effect as dealer banks began to reassess the risk associated with unexpected peg terminations, subsequently leading to wider bid–ask spreads for pegged currencies. This highlights that, even in strong economies, the credibility of a currency peg is influenced not only by the actions of the respective central bank but also by the stability of other pegged currencies. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01117. [ABSTRACT FROM AUTHOR]

We examine changes in market liquidity when several popular U.S. retail brokers restrict client trading in GameStop stock over a six-day period in early 2021 due to higher clearinghouse deposit requirements. When retail investor participation in a high-attentionstock becomes restricted, trading activity declines and trading venue shifts occur – there is less trading in dark markets and more fragmented trading across U.S. stock exchanges. We also find that when a stock order flow mix becomes comprised of fewer retail investors and a higher concentration of institutional investors, the quoted bid-ask spread widens, and trading cost measures rise significantly. [ABSTRACT FROM AUTHOR]

Profitability is a ratio to assess the company's ability to seek profit. This ratio also provides a measure of the level of effectiveness of a company's management. Profitability at PT Bank Mayapada Internasioanl Tbk in 2014-2023 fluctuated every year and tended to decline at the end of the research year. This study aims to determine the effect of Net Interest Margin (NIM), Operating Costs of Operating Income (BOPO), Loan to Deposit Ratio (LDR) on Return On Assets (ROA) for the 2014- 2023 period. This study uses a quantitative method with a descriptive verification approach. The data source used is secondary data with documentation data collection inthe form of annual financial reports and purposive sampling technique. The data analysis technique used is descriptive analysis, regression analysis, correlation analysis, coefficient of determination analysis, and hypothesis testing either partially or simultaneously with the help of the SPSS 25 program. The research results show that the average ROA is 0.919%, categorized as healthy. However, ROA in the last 2 years of the 2022-2023 research period has decreased drastically with the lowest value being 0.04% and is below Bank Indonesia's provisions for the healthy ROA category, namely 1.5%. The average NIM is 3.13% which is categorized as healthy, but in the year of research, namely 2020, it dropped drastically with a minimum value of 0.47%, even far from the minimum limit of 6% according to Bank Indonesia regulations for the healthy NIM category. The average BOPO is 91.79 which is categorized as healthy, but BOPO at the end of the research period in 2024 rose to the highest value, namely 99.40% and is above Bank Indonesia's provisions for the healthy BOPO category, namely a maximum of 85%. The average LDR is 85.10% which is categorized as however the LDR in the 3 years at the end of the research period in 2021 has decreased drastically with the lowest value being 71% and the highest value in 2019 even approaching the maximum and minimum limits, namely 80%-110% accordingly. Bank Indonesia provisions for the healthy LDR category. Partially NIM has no effect on ROA with a contribution of1.80%. BOPO has no effect on ROA with a contribution of 99.40%. LDR has a significant effect on ROA with a contribution of 88.59%. Together (simultaneously) NIM, BOPO, LDR have a significant influence on ROA at PT Bank Mayapada Internasional Tbk for the 2014-2023 period. [ABSTRACT FROM AUTHOR]

Beyond the 2007–2008 financial crisis, the collapse of the Silicon Valley Bank and the acquisition of Credit Suisse by the Swiss investment bank UBS Group AG in 2023 have brought fresh attention to the need for new regulatory capital, liquidity risk management, and leverage requirements. To meet tightened capital requirements, banks have to increase their capital ratios either by increasing equity or by decreasing risk-weighted assets. Both options lead to banks' performance deterioration. One remedy for banks to recover is raising their lending spread. A critical question is how much the lending spread should be increased to offset the drop in the bank's financial performance level. In this study, we focus on the asymmetries and efficiency consequences of performance indices such as economic value added (EVA) and the more commonly used return on equity (ROE) in determining the loan spread. Using data on the largest U.S. banks over the period 2018–2022, our results show that the ROE rule significantly overestimates the magnitude of the lending spreads required to offset the negative financial consequences of increases in capital ratios. The EVA approach, on the other hand, prescribes on average a significantly lower lending spread of 0.4505 basis points against a lending spread of 21.0441 basis points associated with the use of the ROE approach. The efficiency and the level of lending spreads should enable banks to maintain their competitive advantages in the loan markets impacting overall economic productivity and growth. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to assess and predict sovereign credit risk for Egypt, Morroco and Saudi Arabia using credit default swap (CDS) spreads obtained from the DataStream database for the period from 2009 to 2022. Our approach consists of generating the implied default probability and the corresponding credit rating in order to estimate the term structure of the implied default probability using the Nelson–Siegel model. In order to validate the prediction from the probability term structure, we calculate the transition matrices based on the implied rating using the homogeneous Markov model. The main results show that, overall, the probabilities of defaulting in the long term are higher than those in the short term, which implies that the future outlook is more pessimistic given the events that occurred during the study period. Egypt seems to be the country with the most fragile economy, especially after 2009, likely because of the political events that marked the country at that time. The economies of Morocco and Saudi Arabia are more resilient in terms of both default probability and credit rating. These findings can help policymakers develop targeted strategies to mitigate economic risks and enhance stability, and they provide investors with valuable insights for managing long-term investment risks in these countries. [ABSTRACT FROM AUTHOR]

