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1 – 10 of 12Maggie Foley, Richard J. Cebula, John Downs and Xiaowei Liu
The purpose of the current study is to identify variables that, when integrated into the random effects parametric survival model, could be used to forecast the failure rate of…
Abstract
Purpose
The purpose of the current study is to identify variables that, when integrated into the random effects parametric survival model, could be used to forecast the failure rate of small banks in the USA. A bank’s income production, efficiency and costs were taken into consideration when choosing the internal components. The breakout of the financial crisis, bank regulations that affect how the banking sector operates and the federal funds rate are the primary external variables.
Design/methodology/approach
This study uses the random effects parametric survival model to investigate the causes of small bank failures in the USA from 1996 to 2019. The study identifies several characteristics that failed banks frequently display. The main indications that may help to identify the elevated risk of small bank failures include the ROA, the cost of funds, the ratio of noninterest income to assets, the ratio of loan and lease losses to assets, noninterest expenses and core capital (leverage) ratio to assets. Economic disruptions, financial market distress and industry-based regulatory redress by the government exacerbate the financial distress borne by small banks.
Findings
The study revealed that a failed bank typically demonstrates a certain number of characteristics. The key factors that might assist identify which bank would be most likely to collapse include the cost of funding earning assets, the yield on earning assets, core Capital (leverage) ratio to assets, loan and lease loss provision to assets, noninterest expense and noninterest income to assets. Additionally, when a financial crisis occurs or the government changes regulations that could raise the cost of compliance for small banks, the likelihood that a bank will fail increases.
Originality/value
Models based on survival theories are more suitable when the authors examine bank failure as a unique event that happens gradually. The authors use a random effects parametric survival model to investigate the internal and external factors that may influence prospective small bank failure. This model has been developed and used in the medicinal research field. The authors do not choose the Cox proportional hazards model because it does not work well with panel data.
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Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a…
Abstract
Purpose
Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.
Design/methodology/approach
Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.
Findings
The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.
Practical implications
One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.
Originality/value
This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.
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Mariel Alem Fonseca, Naoum Tsolakis and Pichawadee Kittipanya-Ngam
Amidst compounding crises and increasing global population’s nutritional needs, food supply chains are called to address the “diet–environment–health” trilemma in a sustainable…
Abstract
Purpose
Amidst compounding crises and increasing global population’s nutritional needs, food supply chains are called to address the “diet–environment–health” trilemma in a sustainable and resilient manner. However, food system stakeholders are reluctant to act upon established protein sources such as meat to avoid potential public and industry-driven repercussions. To this effect, this study aims to understand the meat supply chain (SC) through systems thinking and propose innovative interventions to break this “cycle of inertia”.
Design/methodology/approach
This research uses an interdisciplinary approach to investigate the meat supply network system. Data was gathered through a critical literature synthesis, domain-expert interviews and a focus group engagement to understand the system’s underlying structure and inspire innovative interventions for sustainability.
Findings
The analysis revealed that six main sub-systems dictate the “cycle of inertia” in the meat food SC system, namely: (i) cultural, (ii) social, (iii) institutional, (iv) economic, (v) value chain and (vi) environmental. The Internet of Things and innovative strategies help promote sustainability and resilience across all the sub-systems.
Research limitations/implications
The study findings demystify the structure of the meat food SC system and unveil the root causes of the “cycle of inertia” to suggest pertinent, innovative intervention strategies.
Originality/value
This research contributes to the SC management field by capitalising on interdisciplinary scientific evidence to address a food system challenge with significant socioeconomic and environmental implications.
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Divya Sharma, M. Vimalkumar, Sirish Gouda, Agam Gupta and Vignesh Ilavarasan
Consumers are increasingly choosing social media over other channels and mechanisms for grievance redressal. However, not all social media grievances elicit a response from…
Abstract
Purpose
Consumers are increasingly choosing social media over other channels and mechanisms for grievance redressal. However, not all social media grievances elicit a response from businesses. Hence, in this research the authors aim to explore the effect of the complainant's social characteristics and the complaint's social and content characteristics on the likelihood of receiving a response to a grievance from the business on social media.
Design/methodology/approach
The authors build a conceptual model and then empirically test it to explore the effect of the complainant's characteristics and the complaint's characteristics on the likelihood of response from a business on social media. The authors use data of consumer grievances received by an Indian airline operator on Twitter during two time periods – the first corresponding to lockdown during Covid-19 pandemic, and the second corresponding to the resumption of business as usual following these lockdowns. The authors use logistic regression and the hazard rate model to model the likelihood of response and the response delay, respectively, for social media customer grievances.
