Corporate governance: association the spots between institutional enhancement, organisational modification and earnings quality

Purpose The purpose of this paper is to explore the link between earnings quality, Audit Committees and the Board of companies located in Jordan through the lens of enhancing corporate governance. Design/methodology/approach The real earnings management (REM) and accruals earnings management models were notably used within the panel data robust regression analysis approach; these were used against certain Audit Committee characteristics (i.e. meeting frequency, amount of Board and Committee participants [both internal and external], size) and Board of Directors. Findings The former characteristics were found to have a positive relationship with REM, while the latter yielded mixed results: while there was no significant identifiable relationship between Board outsiders and REM, there was a positive relationship identified between Board meetings, Board insiders and Board size and REM. In regard to this study’s limitations, the qualitative data gathered for the Board of Directors through the lens of corporate governance enhancement should have been documented with more detail; furthermore, the study was limited to the study of just one nation. Research limitations/implications The data is limited to only a single country. More explanation for Board of Directors need qualitative understandings into corporate governance improvement. The control variables are essentially partial in a developing market context. Practical implications The different corporate governance code and guidelines improvements have varied influence on earnings quality. As predictable, boards of directors most effect on earnings quality. Improvements have included most modification to audit committees but through them slight measured effect on earnings quality. Social implications Jordan’s corporate governance improvements expected organised corporate governance practices generally in place amongst its boards, and though invoking considerable modification to audit committees, eventually included slight modification to earnings quality. However, both improved earnings quality. Originality/value This particular research appears to be the first to consider both Audit Committee and Board of Directors characteristics in one model; indeed, in this vein, this research is also the first to explore the corporate governance enhancements that initially stemmed from there being zero code or guideline regarding its use, despite it becoming required recently. Hence, the authors can say this study has high originality.

Outlier analysis: Natural resources and immigration policy

This replication underlines the importance of outlier diagnostics since many researchers have long neglected influential observations in OLS regression analysis. In his article, entitled “Primary Resources, Secondary Labor,” Shin finds that advanced democracies with increased natural resource wealth, particularly from oil and natural gas production, are more likely to restrict low-skill immigration policy. By performing outlier diagnostics, this replication shows that Shin’s findings are a statistical artifact. When one outlying country, Norway, is removed from the sample data, I observe almost no significant and negative relationship between oil wealth and immigration policy. When two outlying countries are excluded, the effect of oil wealth completely disappears. Robust regression analysis, a widely used remedial method for outlier problems, confirms the results of my outlier diagnostics.

Comparison of M Estimation, S Estimation, with MM Estimation to Get the Best Estimation of Robust Regression in Criminal Cases in Indonesia

Crime incidents that occurred in Indonesia in 2019 based on Survey Based Data on criminal data sourced from the National Socio-Economic Survey and Village Potential Data Collection produced by the Central Statistics Agency recorded 269,324 cases. The high crime rate is caused by several factors, including poverty and population density. Determination of the most influential factors in criminal acts in Indonesia can be done with Regression Analysis. One method of Regression Analysis that is very commonly used is the Least Square Method. However, Regression Analysis can be used if the assumption test is met. If outliers are found, then the assumption test is not completed. The outlier problem can be overcome by using a robust estimation method. This study aims to determine the best estimation method between Maximum Likelihood Type (M) estimation, Scale (S) estimation, and Method of Moment (MM) estimation on Robust Regression. The best estimate of Robust Regression is the smallest Residual Standard Error (RSE) value and the largest Adjusted R-square. The analysis of case studies of criminal acts in Indonesia in 2019 showed that the best estimate was the S estimate with an RSE value of 4226 and an Adjusted R-square of 0.98

Association between leisure-time physical activity and the built environment in China: Empirical evidence from an accelerometer and GPS-based fitness app

To reexamine the relationship between leisure-time physical activity (LTPA) and the built environment (BE), this paper takes advantage of the massive amount of data collected by an accelerometer and GPS-based fitness mobile app. Massive LTPA data from more than 3 million users were recorded by Codoon in 500m by 500m grid cells and aggregated to 742 natural cities in mainland China. Six BE indicators were quantified using GIS at the city scale. Robust regression analysis was used to estimate the correlation between LTPA and BE. Five of six BE indicators—connectivity, road density, land use mix, points of interest density, and density of parks and squares—were significantly, positively, independently, and linearly related to LTPA in the regression analysis. The study obtains findings that are consistent with the previous literature but also provides novel insights into the important role of POI density in encouraging LTPA, as well as how the relationship between LTPA and BE varies by time of day. The study also sheds light on the embrace of new technology and new data in public health and urban studies.

