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We develop a dynamic model of corporate investment and financing, in which shocks to the value of collateralizable assets generate variation in firms' debt capacity. We show that the degree of similarity among firms' financial flexibility forecasts cross-sectional variation in return correlation. We test the implications of the model with firm-level data in two empirical analyses using i) an instrumental variable approach based on shocks to the value of collateralizable corporate assets and ii) the outbreak of the COVID-19 crisis as an event study. We find that firms in the same percentile of the cross-sectional distribution of financial flexibility have 62% higher correlation in stock-return residuals than firms 50 percentiles apart. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

I examine the impact of the opioid epidemic on subprime auto lending. Using a difference-in-differences framework, I find that county-level increases in opioid abuse cause an increase in loan defaults. Moreover, I find that traditional credit scoring attributes (e.g., FICO score) fail to predict loan performance deterioration associated with opioid addiction. The weak predictive performance of traditional credit measures and the resulting higher default rates generate a negative externality for borrowers in opioid-afflicted areas, as evidenced by 5.7% higher loan costs for subprime borrowers. [ABSTRACT FROM AUTHOR]

Going public by merging with a Special Purpose Acquisition Company (SPAC) is much more expensive than conducting a traditional IPO. We rationalize why some companies merge with a SPAC by listing the potential benefits. We analyze the agency problems that certain SPAC features address. SPAC IPO investors and deal sponsors have earned remarkably high annualized average returns, although we warn that recent deals are likely to disappoint. Public investors in the merged companies have earned very low market-adjusted returns on an equally weighted basis, although high redemptions on the worst deals have limited the amount of money that they lost. 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 study how the U.S. government's anti-counterfeiting enforcement actions through Special 301 Reports influence U.S. businesses. We show that anti-counterfeiting enforcement in foreign countries improves U.S. firms' sales, profitability, and valuations. Firms significantly reduce capital and research and development investments when their brands and products are protected from counterfeiting activities. Anti-counterfeiting enforcement measures also improve brand asset value, brand profitability, brand inventiveness, market penetration, and customer loyalty. [ABSTRACT FROM AUTHOR]

We hypothesize that corporate takeover markets create significant constraints for short sellers. Both short sellers and corporate bidders often target firms with declining economic prospects. Yet, a target firm's stock price generally increases upon a takeover announcement, resulting in losses for short sellers. Therefore, short sellers should require higher rates of return when the takeover likelihood is higher. Consistent with this prediction, the return predictability of monthly short interest increases with industry-level takeover probability and decreases as takeover defenses are implemented. Our results suggest that efficient takeover markets create trading frictions for short sellers and can therefore inhibit overall market efficiency. [ABSTRACT FROM AUTHOR]

Using novel merger valuation data, we show that firms selected by investment banks as "comparable peers" are more than twice as likely to later become takeover targets themselves compared to matched control firms. Peer firms not subsequently acquired attract more institutional ownership and analyst coverage, deliver strong operating performance, reduce investments, and increase payouts. Investors are inattentive, though, to peer identification at the time of merger filings' public disclosure. A portfolio that longs peers and shorts controls earns up to 15.6 |$\%$| alpha annually, which mainly comes from the long leg and is difficult to explain by short-sale constraints. 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]

Hand-collecting credit line drawdowns that firms classify as long-term debt, we first document how long-term drawdowns rise with high investment needs or weak external capital market conditions. Nearly all drawdown proceeds finance long-term investment, including M&A activity. Unrated and lower-rated firms rely more on long-term drawdowns than high or very poorly rated firms. We further find that credit lines have tighter covenants than terms loans. Drawdowns are repaid fairly quickly and often refinanced with other long-term debt. Our findings support the monitored liquidity insurance theory of credit lines and highlight that long-term drawdowns act as a valuable bridge financing mechanism. [ABSTRACT FROM AUTHOR]

We empirically evaluate 20 prominent contributions across a broad range of areas in the empirical corporate finance literature. We assemble the necessary data and apply a single, simple econometric method, the connected-groups approach of Abowd et al. to appraise the extent to which prevailing empirical specifications explain variation of the dependent variable, differ in composition of fit arising from various classes of independent variables, and exhibit resistance to omitted variable bias and other endogeneity problems. We assess empirical performance across a wide spectrum of areas in corporate finance and indicate varying research opportunities for empiricists and theorists. [ABSTRACT FROM AUTHOR]

This paper shows that standard disaster risk models are inconsistent with movements in stock market volatility and credit spreads during disasters. We resolve this shortcoming by incorporating persistent macroeconomic crises into a structural credit risk model. The model successfully captures the joint dynamics of aggregate consumption, financial leverage, and asset market risks, both unconditionally and during crises. Leverage strongly amplifies fundamental shocks by continuing to rise while crises endure. We structurally estimate the model and show that it replicates the firm-level implied volatility curve and its cross-sectional relation with observable proxies of default risk. [ABSTRACT FROM AUTHOR]

