THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

Download Full-text

DOES EARNINGS QUALITY MODERATE THE PREDICTIVE CONTENT OF NONOPERATING INCOME?

Gadjah Mada International Journal of Business ◽

10.22146/gamaijb.5544 ◽

2013 ◽

Vol 6 (2) ◽

pp. 275

Author(s):

Slamet Sugiri

Keyword(s):

Linear Regression ◽

Stock Exchange ◽

Earnings Quality ◽

Manufacturing Firms ◽

Income Smoothing ◽

Index Formula ◽

Quality Level ◽

Model Parameters ◽

Operating Cash Flow ◽

Predictive Content

The objective of this study is to empirically examine a hypothesis that earnings quality enhances the ability of nonoperating income to predict future operating cash flow. The magnitude of income smoothing index, measured by Eckel’s (1981) index formula, is used to capture a firm’s quality level of earnings. Higher index is assumed to represent higher level of earnings quality. A linear regression model is developed to test the hypothesis. The model parameters are estimated based on sixty-two manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997. This study finds empirical evidence that supports the proposed hypothesis. That is, earnings quality enhances the predictive content of nonoperating income.

Download Full-text

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Sensors ◽

10.3390/s22010130 ◽

2021 ◽

Vol 22 (1) ◽

pp. 130

Author(s):

Omar Rodríguez-Abreo ◽

Juvenal Rodríguez-Reséndiz ◽

L. A. Montoya-Santiyanes ◽

José Manuel Álvarez-Alvarado

Keyword(s):

Linear Regression ◽

Vibration Amplitude ◽

Regression Models ◽

Engineering Problem ◽

Coefficient Of Determination ◽

Model Parameters ◽

Gray Wolf ◽

Linear Regression Models ◽

Machinery Condition Monitoring ◽

Non Linear

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

Download Full-text

PENGARUH CSR BERDASAR ISO 26000 TERHADAP NILAI PERUSAHAAN DENGAN KARAKTERSITIK PERUSAHAAN SEBAGAI VARIABEL MODERATING

Journal of Innovation in Business and Economics ◽

10.22219/jibe.vol6.no1.67-74 ◽

2015 ◽

Vol 6 (1) ◽

pp. 67

Author(s):

Sri Wahjuni Latifah

Keyword(s):

Data Analysis ◽

Linear Regression ◽

Regression Models ◽

Stock Exchange ◽

Firm Characteristics ◽

Linear Regression Models ◽

Firm Age ◽

Iso 26000

Research of the influence of ISO 26000 CSR toward the company's value as a moderating variable which is done on companies listed in Indonesia Stock Exchange. The Company’s characteristics are measured by firm age, size, leverage and profitability. The data analysis was done by using double linear regression models, the first is to see the effect based on the ISO 26000 CSR and firm characteristics on value, and the second is to see the effect of interaction with the ISO 26000 corporate characteristics. The results of the study showed that there was no influence of ISO 26000, the characteristics of the company toward the value of the company. However, moderated ISO 26000 by firm characteristics affect the value of the company.

Download Full-text

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

Journal of Business Economics ◽

10.35760/eb.2018.v23i1.1812 ◽

2018 ◽

Vol 23 (1) ◽

pp. 60-71

Author(s):

Wigiyanti Masodah

Keyword(s):

Interest Rate ◽

Linear Regression ◽

Interest Rates ◽

Regression Models ◽

Linear Regression Models ◽

Negative Impacts ◽

Main Activity ◽

The Impact ◽

The Given ◽

Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Download Full-text