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Factor analysis is the most used tool in organizational research and its widespread use in scale validations contribute to decision-making in management. However, standard factor analysis is not always applied correctly mainly due to the misuse of ordinal data as interval data and the inadequacy of the former for classical factor analysis. The purpose of this paper is to present and apply the Bayesian factor analysis for mixed data (BFAMD) in the context of empirical using the Bayesian paradigm for the construction of scales.

Ignoring the categorical nature of some variables often used in management studies, as the popular Likert scale, may result in a model with false accuracy and possibly biased estimates. To address this issue, Quinn (2004) proposed a Bayesian factor analysis model for mixed data, which is capable of modeling ordinal (qualitative measure) and continuous data (quantitative measure) jointly and allows the inclusion of qualitative information through prior distributions for the parameters’ model. This model, adopted here, presents considering advantages and allows the estimation of the posterior distribution for the latent variables estimated, making the process of inference easier.

The results show that BFAMD is an effective approach for scale validation in management studies making both exploratory and confirmatory analyses possible for the estimated factors and also allowing the analysts to insert a priori information regardless of the sample size, either by using the credible intervals for Factor Loadings or by conducting specific hypotheses tests. The flexibility of the Bayesian approach presented is counterbalanced by the fact that the main estimates used in factor analysis as uniqueness and communalities commonly lose their usual interpretation due to the choice of using prior distributions.

Considering that the development of scales through factor analysis aims to contribute to appropriate decision-making in management and the increasing misuse of ordinal scales as interval in organizational studies, this proposal seems to be effective for mixed data analyses. The findings found here are not intended to be conclusive or limiting but offer a useful starting point from which further theoretical and empirical research of Bayesian factor analysis can be built.

The impact of volatility crush can be devastating to an option buyer and results in a substantial capital loss, even with a directionally correct strategy. As a result, most volatility plays are for option sellers, but the profit they can achieve is limited and the sellers carry unlimited risk. This paper aims to demonstrate the dynamics of implied volatility (IV) as being influenced by effects of persistence, leverage, market sentiment and liquidity. From the exploratory factor analysis (EFA), they extract four constructs and the results from the confirmatory factor analysis (CFA) indicated a good model fit for the constructs.

This section describes the methodology used for conducting the study. This includes the study area, study approach, sources of data, sampling technique and the method of data analysis.

Although there is extensive literature on methods for estimating IV dynamics during earnings announcement, few researchers have looked at the impact of expected market maker move, IV differential and IV Rank on the IV path after the earnings announcement. One reason for this research gap is because of the recent introduction of weekly options for equities by the Chicago Board of Options Exchange (CBOE) back in late 2010. Even then, the CBOE only released weekly options four individual equities – Bank of America (BAC.N), Apple (AAPL.O), Citigroup (C.N) and US-listed shares of BP (BP.L) (BP.N). The introduction of weekly options provided more trading flexibility and precision timing from shorter durations. This automatically expanded expiration choices, which in turned offered greater access and flexibility from the perspective of trading volatility during earnings announcement. This study has demonstrated the impact of including market sentiment and liquidity into the forecasting model for IV during earnings. This understanding in turn helps traders to formulate strategies that can circumvent the undefined risk associated with trading options strategies such as writing strangles.

The first limitation of the study is that the firms included in the study are relatively large, and the results of the study can therefore not be generalized to medium sized and small firms. The second limitation lies in the current sample size, which in many cases was not enough to be able to draw reliable conclusions on. Scaling the sample size up is only a function of time and effort. This is easily overcome and should not be a limitation in the future. The third limitation concerns the measurement of the variables. Under the assumption of a normal distribution of returns (i.e. stock prices follow a random walk process), which means that the distribution of returns is symmetrical, one can estimate the probabilities of potential gains or losses associated with each amount. This means the standard deviation of securities returns, which is called historical volatility and is usually calculated as a moving average, can be used as a risk indicator. The prices used for the calculations are usually the closing prices, but Parkinson (1980) suggests that the day’s high and low prices would provide a better estimate of real volatility. One can also refine the analysis with high-frequency data. Such data enable the avoidance of the bias stemming from the use of closing (or opening) prices, but they have only been available for a relatively short time. The length of the observation period is another topic that is still under debate. There are no criteria that enable one to conclude that volatility calculated in relation to mean returns over 20 trading days (or one month) and then annualized is any more or less representative than volatility calculated over 130 trading days (or six months) and then annualized, or even than volatility measured directly over 260 trading days (one year). Nonetheless, the guidelines adopted in this study represent the best practices of researchers thus far.

