The Experts below are selected from a list of 21615 Experts worldwide ranked by ideXlab platform
Yunwei Cui - One of the best experts on this subject based on the ideXlab platform.
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a parameter driven Logit Regression model for binary time series
Journal of Time Series Analysis, 2014Co-Authors: Yunwei CuiAbstract:We consider a parameter-driven Regression model for binary time series, where serial dependence is introduced by an autocorrelated latent process incorporated into the Logit link function. Unlike in the case of parameter-driven Poisson log-linear or negative binomial Logit Regression model studied in the literature for time series of counts, generalized linear model (GLM) estimation of the Regression coefficient vector, which suppresses the latent process and maximizes the corresponding pseudo-likelihood, cannot produce a consistent estimator. As a remedial measure, in this article, we propose a modified GLM estimation procedure and show that the resulting estimator is consistent and asymptotically normal. Moreover, we develop two procedures for estimating the asymptotic covariance matrix of the estimator and establish their consistency property. Simulation studies are conducted to evaluate the finite-sample performance of the proposed procedures. An empirical example is also presented.
Azwar Iskandar - One of the best experts on this subject based on the ideXlab platform.
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model prediksi financial distress dengan binary Logit studi kasus emiten jakarta islamic index application of binary Logit Regression on financial distress prediction of jakarta islamic index
MPRA Paper, 2015Co-Authors: Azwar IskandarAbstract:Indonesian Abstract: Penelitian ini bertujuan untuk menganalisis: (i) rasio keuangan yang terpilih sebagai prediktor dalam memprediksi financial distress; (ii) tingkat akurasi model prediksi financial distress yang terbentuk dari analisis. Data yang digunakan adalah data sekunder dari Bursa Efek Indonesia (BEI) berupa Ringkasan Kinerja Perusahaan Tercatat periode 2012-2013. Dengan teknik purposive sampling, penelitian ini menggunakan sampel 23 emiten yang terhitung dalam saham JII. Penelitian ini menggunakan metode analisis Binary Logit Regression. Hasil empiris menunjukkan bahwa rasio-rasio keuangan dalam laporan keuangan perusahaan yang terdiri dari : Current Ratio (CR), Operating Profit Margin (OPM), Return Of Asset (ROA), Return On Equity (ROE) dan nilai beta saham (YLD) dapat digunakan untuk membedakan dan mengklasifikasikan perusahaan ke dalam kelompok yang mengalami financial distress dan non financial distress. Rasio keuangan yang signifikan memprediksi kemungkinan terjadinya financial distress yaitu ROA dan ROE. Rasio-rasio tersebut digunakan dalam model prediksi financial distress berdasarkan indikator Debt to Total Aset Ratio (DAR) (model kedua) dan terbukti layak secara statistik untuk digunakan sebagai model dengan akurasi prediksi 90,9%. Model prediksi financial distress ini dapat digunakan sebagai early warning signal. Bagi pihak regulator seperti BEI, Otoritas Jasa Keuangan dan lainnya, dapat menggunakan model prediksi financial distress ini sebagai tool dalam menjalankan fungsi evaluasi, review dan pengawasan terhadap emiten.English Abstract: The purposes of this research were to analyze: (i) financial ratios choosen as predictor for financial distress prediction; (ii) accuration rate of prediction model that formed from analysis. The data used was from resume of financial report of companies at Indonesia Stock Exchange period of 2012-2013. This research used 23 companies of JII as samples with purposive sampling method. This research used binary Logit Regression analysis. The empirical result shown that the financial ratios such as Current Ratio (CR), Operating Profit Margin (OPM), Return Of Asset (ROA), Return On Equity (ROE) and yield (YLD) can be used for comparing and classifying companies to distress and non-distress group. The financial ratios such as ROA and ROE significantly can be used in predition model with accuration rate of 90,9%. That model also can be used as early warning signal. For regulators such as BEI and Otoritas Jasa Keuangan can use it as tool for evaluating, reviewing and controlling of companies.
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model prediksi financial distress dengan binary Logit studi kasus emiten jakarta islamic index application of binary Logit Regression on financial distress prediction of jakarta islamic index
MPRA Paper, 2015Co-Authors: Azwar IskandarAbstract:The purposes of this research were to analyze: (i) financial ratios choosen as predictor for financial distress prediction; (ii) accuration rate of prediction model that formed from analysis. The data used was from resume of financial report of companies at Indonesia Stock Exchange period of 2012-2013. This research used 23 companies of JII as samples with purposive sampling method. This research used binary Logit Regression analysis. The empirical result shown that the financial ratios such as Current Ratio (CR), Operating Profit Margin (OPM), Return Of Asset (ROA), Return On Equity (ROE) and yield (YLD) can be used for comparing and classifying companies to distress and non-distress group. The financial ratios such as ROA and ROE significantly can be used in predition model with accuration rate of 90,9%. That model also can be used as early warning signal. For regulators such as BEI and Otoritas Jasa Keuangan can use it as tool for evaluating, reviewing and controlling of companies.
Yagoub Elryah - One of the best experts on this subject based on the ideXlab platform.
