96 research outputs found
Micro and macro determinants of delisting and liquidity in indonesian stock market: a time-dependent covariate of survival cox approach
No full textCoxmodel is popular in survival analysis. In the case of time-varying covariateseveral subject-specific attributes possibly to change more frequently than others. This
paper deals with that issue. This study aims to analyze survival data with time-varying
covariate using a time-dependent covariate Cox model. The two case studies employed in
this work are (1) delisting time of companies from IDX and (2) delisting time of company
from LQ45 (liquidity index). The survival time is the time until a company is delisted
from IDX or LQ45. The determinants are eighteen quarterly financial ratios and two
macroeconomics indicators, i.e., the Jakarta Composite Index (JCI) and BI interest rate
that changes more frequent. The empirical results show that JCI is significant for both
delisting and liquidity whereas BI rate is significant only for liquidity. The significant
firm-specific financial ratios vary for delisting and liquidity
Comparison between hybrid quantile regression neural network and autoregressive integrated moving average with exogenous variable for forecasting of currency inflow and outflow in bank Indonesia
No full textSome problems arise in time series analysis are nonlinearity and heteroscedasticity. Methods that can be used to analyze such problems are neural network and quantile regression. There are a lot of studies and developments on both methods, but the study that focuses on the performances of combination of these two methods applied in real case are still limited. Therefore, this study performed a comparison between hybrid Quantile Regression Neural Network (QRNN) and Autoregressive Integrated Moving Average with Exogenous Variable (ARIMAX). Both methods were employed to model the currency inflow and outflow from Bank Indonesia in Nusa Tenggara Timur province. Based on the empirical result, the hybrid QRNN method provided better forecasting for currency outflow whereas the ARIMAX resulted in better forecasting for the inflow
Localising forward intensities for multiperiod corporate default
Full text linkUsing a local adaptive Forward Intensities Approach (FIA) we investigate multiperiod corporate defaults and other delisting schemes. The proposed approach is fully datadriven and is based on local adaptive estimation and the selection of optimal estimation windows. Time-dependent model parameters are derived by a sequential testing procedure that yields adapted predictions at every time point. Applying the proposed method to monthly data on 2000 U.S. public rms over a sample period from 1991 to 2011, we estimate default probabilities over various prediction horizons. The prediction performance is evaluated against the global FIA that employs all past observations. For the six months prediction horizon, the local adaptive FIA performs with the same accuracy as the benchmark. The default prediction power is improved for the longer horizon (one to three years). Our local adaptive method can be applied to any other speci cations of forward intensities
Predicting financial distress in Indonesian life insurance companies with classification methods and synthetic features generation
Full text linkFinancial problems in life insurance companies can become serious if not addressed immediately. Companies experiencing financial distress, for instance, are unable to meet their obligations to pay their liabilities. A company can be categorized as experiencing financial distress when it has an RBC ratio of less than 120%—based on regulation by the Indonesian Finance Service Authority—or ROA < 0 (suffering loss). Therefore, financial distress prediction is carried out to assess the company's current financial condition so that it can be handled early. In this study, we aimed to predict financial distress of Indonesian life insurance companies. We utilized the Support Vector Machine (SVM) classification method, Generalized Extreme Value Regression (GEVR), and Extreme Gradient Boosting (XGB) and by incorporating synthetic feature generation in variable selection. The results of financial distress prediction obtained the best model that can predict the financial condition of life insurance companies in Indonesia at each size, where for sizes 0 and 1, the XGB model with variable selection produces accuracy values of 98.00% and 94.10%, respectively, and AUC values of 100% and 87.40%. Then, at size 2, we can use Stepwise Generalized Extreme Value Regression with accuracy and AUC results of 90.20% and 82.60%, respectively. Each addition of size to the time window classification results tends to reduce the model's performance in predicting the financial condition of life insurance companies in Indonesia
Impact of Covid-19 Vaccination and Financial Policies on Indonesia’s Property Loan Growth
Full text linkResearch Originality: This study provides a novel examination of the impact of COVID-19-related financial policies on property loan growth in Indonesia, a critical area with limited prior quantitative research.Research Objectives: The purpose of this research is to assess how interventions such as Loan-to-Value (LTV) over Finance-to-Value (FTV) ratio (LTV/FTV) relaxation, COVID-19 vaccination as a metric for public activity restrictions, and changes in deposit insurance rates have influenced property loan dynamics during the pandemic.Research Methods: Using monthly banking data from January 2016 to May 2022, this study employs ARIMA Intervention Analysis to capture the effects of these policies.Empirical Results: The empirical results reveal a significant positive shift in property loan growth ten months after the first intervention and a notable impact two months after the third intervention, whereas the second intervention shows limited influence.Implications: These findings imply that integrating COVID-19 vaccination targets into public policy and adjusting deposit insurance rates are effective strategies for sustaining the property loan sector during economic crises. These results provide insights into the role of vaccination targets and financial adjustments in supporting the property loan sector during economic disruptions, offering valuable considerations for future policymaking in similar contexts.JEL Classification: C22, C51, C52, C53, C54