To achieve a competitive advantage, corporations are growingly adopting strategies to effectively promote their market demand. Trade credit payment and pricing strategies provided by corporates can efficiently influence customers' purchasing behavior. Although granting a trade credit strategy can increase corporations' market share, such a strategy is a risk-based financing program for corporations. Therefore, corporates should choose whether to use trade credit financing in their long-term. This paper proposes an analytical model to investigate the evolutionary behaviors of retailers regarding pricing and trade credit strategies in the long term. In the study under investigation, retailers can use two financing strategies: risk-based trade credit and non-trade credit (i.e., pricing). This study provides both numerical and analytical findings. Our findings demonstrate that the risk-based trade credit strategy is the stationary financing solution for retailers in the long term. The result indicates that when customers are financially constrained, providing a trade credit scheme to customers is a successful marketing policy in both short-term and long-term frameworks. [ABSTRACT FROM AUTHOR]

What drives groundbreaking research in economics? Nobel-prize-winning work has had an important impact on public policies, but we still do not understand well what drives such breakthroughs. We collect data on all nobel-prize discoveries in economics to address this question. We find that major advances in the field of economics are brought about by methodological innovation: by developing new and improved research methods. We find that developing for example econometrics in 1933, randomised controlled trials in 1948 and new game theory methods in 1950 were essential to opening the new fields of corporate finance, experimental economics and information economics, respectively. We identify the development of new methods as the main mechanism driving new discoveries and research fields. Fostering this general mechanism (generating novel methods) holds the potential to greatly increase the rate at which we make new breakthroughs and fields. We also show that many of the main methods of economics – such as randomised controlled trials, natural experiments, regression discontinuity, instrumental variables and other statistical methods – had been developed and used in other fields like public health, before economists adopted them. This shift towards more powerful empirical methods in the field has important implications on developing new and better methods and adopting them from related fields to make new advances more rapidly. [ABSTRACT FROM AUTHOR]

Developing green finance is an important measure for China to achieve a win-win situation between economy and ecology. This study adopts the implementation of Green Credit Guidelines (GCG) in China in 2012 as an intervening event and empirically examines its relative impact on corporate financing and investment using Difference-in-Difference and Difference-in-Difference-in-Difference models. Employing the financial data of A-share listed enterprises from 2009 to 2015, we find that the implementation of GCG restrains the financing and investment behavior of heavily polluting enterprises. The effects of GCG are influenced by institutional factors, and GCG has a more inhibitory effect in the higher polluting, state-owned, and eastern region registered enterprises. This result reveals the importance of GCG in adjusting financial leverage and promoting environmental protection. Policy implications, such as governments should improve incentive and punishment mechanism for heavily polluting enterprises and financial institutions should innovate in green credit business, are proposed. [ABSTRACT FROM AUTHOR]

With the rapid development of the economy, a large amount of financial data will be generated during the continuous growth of enterprises. However, due to the explosive growth of the financial data range index, the use of machine learning methods to mine and analyse financial data is extremely important. Among them, accurate financial risk evaluation is an effective measure to prevent and resolve corporate financial crises. In this article, we use fuzzy clustering method to establish a financial risk early warning and evaluation model. Specifically, we use fuzzy C‐mean (FCM), half‐suppressed FCM, and interval FCM clustering algorithms‐based state construction financial risk early warning and evaluation models, to give an evaluation from two aspects of corporate financial indicators and non‐financial indicators system. In order to verify the feasibility and effectiveness of the fuzzy clustering algorithms used in financial data mining, we conducted experiments in financial data mining and early warning in real estate companies and ST companies. The experimental results show that the fuzzy clustering algorithms represented by the FCM clustering algorithm has achieved good results in financial data mining, and can achieve good results in financial risk analysis and financial risk early warning. [ABSTRACT FROM AUTHOR]

Artificial intelligence methods based on deep learning (DL) have recently made significant progress in many different areas including free text classification and sentiment analysis. We believe that corporate governance is one of these areas, where DL can generate very valuable and differential knowledge, for example, by analyzing the biographies of independent directors, which allows for qualitative modeling of their profile in an automatic way. For this technology to be accepted it is important to be able to explain how it generates its results. In this work we have developed a six-dimensional labeled dataset of independent director biographies, implemented three recurrent DL models based on LSTM and transformers along with four ensembles, one of which is an innovative proposal based on a multi-layer perceptron (MLP), trained them using Spanish language and economics and finance terminology and performed a comprehensive test study that demonstrates the accuracy of the results. We have also performed a complete study of explainability using the SHAP methodology by comparatively analyzing the developed models. We have achieved a mean error (MAE) of 8% in the modeling of the open text biographies, which has allowed us to perform a case study of time analysis that has detected significant variations in the composition of the Standard Expertise Profile (SEP) of the boards of directors, related to the crisis of the period 2008–2013. This work shows that DL technology can be accurately applied to free text analysis in the finance and economic domain, by automatically analyzing large volumes of data to generate knowledge that would have been unattainable by other means. [ABSTRACT FROM AUTHOR]