This study has indicated that an earnings announcement can provide a volatility mispricing opportunity to allow an investor to profit from a sudden, sharp drop in IV. More specifically, the methodology developed by Tan and Bing is now well supported both empirically and theoretically in terms of qualifying opportunities that can be profitable because of the volatility crush. Conventionally, the option strategy of shorting strangles carries unlimited theoretical risk; however, the methodology has demonstrated that this risk can be substantially reduced if followed judiciously. This profitable strategy relies on a set of qualifying parameters including liquidity, premium collection, volatility differential, expected market move and market sentiment. Building upon this framework, the understanding of the effects of persistence and leverage resulted in further reducing the risk associated with trading options during earnings announcements. As a guideline, the sentiment and liquidity variables help to qualify a trade and the effects of persistence and leverage help to close the qualified trade.

The authors find a positive association between the effects of market sentiment, liquidity, persistence and leverage in the dynamics of IV during earnings announcement. These findings substantiate further the four factors that influence IV dynamics during earnings announcement and conclude that just looking at persistence and leverage alone will not generate profitable trading opportunities.

The impact of volatility crush can be devastating to the option buyer with substantial capital loss, even for a directionally correct strategy. As a result, most volatility plays are for option sellers; however, the profit is limited and the sellers carry unlimited risk. The authors demonstrate the dynamics of IV as being influenced by effects of persistence, leverage, market sentiment and liquidity. From the EFA, they extracted four constructs and the results from the CFA indicated a good model fit for the constructs. Using EFA, CFA and Bayesian analysis, how this model can help investors formulate the right strategy to achieve the best risk/reward mix is demonstrated. Using Bayesian estimation and IV differential to proxy for differences of opinion about term structures in option pricing, the authors find a positive association among the effects of market sentiment, liquidity, persistence and leverage in the dynamics of IV during earnings announcement.

Loan default risk or credit risk evaluation is important to financial institutions which provide loans to businesses and individuals. Loans carry the risk of being defaulted. To understand the risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amounts of information on borrowers. Statistical predictive analytic techniques can be used to analyse or to determine the risk levels involved in loans. This paper aims to address the question of default prediction of short-term loans for a Tunisian commercial bank.

The authors have used a database of 924 files of credits granted to industrial Tunisian companies by a commercial bank in the years 2003, 2004, 2005 and 2006. The naive Bayesian classifier algorithm was used, and the results show that the good classification rate is of the order of 63.85 per cent. The default probability is explained by the variables measuring working capital, leverage, solvency, profitability and cash flow indicators.

The results of the validation test show that the good classification rate is of the order of 58.66 per cent; nevertheless, the error types I and II remain relatively high at 42.42 and 40.47 per cent, respectively. A receiver operating characteristic curve is plotted to evaluate the performance of the model. The result shows that the area under the curve criterion is of the order of 69 per cent.

The paper highlights the fact that the Tunisian central bank obliged all commercial banks to conduct a survey study to collect qualitative data for better credit notation of the borrowers.

El riesgo de incumplimiento de préstamos o la evaluación del riesgo de crédito es importante para las instituciones financieras que otorgan préstamos a empresas e individuos. Existe el riesgo de que el pago de préstamos no se cumpla. Para entender los niveles de riesgo de los usuarios de crédito (corporaciones e individuos), los proveedores de crédito (banqueros) normalmente recogen gran cantidad de información sobre los prestatarios. Las técnicas analíticas predictivas estadísticas pueden utilizarse para analizar o determinar los niveles de riesgo involucrados en los préstamos. En este artículo abordamos la cuestión de la predicción por defecto de los préstamos a corto plazo para un banco comercial tunecino.

Utilizamos una base de datos de 924 archivos de créditos concedidos a empresas industriales tunecinas por un banco comercial en 2003, 2004, 2005 y 2006. El algoritmo bayesiano de clasificadores se llevó a cabo y los resultados muestran que la tasa de clasificación buena es del orden del 63.85%. La probabilidad de incumplimiento se explica por las variables que miden el capital de trabajo, el apalancamiento, la solvencia, la rentabilidad y los indicadores de flujo de efectivo.

Los resultados de la prueba de validación muestran que la buena tasa de clasificación es del orden de 58.66% ; sin embargo, los errores tipo I y II permanecen relativamente altos, siendo de 42.42% y 40.47%, respectivamente. Se traza una curva ROC para evaluar el rendimiento del modelo. El resultado muestra que el criterio de área bajo curva (AUC, por sus siglas en inglés) es del orden del 69%.

El documento destaca el hecho de que el Banco Central tunecino obligó a todas las entidades del sector llevar a cabo un estudio de encuesta para recopilar datos cualitativos para un mejor registro de crédito de los prestatarios.

Curva ROC, Evaluación de riesgos, Riesgo de incumplimiento, Sector bancario, Algoritmo clasificador bayesiano.