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a study of malaysian islamic banks competitiveness Logit Regression approach
Social Science Research Network, 2014Co-Authors: Yagoub ElryahAbstract:Banking sector plays an important role in Malaysia development. The specific objective of this study was therefore to what extent Islamic Banks increased competition in banking sector in Malaysia. With the aid of annually data from the BNM, the study covered the period 2002 to 2012. The present study used the Logit Regression, the dependent variable was taken by means of dummy, which takes zero for typical and 1 with regard to Islamic banks. By utilizing SPSS, twenty six financial ratios associated with 14 banks were being thoroughly checked by means of enter, forward and backward options inside the search of best variables to distinguish between Islamic and also conventional banks. Our main findings are that Islamic banking banks are better in profits comes to operating income and the diminution in the value of underlying tangible assets arising from loss, damage or non-maintenance of the asset presents another kind of risk that is not of any concern to traditional bond holders. Sharing of losses adversely affect the incentives of both the issuer and the investor making the Islamic debt non-competitive in international or domestic market.
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a study of malaysian islamic banks competitiveness Logit Regression approach
International Journal of Social Science and Humanities Research, 2014Co-Authors: Yagoub ElryahAbstract:������������������������ ������������ �������� ���������� ������������������������������ ������ ′ ���������� ���� �������������� ������ ���������� �������� ���������� ������������������������ �������������� �� ′ �������������� �� �������� ���������� ���������������� ������������������ ������������������ ������ �� ���������������� ���������� ������������������������ �������������������������������� ������������������������ ������������ ������������������������ ������������ �������������� ������ ���������� (��������������) ������������������������ ������������ ������������������������ ������������ ������������������������ ������������ ������������������������ ������ �������������������������� ������������������������ �������������������������������� ������������������������ �������������������������������� ������������������������ ������������ ������������������������ ���������������������������� ����
Gamin Kim - One of the best experts on this subject based on the ideXlab platform.
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Logit Regression based bankruptcy prediction of korean firms
Asia-pacific Journal of Risk and Insurance, 2012Co-Authors: Chulwoo Han, Hyeongmook Kang, Gamin KimAbstract:In this article, we develop a bankruptcy prediction model for Korean firms that utilize Logit Regression. We find that not only financial accounting ratios but equity market inputs and macro-economic variables are also important predictors of bankruptcy. However, unlike the findings of Campbell et al. (2008), using market value of equity in computing total assets did not improve the model. We compare the model with a Merton-type structural model and find that our model demonstrates a higher prediction power in distinguishing distressed firms from healthy firms. Though our model proves to perform better, we are careful to make a conclusion and rather suggest using several models for the purpose of risk management to reduce model risk.
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Logit Regression based bankruptcy prediction of korean firms
Social Science Research Network, 2011Co-Authors: Chulwoo Han, Hyeongmook Kang, Gamin KimAbstract:In this article, we develop a bankruptcy prediction model for Korean firms that utilize Logit Regression. We find that not only financial accounting ratios but equity market inputs and macro-economic variables are also important predictors of bankruptcy. However, unlike the findings in Campbell et al. (2008), using market value of equity in computing total assets did not improve the model. We compare the model with a Merton type structural model and find that our model demonstrates a higher prediction power in distinguishing distressed firms from healthy firms. Though our model proves to perform better, we are careful to make a conclusion and rather suggest to use several models for the purpose of risk management to reduce model risk.
Jiti Gao - One of the best experts on this subject based on the ideXlab platform.
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local Logit Regression for loan recovery rate
Journal of Banking and Finance, 2021Co-Authors: Nithi Sopitpongstorn, Param Silvapulle, Jiti Gao, Jeanpierre FenechAbstract:Abstract This is the first paper to propose a flexible local Logit Regression for defaulted loan recoveries that lie in [0,1]. Via a simulation study, we demonstrate that the proposed model is robust to nonlinearity, and non-normality of errors. Applied to Moody’s dataset, the local Logit model uncovers the intrinsic nonlinear relationship between loan recoveries and covariates, which include loan/borrower characteristics and economic conditions. We exploit the empirical features of the local Logit model to improve the specification of the standard Regression for the fractional response variable (RFRV) model, which we refer to as the calibrated-RFRV model. The estimation of the calibrated-RFRV model is more straightforward and faster than the local Logit model. The overall out-of-sample predictive performance of the calibrated-RFRV is superior to the local Logit, RFRV, neural network (NN), Regression tree (RT) and Inverse Gaussian (IG) models. The local Logit model outperforms others in quantile forecasting, showing the attractiveness of this model for estimating tail risks, the accurate estimation of which is beneficial to risk managers.
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local Logit Regression for recovery rate
Social Science Research Network, 2017Co-Authors: Nithi Sopitpongstorn, Param Silvapulle, Jiti GaoAbstract:We propose a flexible and robust non-parametric local Logit Regression for modelling and predicting defaulted loans' recovery rates that lie in [0,1]. Applying the model to the widely studied Moody's recovery dataset and estimating it by a data-driven method, the local Logit Regression uncovers the underlying nonlinear relationship between the recovery and covariates, which include loan/borrower characteristics and economic conditions. We find some significant nonlinear marginal and interaction effects of conditioning variables on recoveries of defaulted loans. The presence of such nonlinear economic effects enriches the local Logit model specification that supports the improved recovery prediction. This paper is the first to study a non-parametric Regression model that not only generates unbiased and improved recovery predictions of defaulted loans relative to the parametric counterparts, it also facilitates reliable inference on marginal and interaction effects of loan/borrower characteristics and economic conditions. Moreover, incorporating these nonlinear marginal and interaction effects, we improve the specification of parametric Regression for fractional response variable, which we call a "calibrated" model, the predictive performance of which is comparable to that of local Logit model. This calibrated parametric model will be attractive to applied researchers and industry professionals working in the risk management area and unfamiliar with non-parametric machinery.