Universal health coverage relies on providing essential medical services and shielding individuals from financial risks. Our study assesses the progressivity of out-of-pocket (OOP) payments, identifies factors contributing to healthcare expenditure inequality, and examines catastrophic health expenditures (CHE) prevalence in Kazakhstan from 2018 to 2021. Using retrospective analysis of National Statistics Bureau data, we employed STATA 13 version for calculations CHE incidence, progressivity, Lorenz and concentration curves. In 2020–2021, OOP expenditures in Kazakhstan decreased, reflecting a nearly twofold reduction in the CHE incidence to 1.32% and 1.24%, respectively. However, during these years, we observe a transition towards a positive trend in the Kakwani index to 0.003 and 0.005, respectively, which may be explained by household size and education level factors. Increased state financing and quarantine measures contributed to reduced OOP payments. Despite a low healthcare expenditure share in gross domestic product, Kazakhstan exhibits a relatively high private healthcare spending proportion. The low CHE incidence and proportional expenditure system suggest private payments do not significantly impact financial resilience, prompting considerations about the role of government funding and social health insurance in the financing structure. [ABSTRACT FROM AUTHOR]

This article thoroughly examines the influence of digital transformation on the efficiency of corporate supply chains. As global economic integration accelerates and technological innovations deepen, digital transformation has become key to enhancing core corporate competitiveness. This research, utilizing data from A-share listed companies in China between 2007 and 2022, analyzes how companies improve supply chain efficiency through digital transformation. Furthermore, the study establishes a theoretical framework that demonstrates how digital transformation facilitates supply chain efficiency from the perspectives of internal governance and external competition. The research indicates that digital transformation plays a key role in significantly enhancing supply chain efficiency. Furthermore, the results of the mechanism analysis confirmed that digital transformation contributes to enhancing corporate supply chain efficiency by improving the level of corporate governance and the degree of market competition. The study also finds that the effect of digital transformation on supply chain efficiency varies with different corporate backgrounds, indicating its heterogeneous impact. Lastly, an analysis of economic consequences shows that the increased supply chain efficiency resulting from digital transformation can reduce future external transaction costs, strengthening the company's market position and financial performance. This research provides strategic guidance for firms to develop robust strategies amid the digital wave and offers strong policy recommendations for promoting digital supply chain management and enhancing market adaptability. [ABSTRACT FROM AUTHOR]

Using mean and variance causality analysis, this study examines the volatility relationship between Turkish and G7 stock markets. Weekly return data from May 29, 2009, to June 6, 2023, is utilized for the analysis. The Hong mean and variance causality analysis method is employed as the methodology. Based on the results of the study, Turkey and Japan's stock markets have a significant mean causality relationship. Moreover, the variance causality analysis demonstrates a strong relationship between Turkey and stock markets of Canada, France, Germany, Japan, and the United States. The findings contribute to portfolio diversification strategies and highlight the importance of understanding the dynamics of international financial markets. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-feedback process that links watershed plan actions with raw water charges. A dedicated decision support model is created to implement this process and allow long-term financial sustainability analysis. The model offers real-time analysis of various watershed actions using customizable prioritization criteria integrated with different water pricing configurations, including user-polluter pays and beneficiary-pays principles, as well as other funding sources. Application to the Piracicaba–Capiravi–Jundiaí Basins in Brazil shows how important water management and water quality improving actions can be funded through water charges and how finance gaps can be resolved through a shared Vision Modelling approach. [ABSTRACT FROM AUTHOR]

Objectives Analysis of laboratory value often lacks assessment of the laboratory's impact on quality of care. In this study, we aimed to determine the impact of bringing a heparin-induced thrombocytopenia (HIT) antibody assay in-house on a quality metric—patient hospital length of stay (LOS)—and assess any associated cost savings. Methods A retrospective review of patient visits with a HIT antibody assay over a 7-year period determined the mean LOS in send-out vs in-house HIT antibody assay cohorts as well as cohorts of positive and negative results. Our systemwide mean LOS and metrics of acuity were analyzed. We performed a financial analysis of estimated cost savings. Results We found a mean LOS reduction of 3.97 days in the in-house cohort, with no evidence of a systemwide LOS decrease or a decline in patient acuity. This reduction was largely driven by a reduction in LOS among patients with a negative assay result. We found an estimated total cost savings of $3.9 million and an estimated mean savings per patient of $7,305, despite escalating health care costs over time. Conclusions We demonstrated a reduction in LOS following the introduction of an in-house HIT antibody assay that cannot be attributed to either systemwide initiatives or reduced patient acuity and was driven largely by patients with negative assays. This reduction was associated with significant estimated cost savings. [ABSTRACT FROM AUTHOR]

