Regression Tool Research Articles

A Study on Relationship Between Leverage and Profitability – A Case of Food Industries in India

In financial management, Leverage is the key decision area. Leverage constitutes the Degree of Operating Leverage, Degree of Financial Leverage and Degree of Combined Leverage. In this paper, the relationship between Leverage and Profitability has been discussed. For the purpose of this study, data of financial year (FY) 2020 has been considered. About 10 companies in the food industry which are listed in the Bombay Stock Exchange have been chosen for this study. An exploratory form of research design is used which is based on the secondary data. The data was collected from the annual reports of the chosen companies. The data collected has been analyzed by using the regression tool. The main aim of this study is to analyse the performance of the 10 companies, chosen.

Simultaneous extraction of intra- and inter-cycle features for predicting lithium-ion battery's knees using convolutional and recurrent neural networks

The timely and accurate prediction of degradation patterns of lithium-ion batteries (LIBs) is crucial for investigating their lifespan. Although charging-discharging cycling data are common for this purpose, there has been limited exploration into simultaneously extracting features related to both intra- and inter-cycle behaviors, despite their significant relevance. In this study, machine learning methods are proposed to enable the simultaneous extraction of intra- and inter-cycle features from voltage-current-temperature (VIT) cycling datasets for predicting the knees of LIBs. To standardize the length of each cycling data, the time-series data are realigned based on voltage or employing zero-padding, depending on the dataset type. The cycling dataset is then organized as an array along the time and cycle axes before applying convolutional neural networks (CNNs) and/or recurrent neural networks (RNNs) for feature extraction. Three approaches to utilizing these nonlinear regression tools are explored: 2-dimensional CNN (2D CNN), RNN + 1-dimensional CNN (1D CNN), and RNN + 2D CNN. The resulting knees prediction models are evaluated using a benchmark dataset. Our results underscore the advantage of explicitly considering cycle-to-cycle behavior in conjunction with intra-cycle temporal behavior when constructing a data-driven prediction model. We also investigate reducing the input data requirement to facilitate early knee prediction. The findings demonstrate that the input size can be effectively reduced from 100 to the first 60 cycles without compromising prediction performance.

Detecting Shape-Based Interactions Among Environmental Chemicals Using an Ensemble of Exposure-Mixture Regression and Interpretable Machine Learning Tools

There is growing interest in discovering interactions between multiple environmental chemicals associated with increased adverse health effects. However, most existing approaches (1) either use a projection or product of multiple chemical exposures, which are difficult to interpret and (2) cannot simultaneously handle multi-ordered interactions. Therefore, we develop and validate a method to discover shape-based interactions that mimic usual toxicological interactions. We developed the Multi-ordered explanatory interaction (Moxie) algorithm by merging the efficacy of Extreme Gradient Boosting with the inferential power of Weighted Quantile Sum regression to extract synergistic interactions associated with the outcome/odds of disease in an adverse direction. We evaluated the algorithm’s performance through simulations and compared it with the currently available gold standard, the signed-iterative random forest algorithm. We used the 2017–18 US-NHANES dataset (n = 447 adults) to evaluate interactions among nine per- and poly-fluoroalkyl substances and five metals measured in whole blood in association with serum low-density lipoprotein cholesterol. In simulations, the Moxie algorithm was highly specific and sensitive and had very low false discovery rates in detecting true synergistic interactions of 2nd, 3rd, and 4th order through moderate (n = 250) to large (n = 1000) sample sizes. In NHANES data, we found a two-order synergistic interaction between cadmium and lead detected in people with whole-blood cadmium concentrations and lead above 0.605 ug/dL and 1.485 ug/dL, respectively. Our findings demonstrate a novel validated approach in environmental epidemiology for detecting shape-based toxicologically mimicking interactions by integrating exposure-mixture regression and machine learning methods.

Machine Learning for Film Thickness Prediction in Elastohydrodynamic Lubricated Elliptical Contacts

This study extends the use of Machine Learning (ML) approaches for lubricant film thickness predictions to the general case of elliptical elastohydrodynamic (EHD) contacts, by considering wide and narrow contacts over a wide range of ellipticity and operating conditions. Finite element (FEM) simulations are used to generate substantial training and testing datasets that are used within the proposed ML framework. The complete dataset entails 915 samples; split into an 823-sample training dataset and a 92-sample testing dataset, corresponding to 90% and 10% of the combined dataset samples, respectively. The proposed ML model consists of a pre-processing stage in which conventional EHD dimensionless groups are used to minimize the number of inputs into the model, reducing them to only three. The core of the model is based on Gaussian Process Regression (GPR), a powerful ML regression tool, well-suited for small-sized datasets, producing output central and minimum film thicknesses, also in dimensionless form. The last stage is a post-processing one, in which the output film thicknesses are retrieved in dimensional from. The results reveal the capabilities and potential of the proposed ML framework, producing quasi-instantaneous predictions that are far more accurate than conventional film thickness analytical formulae. In fact, the produced central and minimum film thickness predictions are on average within 0.3% and 1.0% of the FEM results, respectively.

