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The purpose of this study is to estimate the linear regression parameters using two alternative techniques. First technique is to apply the generalized linear model (GLM) and the second technique is the Markov Chain Monte Carlo (MCMC) method.

In this paper, the authors adopted the incurred claims of Egyptian non-life insurance market as a dependent variable during a 10-year period. MCMC uses Gibbs sampling to generate a sample from a posterior distribution of a linear regression to estimate the parameters of interest. However, the authors used the R package to estimate the parameters of the linear regression using the above techniques.

These procedures will guide the decision-maker for estimating the reserve and set proper investment strategy.

In this paper, the authors will estimate the parameters of a linear regression model using MCMC method via R package. Furthermore, MCMC uses Gibbs sampling to generate a sample from a posterior distribution of a linear regression to estimate parameters to predict future claims. In the same line, these procedures will guide the decision-maker for estimating the reserve and set proper investment strategy.

Job involvement can be linked with important work outcomes. One way for organizations to increase job involvement is to use machine learning technology to predict employees’ job involvement, so that their leaders of human resource (HR) management can take proactive measures or plan succession for preservation. This paper aims to develop a reliable job involvement prediction model using machine learning technique.

This study used the data set, which is available at International Business Machines (IBM) Watson Analytics in IBM community and applied a generalized linear model (GLM) including linear regression and binomial classification. This study essentially had two primary approaches. First, this paper intends to understand the role of variables in job involvement prediction modeling better. Second, the study seeks to evaluate the predictive performance of GLM including linear regression and binomial classification.

In these results, first, employees’ job involvement with a lot of individual factors can be predicted. Second, for each model, this model showed the outstanding predictive performance.

The pre-access and modeling methodology used in this paper can be viewed as a roadmap for the reader to follow the steps taken in this study and to apply procedures to identify the causes of many other HR management problems.

This paper is the first one to attempt to come up with the best-performing model for predicting job involvement based on a limited set of features including employees’ demographics using machine learning technique.

The purpose of this paper is to propose a framework of decision making to aid practitioners in modeling and optimization experimental data for improvement quality of industrial processes, reinforcing idea that planning and conducting data modeling are as important as formal analysis.

The paper presents an application was carried out about the modeling of experimental data at mining company, with support at Catholic University from partnership projects. The literature seems to be more focussed on the data analysis than on providing a sequence of operational steps or decision support which would lead to the best regression model given for the problem that researcher is confronted with. The authors use the concept of statistical regression technique called generalized linear models.

The authors analyze the relevant case study in mining company, based on best statistical regression models. Starting from this analysis, the results of the industrial case study illustrates the strong relationship of the improvement process with the presented framework approach into practice. Moreover, the case study consolidating a fundamental advantage of regression models: modeling guided provides more knowledge about products, processes and technologies, even in unsuccessful case studies.

The study advances in regression model for data modeling are applicable in several types of industrial processes and phenomena random. It is possible to find unsuccessful data modeling due to lack of knowledge of statistical technique.

An essential point is that the study is based on the feedback from practitioners and industrial managers, which makes the analyses and conclusions from practical points of view, without relevant theoretical knowledge of relationship among the process variables. Regression model has its own characteristics related to response variable and factors, and misspecification of the regression model or their components can yield inappropriate inferences and erroneous experimental results.

The purpose of this paper is to study the influence of different quantitative (traditionally used) and qualitative variables, such as the possible negative effect in determined periods of certain socio-political factors on share price formation.

We first analyse descriptively the evolution of the Ibex-35 in recent years and compare it with other international benchmark indices. Bellow, two techniques have been compared: a classic linear regression statistical model (GLM) and a method based on machine learning techniques called Extreme Gradient Boosting (XGBoost).

XGBoost yields a very accurate market value prediction model that clearly outperforms the other, with a coefficient of determination close to 90%, calculated on validation sets.

According to our analysis, individual accounts are equally or more important than consolidated information in predicting the behaviour of share prices. This would justify Spain maintaining the obligation to present individual interim financial statements, which does not happen in other European Union countries because IAS 34 only stipulates consolidated interim financial statements.

The descriptive analysis allows us to see how the Ibex-35 has moved away from international trends, especially in periods in which some relevant socio-political events occurred, such as the independence referendum in Catalonia, the double elections of 2019 or the early handling of the Covid-19 pandemic in 2020.