Artículo de investigación

This study aims to explore the relationship between chief executive officer (CEO) power and stock price crash risk in India. Furthermore, it seeks to analyse how insider trades may moderate the impact of CEO power on stock price crash risk.

A study of 236 companies from the S&P BSE 500 Index (2014–2023) have been analysed through pooled ordinary least square (OLS) regression in the baseline analysis. To enhance the results' reliability, robustness checks include alternative methodologies, such as panel data regression with fixed-effects, binary logistic regression and Bayesian regression. Additional control variables and alternative crash risk measure have also been utilised. To address potential endogeneity, instrumental variable techniques such as two-stage least squares (IV-2SLS) and difference-in-difference (DiD) methodologies are utilised.

Stakeholder theory is supported by results revealing that CEO power proxies like CEO duality, status and directorship reduce one-year ahead stock price crash risk and vice versa. Insider trades are found to moderate the link between select dimensions of CEO power and stock price crash risk. These findings persist after addressing potential endogeneity concerns, and the results remain consistent across alternative methodologies and variable inclusions.

This study significantly advances research on stock price crash risk, especially in emerging economies like India. The implications of these findings are crucial for investors aiming to mitigate crash risk, for corporations seeking enhanced governance measures and for policymakers considering the economic and welfare consequences associated with this phenomenon.

Global value changes continued to expand until the late 2000s. On the other hand, regional value chains have formed around major regional hubs due to the expansion of domestic demand in emerging economies, such as China, and strengthened trade protectionism since the global financial crisis. Such changes lead to the reorganisation of value chains, focusing on domestic markets (reshoring) or neighbouring countries (nearshoring). In particular, the importance of supply chain risk management has been highlighted following disruptions to the supply network due to the COVID-19 outbreak in December 2019. In this regard, major countries such as the USA and the EU are rapidly shifting to regional value chains for stable and sustainable production, rather than primarily aiming for production efficiency targeted at reducing costs. Industries in particular are more exposed to such supply chain risks under the existing structure and it now has become extremely important for businesses to take reaction to such risks. This is especially important for major industries in a country such as automobile or semiconductor manufacturing industries in South Korea. The aim of this study, therefore, is to establish the basis for the simultaneous growth of ports and linked industries by examining the existing structure of the global value chain for the automotive industry, which has a strong presence in South Korea’s domestic economy. In this regard, this research carries out a supply chain analysis focusing on the imports and exports of automotive parts. It also analyses the current structural risks and suggests risk management measures to secure a stable supply chain.

Basic safety message (BSM) is a core subset of standard protocols for connected vehicle system to transmit related safety information via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Although some safety prototypes of connected vehicle have been proposed with effective strategies, few of them are fully evaluated in terms of the significance of BSM messages on performance of safety applications when in emergency.

To address this problem, a data fusion method is proposed to capture the vehicle crash risk by extracting critical information from raw BSMs data, such as driver volition, vehicle speed, hard accelerations and braking. Thereafter, a classification model based on information-entropy and variable precision rough set (VPRS) is used for assessing the instantaneous driving safety by fusing the BSMs data from field test, and predicting the vehicle crash risk level with the driver emergency maneuvers in the next short term.

The findings and implications are discussed for developing an improved warning and driving assistant system by using BSMs messages.

The findings of this study are relevant to incorporation of alerts, warnings and control assists in V2V applications of connected vehicles. Such applications can help drivers identify situations where surrounding drivers are volatile, and they may avoid dangers by taking defensive actions.

This paper proposes a heuristic, data-driven approach to the rapid performance evaluation of periodic maintenance on complex production plants. Through grouping, maintenance interval (MI)-based evaluation and performance assessment, potential nonvalue-adding maintenance elements can be identified in the current maintenance structure. The framework reduces management complexity and supports the decision-making process for further maintenance improvement.

The evaluation framework follows a prescriptive research approach. The framework is structured in three steps, which are further illustrated in the case study. The case study utilizes real-life data to verify the feasibility and effectiveness of the proposed framework.

Through a case study conducted on 9,538 pieces of equipment from eight offshore oil and gas production platforms, the results show considerable potential for maintenance performance improvement, including up to a 23% reduction in periodic maintenance hours.

The problem of performance evaluation under limited data availability has barely been addressed in the literature on the plant level. The proposed framework aims to provide a quantitative approach to reducing the structural complexity of the periodic maintenance evaluation process and can help maintenance professionals prioritize the focus on maintenance improvement among current strategies.

The proposed framework is especially suitable for initial performance assessment in systems with a complex structure, limited maintenance records and imperfect data, as it reduces management complexity and supports the decision-making process for further maintenance improvement. A similar application has not been identified in the literature.