Prediction of equivalent sand-grain size and identification of drag-relevant scales of roughness – a data-driven approach

Despite decades of research, a universal method for prediction of roughness-induced skin friction in a turbulent flow over an arbitrary rough surface is still elusive. The purpose of the present work is to examine two possibilities; first, predicting equivalent sand-grain roughness size $k_s$ based on the roughness height probability density function and power spectrum (PS) leveraging machine learning as a regression tool; and second, extracting information about relevance of different roughness scales to skin-friction drag by interpreting the output of the trained data-driven model. The model is an ensemble neural network (ENN) consisting of 50 deep neural networks. The data for the training of the model are obtained from direct numerical simulations (DNS) of turbulent flow in plane channels over 85 irregular multi-scale roughness samples at friction Reynolds number $Re_\tau =800$ . The 85 roughness samples are selected from a repository of 4200 samples, covering a wide parameter space, through an active learning (AL) framework. The selection is made in several iterations, based on the informativeness of samples in the repository, quantified by the variance of ENN predictions. This AL framework aims to maximize the generalizability of the predictions with a certain amount of data. This is examined using three different testing data sets with different types of roughness, including 21 surfaces from the literature. The model yields overall mean error 5 %–10 % on different testing data sets. Subsequently, a data interpretation technique, known as layer-wise relevance propagation, is applied to measure the contributions of different roughness wavelengths to the predicted $k_s$ . High-pass filtering is then applied to the roughness PS to exclude the wavenumbers identified as drag-irrelevant. The filtered rough surfaces are investigated using DNS, and it is demonstrated that despite significant impact of filtering on the roughness topographical appearance and statistics, the skin-friction coefficient of the original roughness is preserved successfully.

The reliability of models for converting formaldehyde emissions from wood-based materials to different environmental conditions

The monitoring of the formaldehyde emission potential of wood-based materials by means of test chamber investigations has been established by national and international standards and has made an important contribution to significantly reducing the formaldehyde concentrations indoors. The procedures used are mostly methodologically different, so that the results cannot be directly compared. The first empirical models for converting formaldehyde steady-state concentrations as a function of temperature, air humidity, air change rate and loading were derived in the 1970s. However, the development of lower-emitting materials made it necessary to modify and adapt these models. Formaldehyde emissions from wood-based materials are complex from both a reaction-kinetic and dynamic perspective. There is an exponential dependence of the steady-state concentration on temperature and humidity and an inverse proportionality to the air change rate. The mathematical analysis requires multiple non-linear regression tools. A large data set with reliable results obtained under different conditions is available from previous investigations. With a total of 128 measurement data, there was very good agreement between theory and experiment for a wide range of chamber settings from a statistical point of view. However, the model is not suitable for predicting the emission behavior of individual products. Some uncertainty also exists due to the allowed tolerance limits of the chamber parameters. Since the formaldehyde emission rate depends on the air change rate, a simple linear conversion of steady-state chamber concentrations into room air concentrations for indoor exposure assessments is not possible even at constant temperature and humidity.

Lean Management and Organizational Performance in Selected Manufacturing Firms in Akwa Ibom State

The study centered on Lean management and Organizational performance in selected manufacturing firms in Akwa Ibom State. Two research questions as well as two hypotheses were formulated for the study. The researcher adopted survey research design. The population covers 59 top management level, middle management level and lower management level staff in selected manufacturing firms in Akwa Ibom state. Census Techniques was used to collect data for the entire population. Using Ordinal Logistic Regression Tool Analysis, the two hypotheses were tested at 0.05 level of significance and the results of the analysis showed that Lean Management dimension (Value stream mapping is 3.246, PV = 0.002., and waste elimination is 3.024, PV = 0.003,) has a significance relationship on Organizational Performance in manufacturing firms in Akwa Ibom State. From the study conducted, it was concluded that value stream mapping, waste elimination, are relational dimensions that can influence organizational performance in selected manufacturing firms in Akwa Ibom State. It was recommended that, management of the studied organization should strengthen their policies that will support manufacturing and operations by identifying the value from the customer's perspective, map out the value stream, which is the end-to-end flow of activities required to deliver the product or service analyze the value stream to identify and eliminate waste, such as overproduction, waiting time, unnecessary motion, defects, and excess inventory. It is also recommended that the manufacturing firms should incorporate waste eliminating in its operations process such as value stream mapping, total product maintenance and continues improvement in order to increase overall cost efficiency.