Traditionally, entrepreneurs build long-term companies that generate profits by selling goods and services. Nowadays, entrepreneurs are building companies solely to sell them to larger corporations to profit from early 'exits' due to the glorifying media characteristic. Based on an Israeli case study, this article examines how media outlets portray the 'exit' catchphrase. An analysis of semi-structured interviews with Israeli tech journalists and a thematic analysis of news articles is used to identify the three main narratives appearing in the media for encouraging exits: High-tech entrepreneur (individual), company (entity), and the industry (macro point of view). [ABSTRACT FROM AUTHOR]

This work surveys the use of directed weighted graphs in conducting comparative static analyses. The paper discusses the implementation of a computer-aided process for building spreadsheet-based graph models for inter- and intra-regional financial flows. The graph-theoretic techniques are programmed to enable the interactive visualization and analysis of financial data using Wolfram technologies (i.e., Mathematica software v. 11.3 or later, Wolfram player v. 12 or later). The paper describes the workflow for several interactive visualizations applicable to financial networks. The author provides four programs, written in the Wolfram language, that customize input–output financial models by combining the Manipulate command with built-in Mathematica functions and functions of the IGraph package (IGraph/M). The study is a tutorial article for the generation of a suite of visual schemes that provide patterns, practices, and roadmaps of the financial markets across the globe. [ABSTRACT FROM AUTHOR]

Background: The popularity of robotic-assisted surgery for rectal cancer is increasing, but its superiority over the laparoscopic approach regarding safety, efficacy, and costs has not been well established. Methods: A retrospective single-center study was conducted comparing consecutively performed robotic-assisted and laparoscopic surgeries for rectal cancer between 1 January 2016 and 31 September 2021. In total, 125 adult patients with sporadic rectal adenocarcinoma (distal extent ≤ 15 cm from the anal verge) underwent surgery where 66 were operated on robotically and 59 laparoscopically. Results: Severe postoperative complications occurred less frequently with robotic-assisted compared with laparoscopic surgery, as indicated by Clavien–Dindo classification grades 3b–5 (13.6% vs. 30.5%, p = 0.029). Multiple logistic regression analyses after backward selection revealed that robotic-assisted surgery was associated with a lower rate of total (Clavien–Dindo grades 1–5) (OR = 0.355; 95% CI 0.156–0.808; p = 0.014) and severe postoperative complications (Clavien–Dindo grades 3b–5) (OR = 0.243; 95% CI 0.088–0.643; p = 0.005). Total inpatient costs (median EUR 17.663 [IQR EUR 10.151] vs. median EUR 14.089 [IQR EUR 12.629]; p = 0.018) and surgery costs (median EUR 10.156 [IQR EUR 3.551] vs. median EUR 7.468 [IQR EUR 4.074]; p < 0.0001) were higher for robotic-assisted surgery, resulting in reduced total inpatient profits (median EUR −3.196 [IQR EUR 9.101] vs. median EUR 232 [IQR EUR 6.304]; p = 0.004). Conclusions: In our study, robotic-assisted surgery for rectal cancer resulted in less severe and fewer total postoperative complications. Still, it was associated with higher surgery and inpatient costs. With increasing experience, the operative time may be reduced, and the postoperative recovery may be further accelerated, leading to reduced surgery and total inpatient costs. [ABSTRACT FROM AUTHOR]

Industrial technological progress, as an essential industrial-technological and institutional phenomenon, brings with it the possibility of high profits for firms but also implies new norms and rules of competition, which affect the willingness and propensity of firms to bear the costs of undertaking venture capital projects. This study empirically investigates the causal impact of industrial-technological progress on corporate risk-taking and the mechanism of digital financial growth on the relationship between the two, based on data from China's A-share listed businesses from 2011 to 2020. This paper finds that (1) industrial technological progress improves enterprise risk-taking levels. Moreover, digital financial development has an incentive effect on industrial technological progress and enterprise risk-taking levels. (2) Industrial technological progress under digital financial development generates financing constraint relaxation effects, input capital return enhancement effects, and innovation performance incentive effects, increasing enterprise risk-taking. (3) The positive moderating effect of digital financial development on the relationship between industrial technological progress and the risk-taking level of enterprises in the eastern regions and enterprises in the high-tech industry is more prominent. The study's findings provide a theoretical foundation and policy insights on the crucial elements of industrial-technological progress and enterprises' increased ability to take risks throughout the development of digital finance. [ABSTRACT FROM AUTHOR]