We examine a comprehensive set of systematic and firm‐specific determinants of the credit default swap (CDS), using a two‐step approach to explore the factor's effect on CDS spread changes. We show that systematic factors are important and account for the most changes in the CDS spreads (with average R2 ${R}^{2}$ of 35%), while firm‐specific factors are limited (with R2 ${R}^{2}$ of 5% in panel regression) with only 4 out of 28 firm‐specific factors being significant. It implies that the systematic factors are overlooked in the literature, and they can provide many implications for practitioners in CDS pricing and the firm's credit risk management. [ABSTRACT FROM AUTHOR]

Based on a comprehensive sample of bonds contained in the Bloomberg US Corporate Index from 2013 to 2022, we find a statistically significant but sector-dependent relationship between companies' greenhouse gas emissions per unit of sales, our proxy for transition risk, and the option-adjusted spreads of their bonds—even when controlling for default, term, and liquidity risk factors. This relationship tends to be positive for companies that operate in high-emitting sectors and negative for companies that operate in low-emitting sectors. Our results therefore indicate that transition risk is not (yet) fully priced by traditional sources of risk and functions differently in different sectors. Within high-emitting sectors, investors appear to demand wider option-adjusted spreads for holding bonds of high-emitting firms, while the opposite is true for bonds of companies within low-emitting sectors, all else equal. Our results are robust to the choice of the scope of greenhouse gas emissions (1; 1 and 2; or 1, 2, and 3) and the proxy used for transition risk. For investors who aim to reduce the carbon footprint of their portfolio by underweighting or excluding bonds of high-emitting companies within each sector, our results indicate that this will lead to lower option-adjusted spreads across high-emitting sectors and higher option-adjusted spreads across low-emitting sectors. Whether this will enhance or harm expected risk-adjusted returns relative to the benchmark then depends on the weight of each sector. [ABSTRACT FROM AUTHOR]

This paper examines the long-run pattern of the U.S. nonfinancial corporate sector's liquid financial asset holdings in the period from 1951 to 2018. My approach to examining this pattern builds from the banks' liquidity preference theory put forward in Keynes' 1937 papers, as well as Minsky's 1957 paper discussing the role of financial innovation in the banks' management of liquidity preference. I argue that financial intermediaries affect nonfinancial corporations' liquid asset holdings through two primary money creation channels: (1) banks' commercial and industrial loans advanced to nonfinancial corporations and (2) the liquidity premium of financial intermediaries, which can be measured by the interest rate spread in the intermediaries' liabilities markets. Through theoretical and empirical exercises, I find that these two endogenous liquidity creation channels have been the important factors accounting for liquid asset accumulation by the nonfinancial corporate sector. Overall, my results suggest that financial innovations, structural changes of the U.S. financial system, and financial intermediaries' liquidity premium could contribute to our understanding of the shift in nonfinancial corporations' asset portfolio composition in favor of liquid financial assets. [ABSTRACT FROM AUTHOR]