Abstract 16465: Cardiovascular Disease Burden in the Middle East and North Africa Region

Introduction: Cardiovascular disease (CVD) remains the leading cause of death globally, including the Middle East and North Africa (MENA) region. However, limited research has been conducted on the burden of CVD in this region. Hypothesis: Our study aims to investigate the burden of CVD and related risk factors (RFs) in the MENA. Methods: We used data from the Global Burden of Disease (GBD) 2019 to examine CVD prevalence in 21 MENA countries. Prevalence, mortality, and incidence were analyzed using Bayesian regression tools, demographic methods, and mortality-to-incidence ratios. Disability-adjusted life years (DALYs) were calculated, and RFs were evaluated under the GBD's comparative risk assessment framework. Results: Between 1990 and 2019, CVD prevalence in the MENA increased by 140.9%, while age-standardized (AS) prevalence slightly decreased (-1.3%). CVD mortality rose by 78.3%, but AS death rates fell by 28.01%. Ischemic heart disease remained the most prevalent condition, with higher rates in men, while women had higher rates of CVA. All-age DALYs increased by 53.3%, but AS DALYs decreased by 32.54%. DALY rates varied across countries and were consistently higher in males. Leading RFs included HTN, high LDL, dietary risks, and elevated BMI. Conclusions: The ongoing impact of CVD in the MENA region necessitates targeted interventions that address country-specific variations, including socio-economic factors, healthcare infrastructure, and political stability. To effectively reduce the burden of CVD in MENA, it is crucial to focus on targeting risk factors and implementing effective strategies.

Right-wing Liberalism In Contemporary Europe: Predictors Of Voting And Voter Profile

For the most part, electoral and party research today is centered on the behavior of voters in two or four major clusters that can be conventionally placed on a scale from left to right. On the same scale, there are smaller groups whose value of separation is often questioned. This paper focuses on one of these groups: we consider a segment of voters united on the basis of their support for right-wing liberal parties and try to identify the most significant determinants of their choice. Based on the existing concepts of voting and data from the European Social Survey, 2016–2018, we generalize possible predictors – party and political selfidentification, ideological stances, social setting and material status – up to three theories (‘ideological core’, ‘defectors’ and ‘winners’), and test their plausibility using regression tools. Modeling results show that for a number of parameters the separation of new subgroup is justified. First, we reject the hypothesis of a strategic non-voting for right-liberals (on the contrary, respondents often “strategically” vote for them rather than for some personally closer party). Second, we find similar characteristics of the voter profile in different European countries; in particular, people who share the values of non-interference (associated with opposition to income redistribution and cultural restrictions) are more inclined to electorally back the liberal right. On the other hand, for a complete picture it is not enough to indicate ideological or cleavage preferences: the fact of voting is strongly influenced by person’s socio-economic background, confirming the earlier remarks about certain “privileges” in this electoral segment.

Development of a daily predictive model for the exacerbation of chronic obstructive pulmonary disease

Acute exacerbation (AE) of chronic obstructive pulmonary disease (COPD) compromises health status; it increases disease progression and the risk of future exacerbations. We aimed to develop a model to predict COPD exacerbation. We merged the Korean COPD subgroup study (KOCOSS) dataset with nationwide medical claims data, information regarding weather, air pollution, and epidemic respiratory virus data. The Korean National Health and Nutrition Examination Survey (KNHANES) dataset was used for validation. Several machine learning methods were employed to increase the predictive power. The development dataset consisted of 590 COPD patients enrolled in the KOCOSS cohort; these were randomly divided into training and internal validation subsets on the basis of the individual claims data. We selected demographic and spirometry data, medications for COPD and hospital visit for AE, air pollution data and meteorological data, and influenza virus data as contributing factors for the final model. Six machine learning and logistic regression tools were used to evaluate the performance of the model. A light gradient boosted machine (LGBM) afforded the best predictive power with an area under the curve (AUC) of 0.935 and an F1 score of 0.653. Similar favorable predictive performance was observed for the 2151 individuals in the external validation dataset. Daily prediction of the COPD exacerbation risk may help patients to rapidly assess their risk of exacerbation and will guide them to take appropriate intervention in advance. This might lead to reduction of the personal and socioeconomic burdens associated with exacerbation.