Compared to other variables, the XGBoost model assigns little importance to socio-political factors when it comes to share price formation; however, this model explains 89.33% of its variance.

El propósito de este artículo es estudiar la influencia de diferentes variables cuantitativas (tradicionalmente usadas) y cualitativas, como la posible influencia negativa en determinados períodos de ciertos factores sociopolíticos, sobre la formación del precio de.

Primero analizamos de forma descriptiva la evolución del Ibex-35 en los últimos años y la comparamos con la de otros índices internacionales de referencia. A continuación, se han contrastado dos técnicas: un modelo estadístico clásico de regresión lineal (GLM) y un método basado en el aprendizaje automático denominado Extreme Gradient Boosting (XGBoost).

XGBoost nos permite obtener un modelo de predicción del valor de mercado muy preciso y claramente superior al otro, con un coeficiente de determinación cercano al 90%, calculado sobre las muestras de validación.

De acuerdo con nuestro análisis, la información contable individual es igual o más importante que la consolidada para predecir el comportamiento del precio de las acciones. Esto justificaría que España mantenga la obligación de presentar estados financieros intermedios individuales, lo que no ocurre en otros países de la Unión Europea porque la NIC 34 solo obliga a realizar estados financieros intermedios consolidados.

El análisis descriptivo permite ver cómo el Ibex-35 se ha alejado de las tendencias internacionales, especialmente en periodos en los que se produjo algún hecho sociopolítico relevante, como el referéndum de autodeterminación de Cataluña, el doble proceso electoral de 2019 o la gestión inicial de la pandemia generada por el Covid-19.

En comparación con otras variables, el modelo XGBoost asigna poca importancia a los factores sociopolíticos cuando se trata de la formación del precio de las acciones; sin embargo, este modelo explica el 89.33% de su varianza.

This paper aims to explore the in-sample explanatory and out-of-sample forecasting accuracy of the generalized additive model for location, scale and shape (GAMLSS) model in contrast to the GAM method in Munich’s residential market.

The paper explores the in-sample explanatory results via comparison of coefficients and a graphical analysis of non-linear effects. The out-of-sample forecasting accuracy focusses on 50 loops of three models excluding 10 per cent of the observations randomly. Afterwards, it obtains the predicted functional forms and predicts the remaining 10 per cent. The forecasting performance is measured via error variance, root mean squared error, mean absolute error and the mean percentage error.

The results show that the complexity of asking rents in Munich is more accurately captured by the GAMLSS approach than the GAM as shown by an outperformance in the in-sample explanatory accuracy. The results further show that the theoretical and empirical complexities do pay off in view of the increased out-of-sample forecasting power of the GAMLSS approach.

The computational requirements necessary to estimate GAMLSS models in terms of number of cores and RAM are high and might constitute one of the limiting factors for (institutional) researchers. Moreover, large and detailed knowledge on statistical inference and programming is necessary.

The usage of the GAMLSS approach would lead policymakers to better understand the local factors affecting rents. Institutional researchers, instead, would clearly aim at calibrating the forecasting accuracy of the model to better forecast rents in investment strategies. Finally, future researchers are encouraged to exploit the large potential of the GAMLSS framework and its modelling flexibility.

The GAMLSS approach is widely recognised and used by international institutions such as the World Health Organisation, the International Monetary Fund and the European Commission. This is the first study to the best of the author’s knowledge to assess the properties of the GAMLSS approach in applied real estate research from a statistical asymptotic perspective by using a unique data basis with more than 38,000 observations.

This study aims to capture how the association between a multichannel relational communication strategy (MRCS) and customer performance is contingent upon such customer performance (low- versus high-performance customers) and to reconcile past contradictory results in this marketing-related topic. To this end, the authors propose and validate the method of quantile regression as an unconventional, yet effective, means to proceed to that reconciliation.

This study collected data from 4,934 customers of a private pension fund firm and accounted for both firm- and customer-initiated relational communication channels (RCCs) and for customer lifetime value (CLV). This study estimated a generalized linear model and then a quantile regression model was used to account for customer performance heterogeneity.

This study finds that specific RCCs present different levels of association with performance for low- versus high-performance customers, where outcome customer performance is the dependent variable. For example, the relation between firm-initiated communication (FIC) and performance is stronger for low-CLV customers, whereas the relation between customer-initiated communication (CIC) and performance is increasingly stronger for high-CLV customers but not for low-CLV ones. This study also finds that combining different forms of FIC can result in a negative association with customer performance, especially for low-CLV customers.