Findings
Complainants with high social influence are not more likely to get a response for their grievances on social media. While tagging other individuals and business accounts in a social media complaint has negative effect on the likelihood of business response in both the time periods, the effect of tagging regulatory bodies on the likelihood of response was negative only in the Covid-19 lockdown period. The readability and valence of a complaint were found to positively affect the likelihood of response to a social media grievance. However, the effect of valence was significant only in lockdown period.
Originality/value
This research offers insights on what elicits responses from a service provider to consumers' grievances on social media platforms. The extant literature is a plenty on how firms should be engaging consumers on online media and how online communities should be built, but scanty on grievance redressal on social media. This research is, therefore, likely to be useful to service providers who are inclined to improve their grievance handling mechanisms, as well as, to regulatory authorities and ombudsmen.
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The aim of this paper is to explore the stakeholder exclusion practices of responsible leaders.
Abstract
Purpose
The aim of this paper is to explore the stakeholder exclusion practices of responsible leaders.
Design/methodology/approach
An interpretive multiple case analyses of seven responsibly led organisations was employed. Twenty-two qualitative interviews were undertaken to investigate and understand perceptions and practice of responsible leaders and their approach to stakeholder inclusion and exclusion.
Findings
The findings revealed new and surprising insights where responsible leaders compromised their espoused values of inclusivity through the application of a personal bias, resulting in the exclusion of certain stakeholders. This exclusivity practice focused on the informal evaluation of potential stakeholders’ values, and where they did not align with those of the responsible leader, these stakeholders were excluded from participation with the organisation. This resulted in the creation and continuity of a culture of shared moral purpose across the organisation.
Research limitations/implications
This study focussed on responsible leader-led organisations, so the next stage of the research will include mainstream organisations (i.e. without explicit responsible leadership) to examine how personal values bias affects stakeholder selection in a wider setting.
Practical implications
The findings suggest that reflexive practice and critically appraising management methods in normative leadership approaches may lead to improvements in diversity management.
Originality/value
This paper presents original empirical data challenging current perceptions of responsible leader inclusivity practices and indicates areas of leadership development that may need to be addressed.
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Nishant Agarwal and Amna Chalwati
The authors examine the role of analysts’ prior experience of forecasting for firms exposed to epidemics on analysts’ forecast accuracy during the COVID-19 pandemic.
Abstract
Purpose
The authors examine the role of analysts’ prior experience of forecasting for firms exposed to epidemics on analysts’ forecast accuracy during the COVID-19 pandemic.
Design/methodology/approach
The authors examine the impact of analysts’ prior epidemic experience on forecast accuracy by comparing the changes from the pre-COVID-19 period (calendar year 2019) to the post-COVID period extending up to March 2023 across HRE versus non-HRE analysts. The authors consider a full sample (194,980) and a sub-sample (136,836) approach to distinguish “Recent” forecasts from “All” forecasts (including revisions).
Findings
The study's findings reveal that forecast accuracy for HRE analysts is significantly higher than that for non-HRE analysts during COVID-19. Specifically, forecast errors significantly decrease by 0.6% and 0.15% for the “Recent” and “All” forecast samples, respectively. This finding suggests that analysts’ prior epidemic experience leads to an enhanced ability to assess the uncertainty around the epidemic, thereby translating to higher forecast accuracy.
Research limitations/implications
The finding that the expertise developed through an experience of following high-risk firms in the past enhances analysts’ performance during the pandemic sheds light on a key differentiator that partially explains the systematic difference in performance across analysts. The authors also show that industry experience alone is not useful in improving forecast accuracy during a pandemic – prior experience of tracking firms during epidemics adds incremental accuracy to analysts’ forecasts during pandemics such as COVID-19.
Practical implications
The study findings should prompt macroeconomic policymakers at the national level, such as the central banks of countries, to include past epidemic experiences as a key determinant when forecasting the economic outlook and making policy-related decisions. Moreover, practitioners and advisory firms can improve the earning prediction models by placing more weight on pandemic-adjusted forecasts made by analysts with past epidemic experience.