Novel Fractional Order and Stochastic Formulations for the Precise Prediction of Commercial Photovoltaic Curves

To effectively represent photovoltaic (PV) modules while considering their dependency on changing environmental conditions, three novel mathematical and empirical formulations are proposed in this study to model PV curves with minimum effort and short timing. The three approaches rely on distinct mathematical techniques and definitions to formulate PV curves using function representations. We develop our models through fractional derivatives and stochastic white noise. The first empirical model is proposed using a fractional regression tool driven by the Liouville-Caputo fractional derivative and then implemented by the Mittag-Leffler function representation. Further, the fractional-order stochastic ordinary differential equation (ODE) tool is employed to generate two effective generic models. In this work, multiple commercial PV modules are modeled using the proposed fractional and stochastic formulations. Using the experimental data of the studied PV panels at different climatic conditions, we evaluate the proposed models’ accuracy using two effective statistical indices: the root mean squares error (RMSE) and the determination coefficient (R2). Finally, the proposed approaches are compared to several integer-order models in the literature where the proposed models’ precisely follow the real PV curves with a higher R2 and lower RMSE values at different irradiance levels lower than 800 w/m2, and module temperature levels higher than 50 °C.

Improved regression model for anaerobic ammonium oxidation by repeated and prolonged batch assay under stressful salinity and pH conditions

The aim of this study was to propose repeated and prolonged batch (RPB) assay as a promising specific anammox activity (SAA) methodology assessing the anammox activity under stressed salinity and pH conditions. Response surface analysis (RSA) was used as a regression tool to evaluate statistical significance. The feasibility of RPB was investigated at 0 to 15 g-NaCl/L of salinity and pH 6 to 8 with reflecting the results of preliminary SAA. As a result, conventional SAA was statistically insignificant. In addition, the RSA results obtained from repeated batch did not meet the statistical significance despite ten times iterative reaction. Interestingly, the RPB assay (i.e., applied both repeated and prolonged reaction) was effective to obtain the reliable results. Candidadus Brocadia and Candidadus Jettenia were functional anammox microbiome during RPB. Outcomes of this study suggest that RPB assay can be applied to accurately determine the anammox activity under various stressful conditions.

Deep Resequencing of the 1q22 Locus in Non-Lobar Intracerebral Hemorrhage.

Genome-wide association studies have identified 1q22 as a susceptibility locus for cerebral small vessel diseases, including non-lobar intracerebral hemorrhage (ICH) and lacunar stroke. In the present study, we performed targeted high-depth sequencing of 1q22 in ICH cases and controls to further characterize this locus and prioritize potential causal mechanisms, which remain unknown. A total of 95,000 base pairs spanning 1q22, including SEMA4A, SLC25A44, and PMF1/PMF1-BGLAP were sequenced in 1,055 spontaneous ICH cases (534 lobar and 521 non-lobar) and 1,078 controls. Firth regression and Rare Variant Influential Filtering Tool analysis were used to analyze common and rare variants, respectively. Chromatin interaction analyses were performed using Hi-C, chromatin immunoprecipitation followed by sequencing, and chromatin interaction analysis with paired-end tag databases. Multivariable Mendelian randomization assessed whether alterations in gene-specific expression relative to regionally co-expressed genes at 1q22 could be causally related to ICH risk. Common and rare variant analyses prioritized variants in SEMA4A 5'-UTR and PMF1 intronic regions, overlapping with active promoter and enhancer regions based on ENCODE annotation. Hi-C data analysis determined that 1q22 is spatially organized in a single chromatin loop, and that the genes therein belong to the same topologically associating domain. Chromatin immunoprecipitation followed by sequencing and chromatin interaction analysis with paired-end tag data analysis highlighted the presence of long-range interactions between the SEMA4A-promoter and PMF1-enhancer regions prioritized by association testing. Multivariable Mendelian randomization analyses demonstrated that PMF1 overexpression could be causally related to non-lobar ICH risk. Altered promoter-enhancer interactions leading to PMF1 overexpression, potentially dysregulating polyamine catabolism, could explain demonstrated associations with non-lobar ICH risk at 1q22, offering a potential new target for prevention of ICH and cerebral small vessel disease. ANN NEUROL 2024;95:325-337.