Risk Factors Analysis of Bone Mineral Density Based on Lasso and Quantile Regression in America during 2015–2018

This study aimed to explore the risk factors of bone mineral density (BMD) in American residents and further analyse the extent of effects, to provide preventive guidance for maintenance of bone health. A cross-sectional study analysis was carried out in this study, of which data validity was identified and ethics approval was exempted based on the National Health and Nutrition Examination Survey (NHANES) database. Candidates’ demographics, physical examination, laboratory indicators and part of questionnaire information were collected and merged from NHANES in 2015–2016 and 2017–2018. The least absolute shrinkage selection operator (lasso) was used to select initial variables with “glmnet” package of R, quantile regression model to analyze influence factors of BMD and their effects in different sites with “qreg” code in Stata. Among 2937 candidates, 17 covariates were selected by lasso regression (λ = 0.00032) in left arm BMD, with 16 covariates in left leg BMD (λ = 0.00052) and 14 covariates in total BMD (λ = 0.00065). Quantile regression results displayed several factors with different coefficients in separate sites and quantiles: gender, age, educational status, race, high-density lipoprotein (HDL), total cholesterol (TC), lead, manganese, ethyl mercury, smoking, alcohol use and body mass index (BMI) (p < 0.05). We constructed robust regression models to conclude that some demographic characteristics, nutritional factors (especially lipid levels, heavy metals) and unhealthy behaviors affected BMD in varying degrees. Gender and race differences, Low-fat food intake and low exposure to heavy metals (mostly lead, manganese and mercury) should be considered by both clinical doctors and people. There is still no consensus on the impact of smoking and alcohol use on bone mineral density in our study.

PEMODELAN REGRESI RIDGE ROBUST S,M, MM-ESTIMATOR DALAM PENANGANAN MULTIKOLINIERITAS DAN PENCILAN (Studi Kasus : Faktor-Faktor yang Mempengaruhi Kemiskinan di Jawa Tengah Tahun 2020)

The Ordinary Least Squares (OLS) is one of the most commonly used method to estimate linier regression parameters. If there is a violation of assumptions such as multicolliniearity especially coupled with the outliers, then the regression with OLS is no longer used. One method can be used to solved the multicollinearity and outliers problem is Ridge Robust Regression. Ridge Robust Regression is a modification of ridge regression method used to solve the multicolliniearity and using some estimators of robust regression used to solve the outlier, the estimator including : Maximum likelihood estimator (M-estimator), Scale estimator (S-estimator), and Method of moment estimator (MM-estimator). The case study can be used with this method is data with multicollinearity and outlier, the case study in this research is poverty in Central Java 2020 influenced by life expentancy, unemployment number, GRDP rate, dependency ratio, human development index, the precentage of population over 15 years of age with the highest education in primary school, mean years school. The result of estimation using OLS show that there is a multicollinearity and presence an outliers. Applied the ridge robust regression to case study prove that ridge robust regression can improve parameter estimation. The best ridge robust regression model is Ridge Robust Regression S-Estimator. The influence value of predictor variabels to poverty is 73,08% and the MSE value is 0,00791.

The Development of CO2 Instantaneous Emission Model of Full Hybrid Vehicle with the Use of Machine Learning Techniques

Road transport contributes to almost a quarter of carbon dioxide emissions in the EU. To analyze the exhaust emissions generated by vehicle flows, it is necessary to use specialized emission models, because it is infeasible to equip all vehicles on the road in the tested road sections with the Portable Emission Measurement System (PEMS). However, the currently used emission models may be inadequate to the investigated vehicle structure or may not be accurate due to the used macroscale. This state of affairs is especially related to full hybrid vehicles, since there are none of the microscale emission models that give estimated emissions values exclusively for this kind of drive system. Several automakers over the past decade have invested in hybrid vehicles with great opportunities to reduce costs through better design, learning, and economies of scale. In this work, the authors propose a methodology for creating a CO2 emission model, which takes relatively little computational time, and the models created give viable results for full hybrid vehicles. The creation of an emission model is based on the review of the accuracy results of methods, such as linear, robust regression, fine, medium, coarse tree, linear, cubic support vector machine (SVM), bagged trees, Gaussian process regression (GPR), and neural network (NNET). Particularly in the work, the best fit for the road input data for the CO2 emission model creation was the GPR method. PEMS data was used, as well as model training data and model validation. The model resulting from this methodology can be used for the analysis of emissions from simulation tests, or they can be used for input parameters for speed, acceleration, and road gradient.