Originality/value
The uncertainty induced by the COVID-19 pandemic increases uncertainty in global financial markets. Under such circumstances, the importance of analysts’ role as information intermediaries gains even more importance. This raises the question of what determines analysts’ forecast accuracy during the COVID-19 pandemic. Building upon prior literature on the role of analyst experience in shaping analysts’ forecasts, the authors examine whether experience in tracking firms exposed to prior epidemics allows analysts to forecast more accurately during COVID-19. The authors find that analysts who have experience in forecasting for firms with high exposure to epidemics (H1N1, Zika, Ebola, and SARS) exhibit higher accuracy than analysts who lack such experience. Further, this effect of experience on forecast accuracy is more pronounced while forecasting for firms with higher exposure to the risk of COVID-19 and for firms with a poor ex-ante informational environment.
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Tobias Otterbring, Peter Samuelsson, Jasenko Arsenovic, Christian T. Elbæk and Michał Folwarczny
Previous research on salesperson-customer proximity has yielded mixed results, with some studies documenting positive proximity effects on shopping responses and others…
Abstract
Purpose
Previous research on salesperson-customer proximity has yielded mixed results, with some studies documenting positive proximity effects on shopping responses and others demonstrating the reverse. To reconcile such mixed findings, this paper aims to test whether and how salesperson proximity influences a series of key customer outcomes in actual retail settings using sample sizes that are considerably larger than most former investigations.
Design/methodology/approach
We conducted two high-powered field studies (N = 1,312) to test whether salesperson‐customer proximity influences consumers’ purchase behavior and store loyalty. Moreover, we investigated whether the short-term effects on purchase behavior were moderated by the extent to which the consumption context had a clear connection to consumers’ own bodies.
Findings
Salesperson proximity increased purchase incidence and spending in consumption contexts with a bodily basis (e.g. clothes, beauty, health), suggesting that consumers “buy their way out” in these contexts when a salesperson is violating their personal space. If anything, such proximity had a negative impact on consumers’ purchase behavior in contexts that lacked a clear bodily connection (e.g. building materials, furniture, books). Moreover, the link between proximity and consumer responses was mediated by discomfort, such that a salesperson standing close-by (vs farther away) increased discomfort, with negative downstream effects on shopping responses. Importantly, the authors found opposite proximity effects on short-term metrics (purchase incidence and spending) and long-term outcomes (store loyalty).
Research limitations/implications
Drawing on the nonverbal communication literature and theories on processing fluency, the current work introduces a theoretically relevant boundary condition for the effects of salesperson-customer proximity on consumers’ purchase behavior. Specifically, the bodily basis of the consumption context is discussed as a novel moderator, which may help to explain the mixed findings in this stream of research.
Practical implications
Salesperson-customer proximity may serve as a strategic sales tactic to improve short-term revenue in settings that are closely tied to consumers’ own bodies and characterized by one-time purchases. However, as salesperson proximity was found to be associated with lower store loyalty, irrespective of whether the shopping setting had a bodily basis, the risk of violating consumers’ personal space may have costly consequences from a long-term perspective.
Originality/value
The present field studies make three central contributions. First, we introduce a novel moderator for proximity effects in various sales and service settings. Second, we test the focal hypotheses with much higher statistical power than most existing proximity studies. Finally, we document that salesperson-customer proximity ironically yields opposite results on short-term metrics and long-term outcomes, thus underscoring the importance of not solely focusing on sales effectiveness when training frontline employees.
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Corporate disclosures are essential because they provide transparent and accurate information about a company's financial health, performance, risks and governance practices. They…
Abstract
Purpose
Corporate disclosures are essential because they provide transparent and accurate information about a company's financial health, performance, risks and governance practices. They enable investors to make informed decisions, promote market efficiency and maintain trust in the financial system. This paper uses bibliometrics to identify the intellectual composition of the literature on corporate disclosure.
Design/methodology/approach
Based on the bibliometric information of 4,551 articles on corporate disclosure research, the authors conducted citation, keyword co-occurrence, bibliographic coupling and publication analyses to elucidate the leading articles, authors, sources, institutions, countries, themes and topics in the field of corporate disclosure from the 1960s to 2021.
Findings
The findings of this review demonstrate that corporate disclosure research is based on four broad themes – the role of disclosure in capital markets, non-financial disclosure, determinants of corporate disclosure and firm risk and intellectual capital disclosure. This review suggests that management should pay attention to the financial and non-financial corporate information that investors, regulators and the government emphasise.
Originality/value
This paper is the first comprehensive bibliometric review on corporate disclosure. It summarises the regulatory shifts, technological changes and industry trends that have influenced corporate disclosure research. Besides identifying broad research themes, the authors performed bibliographic coupling for research on disclosure sources, including annual reports, management forecasts, earnings calls, press releases, the Internet and social media, to reveal the thematic clusters related to these sources.
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