Pengaruh Lingkungan Kerja Fisik, Disiplin Kerja Dan Stres Kerja Terhadap Kinerja Karyawan Pada Pabrik Gula Merah UD Bunga Mas

This research aims to determine the influence of the physical work environment, work discipline and work stress on employee performance at the UD Bunga Mas brown sugar factory. The variables used in this research are Physical Work Environment (X1), Work Discipline (X2), Work Stress (X3), and Employee Performance (Y). The research location is the UD Bunga Mas Brown Sugar Factory which is located in Karanglo Hamlet, Tales Village RT 01 RW 04 Ngadiluwih District, Kediri Regency. By using data collection techniques in the form of interviews, questionnaires and observations.Meanwhile, the analysis used uses quantitative methods with multiple linear regression tools using the SPSS version 25.0 software program. Based on tests carried out by partial linear regression analysis, it shows that the Physical Work Environment (X1) has a negative and insignificant effect on Employee Performance (Y) and Work Discipline (X2) has a positive and insignificant effect on Employee Performance (Y). Meanwhile, the Job Stress variable (X3) has a negative and significant influence on Employee Performance (Y). This shows a significant value for variable X1 0.116 > 0.05, variable X2 0.538 > 0.05, and X3 0.000 < 0.05. Partially, the Physical Work Environment (X1), Work Discipline (X2), and Work Stress (X3) have a positive and significant effect on Employee Performance (Y) at the UD Bunga Mas Brown Sugar Factory.

Machine learning regression tools for erosion prediction of WC-10Co4Cr thermal spray coating

The prediction of erosion in WC-10Co4Cr thermal spray coating is predicted using regression machine learning technique. A pot tester helped to examine the erosion rate of WC-10Co4Cr thermal spray coatings. WC-10Co4Cr thermal spray powder was sprayed onto the SS316L steel. Different impingement conditions (30, 45, and 60°) were tested by using textures designed to simulate erosion. The collected data is used to construct a robust Gaussian Process Regression (GPR) model. The projected values are compared to the actual values obtained via experimentation. To further demonstrate the accuracy of the suggested model, the produced model is compared to various state-of-the-art machine learning methods. The GPR outperforms more commonplace methods of other regression techniques like decision trees, Ensemble boosted trees, and linear regression models. The erosion of coated and bare SS316L austenitic steel was effectively predicted using a GPR model.

A physics-based machine learning technique rapidly reconstructs the wall-shear stress and pressure fields in coronary arteries.

With the global rise of cardiovascular disease including atherosclerosis, there is a high demand for accurate diagnostic tools that can be used during a short consultation. In view of pathology, abnormal blood flow patterns have been demonstrated to be strong predictors of atherosclerotic lesion incidence, location, progression, and rupture. Prediction of patient-specific blood flow patterns can hence enable fast clinical diagnosis. However, the current state of art for the technique is by employing 3D-imaging-based Computational Fluid Dynamics (CFD). The high computational cost renders these methods impractical. In this work, we present a novel method to expedite the reconstruction of 3D pressure and shear stress fields using a combination of a reduced-order CFD modelling technique together with non-linear regression tools from the Machine Learning (ML) paradigm. Specifically, we develop a proof-of-concept automated pipeline that uses randomised perturbations of an atherosclerotic pig coronary artery to produce a large dataset of unique mesh geometries with variable blood flow. A total of 1,407 geometries were generated from seven reference arteries and were used to simulate blood flow using the CFD solver Abaqus. This CFD dataset was then post-processed using the mesh-domain common-base Proper Orthogonal Decomposition (cPOD) method to obtain Eigen functions and principal coefficients, the latter of which is a product of the individual mesh flow solutions with the POD Eigenvectors. Being a data-reduction method, the POD enables the data to be represented using only the ten most significant modes, which captures cumulatively greater than 95% of variance of flow features due to mesh variations. Next, the node coordinate data of the meshes were embedded in a two-dimensional coordinate system using the t-distributed Stochastic Neighbor Embedding (-SNE) algorithm. The reduced dataset for -SNE coordinates and corresponding vector of POD coefficients were then used to train a Random Forest Regressor (RFR) model. The same methodology was applied to both the volumetric pressure solution and the wall shear stress. The predicted pattern of blood pressure, and shear stress in unseen arterial geometries were compared with the ground truth CFD solutions on "unseen" meshes. The new method was able to reliably reproduce the 3D coronary artery haemodynamics in less than 10 s.