We present results from an experiment where participants have access to automated trading algorithms, which they may deploy at will while still trading manually. Treatments differ in whether robots must not be halted, deployment is compulsory, or robots can be halted and replaced at will. We hypothesize that robot trading would reduce mispricing, and that the effect would be more pronounced as commitment degree increases. Yet, compared to manual trading only, we observe equally large and frequent mispricing and, in early trading, significantly higher bid–ask spreads and more frequent flash crashes/price surges. Participants earn more, provided they combine robot and manual trading. Compared to evidence from archival data, we find significantly higher use of liquidity-taking robots. We attribute this to the inability, in the field, to identify the presence of liquidity takers when they happen not to trade. [ABSTRACT FROM AUTHOR]

The aim of this study is to analyse and assess the yields and liquidity of sovereign green bonds in selected countries and to compare the yields between sovereign green bonds and conventional bonds. Sovereign green bonds are issued by governments to finance environmental and social projects and represent a relatively new and growing asset class. This study seeks to analyse the financial performance of sovereign green bonds by examining yields and liquidity metrics, such as bid–ask spreads. The findings of this research suggest that the yield to maturity (YTM) of sovereign green bonds is influenced by conventional bond return, while conventional sovereign bonds are affected by the financial market return. Furthermore, the results confirm that the liquidity of sovereign green bonds can be explained by bond maturity. [ABSTRACT FROM AUTHOR]

This paper highlights the lessons drawn from the demise of the Brent Crude Oil futures contract that was traded on the Singapore Stock Exchange (SGX). We analyze the market microstructure of the contract prior to its failure—specifically, the number of trades, trading volume, open interest, bid–ask spread, and volatility. We find a steady decline in the mean volume, open interest, and number of trades as the contracts near their demise. The bid–ask spread of the contract also widens. Investigations of the mutual offset feature of the Brent Crude Oil futures contract between SGX and the International Commodity Exchange (ICE) provides evidence that trading volume, open interest, and the number of trades increase significantly during 4:00–5:45 PM local time when mutual offset is available. [ABSTRACT FROM AUTHOR]

Market microstructure invariance (MMI) stipulates that trading costs of financial assets are driven by the volume and volatility of bets, but these variables are inherently difficult to identify. With futures transactions data, we estimate bet volume as the trading volume of brokerage firms that trade on behalf of their clients and bet volatility as the trade‐related component of futures volatility. We find that the futures bid–ask spread lines up with bet volume and bet volatility as predicted by MMI, and that intermediation by high‐frequency traders does not interfere with the MMI relation. [ABSTRACT FROM AUTHOR]

Using a sample of 7,919 banks from 30 OECD countries over 1995–2019, we examine the impact of low interest rates on banks' net interest margins. Our results confirm a positive relationship between interest rates and interest margins, which is stronger in a low interest rate environment. In more concentrated markets, however, interest margins are less sensitive to the level of interest rates, as interest rate sensitivities of income and expense margins match. But our results also suggest that the effect of market concentration on the link between interest rates and interest margins is weaker when interest rates approach zero. [ABSTRACT FROM AUTHOR]

We examine the relation between excess corporate cash holdings and equity option market liquidity from January 3, 2005 to December 31, 2019. We show that the level of cash reserve in excess of what can be captured by firm characteristics significantly explains stock option liquidity. Trading volume and option open interest increase in companies with a higher magnitude of excess cash, whereas the bid–ask spreads of stock options decline in excess cash. Our findings confirm the theoretical prediction that excess cash improves option market liquidity as it reduces adverse selection problems caused by uncertainty in firm valuations. This relation remains more pronounced with put options, out‐of‐the‐money contracts, and short‐maturity contracts. In addition, excess cash has a stronger impact on option liquidity within firms that have a greater degree of informed trading and during high‐volatility periods in financial markets. Our results show that when uncertainty about firm prospects rises, excess cash becomes more valuable and affects option market liquidity. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate whether the cash flow forecasts (CFF) of analysts can disseminate valuable information to the information environments of companies. Design/methodology/approach: The author uses empirical archival methodology to conduct differences-in-difference analyses. Findings: It is found that information asymmetry decreases in the treatment group following the initiation of CFF during the postperiod, which is consistent with the hypothesis of this paper. Originality/value: To the best of the author's knowledge, this study is the first among the cash flow forecast studies to demonstrate the usefulness of CFF in the mitigation of information asymmetry, a friction that is widespread in capital markets. [ABSTRACT FROM AUTHOR]

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