Disaggregated IPO returns, economic uncertainty and the long-run performance of SME IPOs

PurposeSmall and medium enterprises (SMEs) play a crucial role in national economies worldwide, generating employment and contributing to innovation. This study tries to investigate the performance of the newly started IPO platform for the SMEs in India through a two-staged framework developed to measure pre-market and post-market underpricing separately and the impact of economic policy uncertainty (EPU) on the IPO returns using the EPU index which is based on newspaper coverage frequency. Further, the long-run performance of SME IPOs and the factors affecting the same have also been analyzed. The two-staged framework is helpful in capturing the impact of different factors separately on the two distinctive markets and providing effective investment strategies to the investors.Design/methodology/approachA sample of 384 SME IPOs issued during 2012–2018 has been analyzed using robust regression analysis.FindingsThe study highlights the fact that there are differences in the factors affecting pre-market and post-market underpricing and reports that investors subscription rate, issue expenses, lead manager reputation and EPU are positively associated, whereas the age of the firm is negatively associated with the pre-market underpricing, and lead manager reputation positively impacts the post-market underpricing whereas issue premium and pre-market underpricing are negatively associated. Pre-market underpricing subsumes all the impact of EPU (publicly available information) in it, hence providing credence to the semi-strong market hypothesis of the Efficient Market Hypothesis (EMH). The long-run performance of SME IPOs increases with time, and lead manager reputation, pre-market and post-market underpricing are positively related to the one-year return whereas issue size, turnover and issue expense are negatively related.Originality/valueThis paper is believed to be the first attempt to analyze the performance of SME IPOs by disaggregating IPO underpricing. The findings of this study will have a great insight for the investors and policymakers.

Interplay between environmental, social and governance coordinates and the financial performance of agricultural companies

On the complex framing of the agricultural fields, related to the corporate social responsibility (CSR), the general objective of this paper is to assess the impacts of environmental, social and governance (ESG) credentials of CSR and human capital features on the financial performance of agricultural companies. The data consists of a sample of 412 public companies from the Thomson Reuters Eikon database, with data for 2020, operating in 17 agricultural areas with headquarters allocated around the world. The methodological endeavor embeds two econometric procedures, multifactorial models of robust regression and structural equation modelling (SEM). The research results bring new evidence to underline the risks related to the sustainability of the financial performance of agricultural companies and the decisive role played by the ESG dimensions to counteract these risks, particularly by the environmental pillar, along with CSR specific strategies and human capital characteristics (management board and employees). We propose several strategies for companies operating in agricultural fields in order to enhance profitability by CSR measures.

Penalized wavelet estimation and robust denoising for irregular spaced data

Nonparametric univariate regression via wavelets is usually implemented under the assumptions of dyadic sample size, equally spaced fixed sample points, and i.i.d. normal errors. In this work, we propose, study and compare some wavelet based nonparametric estimation methods designed to recover a one-dimensional regression function for data that not necessary possess the above requirements. These methods use appropriate regularizations by penalizing the decomposition of the unknown regression function on a wavelet basis of functions evaluated on the sampling design. Exploiting the sparsity of wavelet decompositions for signals belonging to homogeneous Besov spaces, we use some efficient proximal gradient descent algorithms, available in recent literature, for computing the estimates with fast computation times. Our wavelet based procedures, in both the standard and the robust regression case have favorable theoretical properties, thanks in large part to the separability nature of the (non convex) regularization they are based on. We establish asymptotic global optimal rates of convergence under weak conditions. It is known that such rates are, in general, unattainable by smoothing splines or other linear nonparametric smoothers. Lastly, we present several experiments to examine the empirical performance of our procedures and their comparisons with other proposals available in the literature. An interesting regression analysis of some real data applications using these procedures unambiguously demonstrate their effectiveness.