KINERJA ECONOMIZER PADA BOILER
No full textThis paper employed the dual response approach for case of Multivariate Robust Parameter Design (MRPD) which is developed by Del Castillo and Miro Quesada. MRPD method can be applied for any design of experiment. The optimization in this method uses minimizing variance function with restriction on mean function. In this paper, MRPD is applied to the case of optimization of heat transfer efectivity and operational cost at economizer. Those two responses are optimized by setting the level of control factors; diametre of tube hole, transversal spacing, and fin nearness. Temperature of feedwater is hold as a noise factor. Optimization is calculated by fmincon in MATLAB 7.0. The optimal condition for heat tranfer efectivity is 77.17% and operational cost is 30.58 kW. The optimal condition is attained at diametre of tube hole 1.5 inch, transversal spacing 3.5 inch, and fin density 3 fin/inch. Abstract in Bahasa Indonesia: Penelitian ini menggunakan metode pendekatan dual response terhadap kasus Multivariate Robust Parameter Design (MRPD) yang dikembangkan oleh Del Castillo dan Miro Quesada. Metode MRPD tidak mensyaratkan jenis rancangan percobaan yang dapat digunakan dalam proses optimasi, yang dilakukan dengan meminimalkan fungsi varians terhadap kendala fungsi rerata. Pada penelitian ini, metode MRPD diterapkan untuk kasus pencarian nilai optimal respon yaitu efektifitas perpindahan panas dan biaya operasi pada economizer. Optimasi kedua respon dilakukan dengan cara mengoptimalkan level faktor kontrol diameter luar tubing, transversal spacing, dan kerapatan fin. Temperatur feedwater berlaku sebagai faktor noise. Optimasi dilakukan dengan bantuan fmincon pada MATLAB 7.0 yang menghasilkan kondisi optimum untuk efektifitas perpindahan panas sebesar 77,17% dan biaya operasi sebesar 30,58 kW. Kondisi tersebut dicapai pada saat level diameter luar tubing sebesar 1,5 inci, transversal spacing sebesar 3,5 inci, dan kerapatan fin sebesar 3 fin/inci. Kata kunci: Economizer, dual response, Multivariate Robust Parameter Desig
Additive survival least square support vector machines: A simulation study and its application to cervical cancer prediction
No full textHybrid multivariate generalized space-time autoregressive artificial neural network models to forecast air pollution data at Surabaya
No full textMutual Information-Based Variable Selection on Latent Class Cluster Analysis
No full textMachine learning techniques are becoming indispensable tools for extracting useful information. Among many machine learning techniques, variable selection is a solution used for converting high-dimensional data into simpler data while still preserving the characteristics of the original data. Variable selection aims to find the best subset of variables that produce the smallest generalization error; it can also reduce computational complexity, storage, and costs. The variable selection method developed in this paper was part of a latent class cluster (LCC) analysis—i.e., it was not a pre-processing step but, instead, formed part of LCC analysis. Many studies have shown that variable selection in LCC analysis suffers from computational problems and has difficulty meeting local dependency assumptions—therefore, in this study, we developed a method for selecting variables using mutual information (MI) in LCC analysis. Mutual information (MI) is a symmetrical measure of information that is carried by two random variables. The proposed method was applied to MI-based variable selection in LCC analysis, and, as a result, four variables were selected for use in LCC-based village clustering
Multivariate Gamma Regression: Parameter Estimation, Hypothesis Testing, and Its Application
No full textGamma distribution is a general type of statistical distribution that can be applied in various fields, mainly when the distribution of data is not symmetrical. When predictor variables also affect positive outcome, then gamma regression plays a role. In many cases, the predictor variables give effect to several responses simultaneously. In this article, we develop a multivariate gamma regression (MGR), which is one type of non-linear regression with response variables that follow a multivariate gamma (MG) distribution. This work also provides the parameter estimation procedure, test statistics, and hypothesis testing for the significance of the parameter, partially and simultaneously. The parameter estimators are obtained using the maximum likelihood estimation (MLE) that is optimized by numerical iteration using the Berndt–Hall–Hall–Hausman (BHHH) algorithm. The simultaneous test for the model’s significance is derived using the maximum likelihood ratio test (MLRT), whereas the partial test uses the Wald test. The proposed MGR model is applied to model the three dimensions of the human development index (HDI) with five predictor variables. The unit of observation is regency/municipality in Java, Indonesia, in 2018. The empirical results show that modeling using multiple predictors makes more sense compared to the model when it only employs a single predictor
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