The authors tested the conceptual model in one single firm in the specific context of financial services and with cross-sectional data, so there should be caution when extrapolating this study’s findings.

This study offers nuanced and precise managerial insights on recommended resource allocation along with relational communication efforts, showing how managers can benefit from adopting a differentiated-customer performance approach when designing their MRCS.

This study provides an overview of the state of the art of MRCS, proposes a contingency analysis of the relationship between MRCS and performance based on customer performance heterogeneity and suggests the quantile method to perform such analysis and help reconcile past contradictory findings. This study shows how the association between RCCs and CLV varies across the conditional quantiles of the distribution of customer performance. This study also addresses a recent call for a more holistic perspective on the relationships between independent and dependent variables.

The purpose of this paper is to examine the impact of agency conflicts between managers and shareholders on corporate risk management and financial performance of Saudi firms listed in the Saudi Stock Exchange Tadawul.

To investigate the effect of agency conflicts between managers and shareholders on corporate risk management and financial performance, we use a sample of 180 Saudi firms listed in the Saudi Stock Exchange Tadawul during the period from 2009 to 2018. Econometrically, we employ Vector Autoregressive (VAR) and General Linear Model (GLM) techniques as an appropriate methodology.

Our findings show that the risk level of the last year increase the corporate risk management and the performance of Saudi firm. We remark that the separation amongst control and ownership generates agency conflicts amongst managers and shareholders which can affect their behavior in decision-making and performance of the Saudi firms. Thus, the conflicts of interest arise from the differences among the work horizon, the risk assumed, the performance of enterprises, and the level of remuneration desired by the managers and shareholders in the case of Saudi firms.

The main contributions of our paper prove that the deepen the study of agency costs linked to a shareholding structure through the analysis of monitoring, obligation, and opportunity costs in the Saudi firms.

The purpose of this paper is to examine whether operating countries influence restaurant franchising system performance and what would be an optimal international franchise proportion.

The authors observed ten publicly traded franchise firms that operated between 1995 and 2015. Data analysis is conducted through a generalized linear model (GLM) of panel data.

The model confirms a curvilinear U-shaped relationship between international franchise expansion and firm performance, similar to domestic franchising. The authors found that international franchisors have a higher optimal franchise proportion than domestic franchisors. The authors did not find that operating countries influence firm performance.

This study contributes to franchising literature by expanding limited empirical studies on international franchising. It provides practitioners with a new optimal franchise proportion at the international level.

This study aims to investigate the factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to seven days.

In this study, first, the important factors to influence the demand in EDs were extracted from literature then the relevant factors to the study are selected. Then, a deep neural network is applied to constructing a reliable predictor.

Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable by using the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression, autoregressive integrated moving average, support vector regression, generalized linear models, generalized estimating equations, seasonal ARIMA and combined ARIMA and linear regression.

The authors applied this study in a single ED to forecast patient visits. Applying the same method in different EDs may give a better understanding of the performance of the model to the authors. The same approach can be applied in any other demand forecasting after some minor modifications.

To the best of the knowledge, this is the first study to propose the use of long short-term memory for constructing a predictor of the number of patient visits in EDs.

This study aims to empirically investigate the effect of managerial ownership on bank value concerning conventional and Islamic bank. The analysis uses a balanced panel data set based on a sample consisting of 480 bank-year observations between 2003 and 2017.

Ordinary least squares, fixed effect and random effect have been used primarily to examine the relationship between managerial ownership and banks' value. Later, the authors validate the core results by using the generalized linear model.

This study provides general support for the claim of interest alignment that encourages bank standards with a high level of managerial ownership and partly opposes the view of the entrenchment effects.In addition, the study finds a U-shaped and insignificant relation between managerial ownership and bank value. This indicates that initially, managerial ownership is a blessing, and later, it becomes a curse in considering bank value. Moreover, bank value affects managerial ownership positively both for conventional and Islamic banks.

A good number of studies are available in the current literature, which examine the impact of managerial ownership on either bank performance or risk-taking. However, very few studies are found that examine the bidirectional relationship between managerial ownership and banks' value. Moreover, to the best of authors’ knowledge, there is a dearth of literature on this topic that is built on the comparative analysis between conventional and Islamic banks.