This work investigates the SMEs of the Italian automotive supply chain, testing the impact of an innovation policy created with the target of supporting investments in Industry 4.0. In detail, the authors examine the impact of this policy on SMEs' access to the capital market (i) and their financial performance (ii). The former is an appropriate proxy to measure the effectiveness of such policy; while the latter is a good candidate to evaluate the sustainability of this program after its conclusion. According to our results, SMEs with access to the incentives of this innovation policy are those observations with higher rates of admission to external financial resources (i.e., 0.3 higher debt ratio) and higher financial performance (i.e., 2 times the odds of having a positive profit, and 0.01 higher financial indexes). Hence, evidence suggests a positive evaluation of this public intervention, as well as positive expectations on its sustainability. In view of these insights, several policy implications are formulated to lead governments into the fourth industrial revolution. [ABSTRACT FROM AUTHOR]

This study aims to analyze the financial performance of the National Amil Zakat Agency of Lumajang Regency in 2018 to 2022. In analyzing financial performance in this study using activity ratios, efficiency ratios, amil fund ratios, liquidity ratios, and growth ratios in accordance with guidelines issued by the National Amil Zakat Agency's strategic study center. This research uses a qualitative descriptive approach. The types and sources of data used are secondary data in the form of financial reports of BAZNAS Lumajang Regency in 2018 - 2022 with data collection methods, namely documentation. The results showed that the average activity ratio of BAZNAS Lumajang Regency was effective, and showed good results in terms of collecting and distributing Zakat, Infaq, Sadaqah (ZIS) funds. Based on the average efficiency ratio, it shows efficient results even though the ratio of operational costs to total amil rights is still inefficient. Based on the ratio of amil funds good results were obtained. Based on the average results of the liquidity ratio is not good because it indicates the obligation to distribute ZIS funds that have not been realized. Based on the ratio of collection growth the results obtained also show not good. [ABSTRACT FROM AUTHOR]

Our article offers insights on the role of operational research (OR) in understanding financial and economic systems' risks and dynamics. It presents the latest methods in OR to address risks and uncertainties in these systems, covering topics such as options pricing, portfolio optimization, banking resilience, and the analysis of financial and economic co-movements. The included studies utilize advanced analytical tools, like stochastic programming, decision analysis, and machine learning, to create robust models for complex systems. These contributions aim to be valuable for academics, practitioners, policymakers, and regulators in the field. [ABSTRACT FROM AUTHOR]

In this study, we explored the dynamic field of fuzzy logic and artificial intelligence (AI) in financial analysis from 1990 to 2023. Utilizing the bibliometrix package in RStudio and data from the Web of Science, we focused on identifying mathematical models and the evolving role of fuzzy information granulation in this domain. The research addresses the urgent need to understand the development and impact of fuzzy logic and AI within the broader scope of evolving technological and analytical methodologies, particularly concentrating on their application in financial and banking contexts. The bibliometric analysis involved an extensive review of the literature published during this period. We examined key metrics such as the annual growth rate, international collaboration, and average citations per document, which highlighted the field's expansion and collaborative nature. The results revealed a significant annual growth rate of 19.54%, international collaboration of 21.16%, and an average citation per document of 25.52. Major journals such as IEEE Transactions on Fuzzy Systems, Fuzzy Sets and Systems, the Journal of Intelligent & Fuzzy Systems, and Information Sciences emerged as significant contributors, aligning with Bradford's Law's Zone 1. Notably, post-2020, IEEE Transactions on Fuzzy Systems showed a substantial increase in publications. A significant finding was the high citation rate of seminal research on fuzzy information granulation, emphasizing its mathematical importance and practical relevance in financial analysis. Keywords like "design", "model", "algorithm", "optimization", "stabilization", and terms such as "fuzzy logic controller", "adaptive fuzzy controller", and "fuzzy logic approach" were prevalent. The Countries' Collaboration World Map indicated a strong pattern of global interconnections, suggesting a robust framework of international collaboration. Our study highlights the escalating influence of fuzzy logic and AI in financial analysis, marked by a growth in research outputs and global collaborations. It underscores the crucial role of fuzzy information granulation as a mathematical model and sets the stage for further investigation into how fuzzy logic and AI-driven models are transforming financial and banking analysis practices worldwide. [ABSTRACT FROM AUTHOR]