The Influence of Organizational Culture and Organizational Commitment to Employee Work Discipline at the Bandung City Transportation Service

This study aims to determine whether the variable (X) organizational culture and organizational commitment affect the variable (Y) employee discipline. The data collection method was carried out using descriptive and verification methods through questionnaires distributed to employees of the Bandung City Transportation Office. Samples were taken as many as 40 respondents using Purposive Sampling. The data obtained were then processed using SPSS Version 22 and analyzed using multiple linear regression tools. Based on the research results that have been conducted by researchers, the Ttest test shows that organizational culture has an effect on employee work discipline with a low relationship level of 9.50%, organizational commitment has a positive and significant effect on employee work discipline by 84.10% and the R square test together shows that organizational culture and organizational commitment jointly have a positive and significant effect on employee work discipline of 85.70% while the remaining 14.30% are influenced by other parties not examined.

Exponentially quantile regression-ratio-type estimators for robust mean estimation

Traditional ordinary least square (OLS) regression is commonly utilized to develop regression-ratio type estimators with traditional and non traditional measures of location. However, when data are contaminated by outliers, the ordinary least square estimates become inappropriate, and the alternative approach is to use the robust regression method. To solve this issue, the use of robust regression tools for mean estimation is a commonly settled practice. In the present study, we have proposed an efficient family of exponential quantile regression-ratio type estimators by using the auxiliary information for estimating the finite population mean under simple random sampling scheme. Here it is worth noting that quantile regression is robust to outliers. Mathematical expressions such as bias, mean squared error (MSE), and minimum mean squared error are derived up to first order of approximation. To support theoretical findings, two real data collections originating from different sources are used for numerical illustration. The results are showing the superiority of proposed exponential quantile robust regression family of estimators as compared to the existing estimators under simple random sampling scheme.

Tax Revenue Generation and Economic Growth: A Pre and Post Treasury Single Account Implementation in Nigeria

The main objective of this study is to examine the relationship between tax revenues generated and the economic growth of Nigeria in a pre and post treasury single account implementation era. The study specifically assessed the relationship between Value Added Tax and the economic growth of Nigeria in a pre and post TSA implementation periods. It also determined the relationship between Company Income Tax and economic growth in a pre and post TSA era in Nigeria. The study also verified the relationship between Customs and Excise Duty and the economic growth in Nigeria, in a pre and post TSA implementation period in Nigeria. The population for the study was Nigerian economy and the sample size was six years of pre TSA era and Six years of post TSA implementation era. This sample was selected because TSA is just six years old in Nigeria and to compare the pre and post, the researcher had to take the same number of years for meaningful comparison. Three hypotheses were formulated for the study in null form using the Gross Domestic Product(GDP) as the dependent variable on Tax revenues comprising Value Added Tax(VAT), Company Income Tax (CIT) and Customs and Excise Duty (CED) which are the independent variables. Data for the study was collected from statistical records and bulletins from Central bank of Nigeria and Federal inland revenue bulletins as secondary data for the period of the study. The analysis tool for the study was a Regression and correlation analysis tools which aimed at establishing the relationship between the economic growth of Nigeria and the Tax revenue sources used as the independent variables prior to the introduction of TSA and after the introduction of TSA in Nigeria. The result shows that before the implementation of TSA, VAT has a positive and insignificant effect on the GDP while CIT had a positive and insignificant effect on GDP and CED had a negative and insignificant effect on GDP in Nigeria. On the post TSA implementation

THE EFFECT OF INFLATION AND INTEREST RATE ON PROFITABILITY FOR SHARIA BANKING RESULTS

This study was conducted to examine the effect of the inflation variable on the profitability of Islamic banking profit sharing in Indonesia, as well as to test whether there is an asymmetric effect of inflation and interest rate variables on the profitability of Islamic banking profit sharing in Indonesia, the study was conducted using agency theory, portfolio selection and policy. monetary. This study uses a quantitative method, the regression tool used is the Non-linear Autoregressive Lag Model (NARDL) using the Eviews 10 data processing tool, the sample of this study is Islamic banking registered with the Financial Services Authority (OJK), with an observation period from 2014 -2020. The results of this study indicate that inflation and interest rate variables have an asymmetric relationship to the profitability of Islamic banking in Indonesia, namely when inflation and interest rates rise, the effect is smaller than when inflation and interest rates fallKeywords: Profitability, Asymmetric, Inflation, Interest Rate, NARDL