This study explored the multi-layered nature of the work–life experiences of dual income parents in the Philippines by attuning to the voices within their narratives. The border theory was utilized as a framework in understanding these experiences. Semi-structured interviews were conducted among twelve individuals in dual income relationships. All participants had at least one child. The Listening Guide was used to analyze the qualitative data. Four stories emerged from the analysis: stories of financial struggle and security, stories of childcare, stories of connection, and stories of self-denial and self-care. Various voices within these stories were identified and discussed. Findings highlight the importance of balance and social support for dual income couples, as well as the relevance of socio-cultural nuances that impact work–life experiences. Practical implications, particularly how organizations can support their employees in dual income relationships, are discussed. [ABSTRACT FROM AUTHOR]

The regional industry network (RIN) is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region. For most investors and financial analysts lacking extensive experience, the decision-support information provided by industry networks may be too vague. Conversely, RINs express more detailed and specific industry connections both within and outside the region. As RIN analysis is domain-specific and current financial network analysis tools are designed for generalized analytical tasks and cannot be directly applied to RINs, new visual analysis approaches are needed to enhance information exploration efficiency. In this study, we collaborated with domain experts and proposed V4RIN, an interactive visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data. Through multiple views in the system panel, users can comprehensively explore the structure, geographical distribution, and spatiotemporal variations of the RIN. Two case studies were conducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system. [ABSTRACT FROM AUTHOR]

There has been a secular decline in published research on corporate finance topics over the past decade or so. While this raises questions about the vitality of the field going forward, I argue that a number of recent developments represent important new directions for research in corporate finance. These developments have begun to have an impact on scholarly activity and I expect this to continue in the near future. [ABSTRACT FROM AUTHOR]

Technology is paving innovative ways to provide financial services and improve the efficiency of financial systems. Since it is a dynamic field of research, it is important to look back on the ever-changing field of financial technology. This paper aims to analyse the existing research on financial technology through a bibliometric approach. The data were gathered from the Scopus database using secondary sources, and the analysis is presented descriptively along with science-mapping techniques. This paper offers an overview of the influential journals, authors, and organizations contributing to financial technology research. The study focuses on citation, cocitation, bibliographic coupling, and coauthorship analysis within the collected corpus. It is worth noting that this study is limited by the use of only one database, Scopus and excludes grey literature, this could lead to skewed results but this can be an arena for future research. [ABSTRACT FROM AUTHOR]

This study aims to provide empirical evidence regarding the comparison of the value of the Early Warning System and Risk-Based Capital in measuring the financial performance of insurance companies before and during the COVID-19 pandemic. The Early Warning System measurement indicator uses the claim expense ratio. Sampling using purposive sampling method and obtained a sample of 18 insurance companies listed on the Indonesia Stock Exchange (IDX) for the period 2017-2022. The type of data used is secondary data in the form of financial reports collected through the company's official website and the Indonesia Stock Exchange (IDX). The data analysis method uses non-parametric statistics, namely the Wilcoxon Sign Rank Test with the help of SPSS version 27. The results prove that there is a significant increase in the value of the Early Warning System during the pandemic compared to before the Covid-19 pandemic. The Risk-Based Capital value did not experience a significant decrease during the pandemic compared to before the Covid-19 pandemic. In addition, in this study, the Early Warning System is considered more accurate than Risk-Based Capital because it has a smaller standard error. [ABSTRACT FROM AUTHOR]

This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The bivariate VAR system comprises asset returns and a further prediction variable, such as the dividend-price ratio, and allows pinning down the question of return predictability to the value of one particular model parameter. We develop a new shrinkage type prior for this parameter and compare our Bayesian approach to ordinary least squares estimation and to the reduced-bias estimator proposed in Amihud and Hurvich (2004. "Predictive Regressions: A Reduced-Bias Estimation Method." Journal of Financial and Quantitative Analysis 39: 813–41). A simulation study shows that the Bayesian approach dominates the reduced-bias estimator in terms of observed size (false positive) and power (false negative). We apply our methodology to a system comprising annual CRSP value-weighted returns running, respectively, from 1926 to 2004 and from 1953 to 2021, and the logarithmic dividend-price ratio. For the first sample, the Bayesian approach supports the hypothesis of no return predictability, while for the second data set weak evidence for predictability is observed. Then, instead of the dividend-price ratio, some prediction variables proposed in Welch and Goyal (2008. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction." Review of Financial Studies 21: 1455–508) are used. Also with these prediction variables, only weak evidence for return predictability is supported by Bayesian testing. These results are corroborated with an out-of-sample forecasting analysis. [ABSTRACT FROM AUTHOR]

Recent years have seen increasing initiatives involving more applications of real options to value the strategizing process. These initiatives, referred to as Real Option Theory (ROT), imply greater inclusiveness of simple and advanced real options in strategizing processes. While substantial theoretical groundwork on ROT has been laid in corporate finance, and both qualitative and quantitative studies on ROT in business management journals are appearing on an increasing basis, there remain significant opportunities for more research on strategic synergism in patent-based acquisitions. In this vein, the current paper aims to explore a rainbow real options application (real options that are exposed to two sources of uncertainty) to measure patent-based collaborative synergies in high-tech mergers and acquisitions. Having conducted the deviant case study of ZOOX start-up's acquisition by Amazon.com in 2020, this paper justifies the proposition of the employability of rainbow real options for the valuation of network and relational synergies in highly risky patent-based acquisitions with multiple uncertainties. [ABSTRACT FROM AUTHOR]

Public–private partnership (PPP) is a prominent tool for sustainable infrastructure development. However, the positive contributions of PPPs toward the attainment of sustainable, climate resilience and zero-carbon infrastructure projects are hampered by poor financial risk management. This problem is more prevalent in developing countries like Ghana where private investment inflow has plummeted due to the COVID-19 recession and poor project performance. Thus, this study aims to assess the key financial risk management strategies in ensuring sustainable PPP infrastructure projects in Ghana. The study utilised primary data from PPP practitioners in Ghana solicited through survey questionnaires. Factor analysis, mean scores and fuzzy synthetic analysis are the data analysis techniques for this study. The results revealed that sustainable and green funding models, effective cost-reduction initiatives, a competent team with committed leadership and emerging technologies and regulations constitute the key strategies for managing the financial risks of sustainable PPP infrastructure projects. Although future studies must expand the scope of data gathering, the findings of the study enrich the theoretical understanding of financial risks in sustainable investments in PPP infrastructures. Relevant remedies that will aid the development of practical financial risk management guidelines are also provided in this study for PPP practitioners. [ABSTRACT FROM AUTHOR]

This research study provides insights on how activist hedge funds perform valuation analyses of target firms. Relevant data was hand‐collected from a sample of activist hedge fund presentations. Based on the hedge funds' valuation analyses, the undervaluation of the target firms amounts to approximately 30%, compared to the targets' current share price. Besides, activist investors derive a value enhancement potential from their proposed strategies of approximately 70% to the targets' current share price. These valuation results rely predominately on trading multiples. The dominant multiples are Enterprise value/EBITDA (EV/EBITDA) and Price/Earnings (P/E). Further, applied multiples are mainly forward‐looking, and the predicted performance measures are primarily consensus estimates. Besides, hedge funds sometimes adjust multiples arbitrarily to increase the comparability. Our results confirm that short‐term investors rely predominately on pricing analyses in their valuations. [ABSTRACT FROM AUTHOR]

By providing important indicators, financial indices help investors make educated judgements regarding their assets, much like vital sign monitors for the financial markets. The best way for investors to keep up with the market and make strategic adjustments is to keep an eye on these indexes. Researching the most important financial indexes for making educated investing decisions is, thus, quite relevant. Finding the most essential financial indices from an investing standpoint and assigning a weight to each of those indexes are the main goals of this research. A weighted score is derived by combining four financial indices in a Multi-Criteria Decision-Making (MCDM) technique. These objectives are then pursued. Triangular Fuzzy Numbers (TFNs) and the Fuzzy Analytic Hierarchy Process (F-AHP) are used to determine the weights of criteria in this technique. Using these methods together, the research hopes to provide a thorough analysis of the role that different financial indexes have in informing investment choices. This study emphasizes the paramount importance of considering the Price Earning to Growth (PEG) ratio when making investment decisions, followed by the Debt Equity Ratio. Price to Book Value and Dividend Yield, while relevant, carry comparatively less weightage in the overall assessment. Investors are advised to use these insights as a guideline in their financial analysis and decision-making processes. [ABSTRACT FROM AUTHOR]

Whether proxied by the amount raised or the probability that this amount exceeds the soft cap, the factors driving the fundraising success of token offerings have changed considerably over the past 4 years. Possible factors include target market capitalization for the token, genuine signals of the venture's public credibility, price and momentum of ether, domicile of the venture and distribution of the token. There is a large previous literature on this topic, rooted in the entrepreneurial finance and crowdfunding literature, with numerous hypotheses based on signalling theory in its broadest sense. But due to substantial differences in samples, which end before 2020, the results are not only mixed but out of date. We have built the largest and most complete dataset on token offerings, spanning 2017 and 2022. The determinants of the amount raised were robust to different subsamples until the market virtually disappeared during the Covid pandemic. But it resumed in early 2021 and within 12 months had reached an all‐time high in terms of number if not market capitalization of new offerings. During this period the market changed completely, with success now depending mostly on the type of token offering and the choice of launchpad platform. [ABSTRACT FROM AUTHOR]

This article empirically examines how political connections (PCs) affect a firm's media reaction after corporate fraud. Using data for Chinese listed companies from 2008 to 2021, we find that the media reports more positively for firms with PCs than for others that do not possess such advantages after the enforcement against fraud. The results are robust to a series of robustness checks and endogeneity corrections. When decomposing media reports, we find that PCs only facilitate positive media coverage but do not impede negative media coverage, which is more pronounced in state‐controlled media. This suggests that PCs protect firms' branding by facilitating positive media reports rather than withholding bad news. Moreover, we find this protective effect is more pronounced in firms with stronger PCs, weaker anti‐corruption regulation, lighter punishment for fraud, private ownership, and more donations. Further, the consequences analysis shows that this kind of protective effect significantly increases the probability of future fraud and stock price crashes. Our findings present a new perspective on the role of PCs and provide evidence for political bias in media coverage. [ABSTRACT FROM AUTHOR]

In contrast to post-listing dividend initiations, the corporate finance literature offers limited coverage of pre-IPO dividends. The present inquiry assesses IPOs in Hong Kong where both pre-listing dividend distributions and IPO vendor sales occur with regularity. As a means of distributing cash to controllers, pre-IPO dividends potentially offer an exit route for insiders without the need for a conspicuous vendor sale at IPO. This study extends the limited evidence on the "Pre-IPO Dividend puzzle" (Martin and Zeckhauser 2010) and the information signaling role of prelisting cash distributions (Wang et al. 2023) in two important ways. First, unlike the mainland PRC, where regulatory proscriptions discourage secondary offers, vendor sales occur often in Hong Kong. Second, in contrast to the US, mainland PRC, and most other jurisdictions, Hong Kong's tax environment neither encourages nor discourages dividends relative to capital gains. For individual investors, the Hong Kong SAR Government imposes a zero rate of tax on both stock income and capital gains. Most issuers in the Hong Kong market during the study period demonstrated a significant record of pre-IPO profits. Accordingly, the incidence of cash payouts is at a high rate relative to rates evident in other major IPO locales. Hong Kong therefore provides a novel setting for investigation of pre-IPO dividend behavior. Present study findings suggest that pre-IPO cash distributions do not substitute for IPO vendor sales. Post-listing cash distributions also exhibit little to no association with vendor sales. However, ex-ante uncertainty, as proxied by offer price range, bears positive correlation with the frequency of post-listing dividend omissions. Finally, issuers distributing cash pre-IPO demonstrate greater frequency of pay-outs post-IPO. [ABSTRACT FROM AUTHOR]

We propose a tractable model of dynamic investment, spinoffs, financing, and risk management for a multidivision firm facing costly external finance. Our analysis formalizes the following insights: (i) Within‐firm resource allocation is based not only on divisions' productivity, as in winner‐picking models, but also their risk; (ii) firms may voluntarily spin off productive divisions to increase liquidity; (iii) diversification can reduce firm value in low‐liquidity states, as it increases the spinoff cost and hampers liquidity management; (iv) corporate socialism makes liquidity less valuable; and (v) division investment is determined by the ratio between marginal q$q$ and marginal value of cash. [ABSTRACT FROM AUTHOR]

Domestically listed Chinese (A‐share) firms have lower stock returns than externally listed Chinese, developed, and emerging country firms during 2000 to 2018. They also have lower net cash flows than matched unlisted Chinese firms. The underperformance of both stock and accounting returns is more pronounced for large A‐share firms, while small firms show no underperformance along either dimension. Investor sentiment explains low stock returns in the cross‐country and within‐A‐share samples. Institutional deficiencies in listing and delisting processes and weak corporate governance in terms of shareholder value creation are consistent with the underperformance in stock returns and net cash flows. [ABSTRACT FROM AUTHOR]

We study the extent to which credit index (CDX) options are priced consistent with S&P 500 (SPX) equity index options. We derive analytical expressions for CDX and SPX options within a structural credit‐risk model with stochastic volatility and jumps using new results for pricing compound options via multivariate affine transform analysis. The model captures many aspects of the joint dynamics of CDX and SPX options. However, it cannot reconcile the relative levels of option prices, suggesting that credit and equity markets are not fully integrated. A strategy of selling CDX volatility yields significantly higher excess returns than selling SPX volatility. [ABSTRACT FROM AUTHOR]

We develop a model in which a firm's manager can voluntarily disclose to privately informed investors. In equilibrium, the manager only discloses sufficiently favorable news. If the manager is known to be informed but disclosure is costly, the probability of disclosure increases with market liquidity and the stock trades at a discount relative to expected cash flows. However, when investors are uncertain about whether the manager is informed, disclosure can decrease with market liquidity and the stock can trade at a premium relative to expected cash flows. Moreover, contrary to common intuition, public information can crowd in more voluntary disclosure. [ABSTRACT FROM AUTHOR]