The Determinants of Credit Risk Under Dual Banking System: Indonesian Experience Based on Bank Specific Variables

In Indonesian banking system, conventional banks are operating side by side with Islamic banking in a dual banking system. In terms of the credit risk determinants, Islamic banks should be affected by the different factors as conventional banks. However, the similarity of Islamic banks and the conventional bank in terms of contracts might lead to the opinion the same variables are affecting the performance of Islamic and conventional banks. The objective of the study is to examine and obtain an understanding on how the credit and financing in Indonesian dual banking system responses to changes in bank-specific variables. The main approach to fit the model used in this study is the dynamic panel data. Based on the result of the combined model, there are some independent variables that significantly affect credit risk. Profitability significantly affects credit risk with a negative relationship. While size significantly affects credit risk with a positive relationship. When it comes to the dummy variable, it can be said that the type of bank doesn’t play a significant role in determining the credit risk. In other word, there is no difference between Islamic bank and conventional banks in terms of credit risk. To analyze the crisis effect deeper, we compare the result of conventional banking model 2016-2020 and Islamic banking model 2016-2020. There is no independent variable that significantly affect the credit risk in the conventional banking model 2016-2020, three out of four independent variables affect credit risk significantly in the Islamic banking model 2016-2020. This is because conventional banks tend to play safe by avoiding the disbursement of credit and focusing on derivatives. However, this strategy is not suitable for Islamic banking as they are not allowed to do speculative activities. Islamic banking are still focusing on traditional banking activity.

Identifying Individualized Risk Profiles for Radiotherapy-Induced Lymphopenia Among Patients With Esophageal Cancer Using Machine Learning

PURPOSE Radiotherapy (RT)-induced lymphopenia (RIL) is commonly associated with adverse clinical outcomes in patients with cancer. Using machine learning techniques, a retrospective study was conducted for patients with esophageal cancer treated with proton and photon therapies to characterize the principal pretreatment clinical and radiation dosimetric risk factors of grade 4 RIL (G4RIL) as well as to establish G4RIL risk profiles. METHODS A single-institution retrospective data of 746 patients with esophageal cancer treated with photons (n = 500) and protons (n = 246) was reviewed. The primary end point of our study was G4RIL. Clustering techniques were applied to identify patient subpopulations with similar pretreatment clinical and radiation dosimetric characteristics. XGBoost was built on a training set (n = 499) to predict G4RIL risks. Predictive performance was assessed on the remaining n = 247 patients. SHapley Additive exPlanations were used to rank the importance of individual predictors. Counterfactual analyses compared patients' risk profiles assuming that they had switched modalities. RESULTS Baseline absolute lymphocyte count and volumes of lung and spleen receiving ≥ 15 and ≥ 5 Gy, respectively, were the most important G4RIL risk determinants. The model achieved sensitivitytesting-set 0.798 and specificitytesting-set 0.667 with an area under the receiver operating characteristics curve (AUCtesting-set) of 0.783. The G4RIL risk for an average patient receiving protons increased by 19% had the patient switched to photons. Reductions in G4RIL risk were maximized with proton therapy for patients with older age, lower baseline absolute lymphocyte count, and higher lung and heart dose. CONCLUSION G4RIL risk varies for individual patients with esophageal cancer and is modulated by radiotherapy dosimetric parameters. The framework for machine learning presented can be applied broadly to study risk determinants of other adverse events, providing the basis for adapting treatment strategies for mitigation.

Hand-Wrist Disorders in Chainsaw Operators: A Follow-Up Study in a Group of Italian Loggers

Despite the mechanization process implemented in arboriculture, logging tasks are still manually performed by chainsaw operators, which therefore are exposed to the risk of developing hand-wrist musculoskeletal disorders. Our research aimed to: (a) define whether the slight changes observed in 2017 showed an evolution to overt diseases; (b) study some risk determinants for these diseases such as age, working experience, and performing a secondary job. We recruited in a two-year follow-up study, 38 male forestry workers performing logging tasks employed in the Sicilian Forestry Department located in Enna. All the subjects underwent: (1) personal data collection; (2) administration of questionnaire addressed at upper limbs symptoms with a hand chart; (3) physical examination of the upper limbs, including Tinel’s and Phalen’s maneuvers; (4) ultrasound investigation of the hand-wrist area. In the two-year follow-up study we registered an overall increasing in wrist disorders, thus we can assume that forestry workers may be a target population for wrist diseases and deserve a particular attention in workers’ health surveillance programs. Interestingly, the prevalence of wrist-hand disorders resulted to be higher in younger workers.

Recruitment of Employees—Assumptions of the Risk Model

The aim of this article is to present the risk model premises related to worker recruitment. Recruitment affects the final selection of workers, whose activities contribute to corporate competitive advantages. Hiring unfavorable workers can influence the results produced by an organization. This risk mostly affects situations when searching for workers via the external labor market, although it can also affect internal recruitment. Therefore, it is necessary to attempt to identify recruitment risk determinants and classify their meaning in such processes. Model formation has both theoretical and intuitive characteristics. Model dependencies and their characteristics are identified in this paper. We attempted to assess the usability of the risk model for economic praxis. The analyses and results provide a model identification of dependencies between the factors determining a workers recruitment process and the risk which is caused by this process (employing inadequate workers who do not meet the employer’s expectations). The identification of worker recruitment process determinants should allow for practically reducing the risk of employing an inadequate worker and contribute to the reduction in unfavorable recruitment processes. The added value of this publication is the complex identification of recruitment process risk determinants and dependency formulations in a model form.

EXPRESS: Magnetic Resonance Lymphangiography in Group 1 Paediatric Pulmonary Arterial Hypertension

Pulmonary hypertension could have thoracic lymphatic abnormalities caused by right ventricular failure. Since there is no description of such abnormalities, the purpose of this study was to investigate them with magnetic resonance. Prospective review magnetic resonance T2-weighted lymphangiography was performed between January 2017 and October 2019 through quantitative thoracic duct diameter, diameter index and qualitative lymphatic abnomalities types: 1- little or none abnormalities, 2-abnormalities in supraclavicular region, 3-abnormalities extending into the mediastinum, and 4-abnormalities extending into the lung. Five patients group 1 pulmonary arterial hypertension participated in this study. The mean: age was 12.44±4.92 years old, three male and two female, The quantitative analysis yielded the following results: mean thoracic duct diameter of 2.92±0.16 mm, and thoracic duct index 2.28±1.03 mm/m2. Qualitative lymphangiography abnormalities were type 1 in three patients, type 2 in one, all with low risk determinants and type 3 in one with hight risk determinants and rigth ventricular faillure. Magnetic resonance T2-weighted lymphangiography in group 1 paediatric pulmonary arterial hypertension allowed for the identification of the thoracic duct, wich was used to perform both quantitative and qualitative analysis of thoracic lymphatic abnormalities, in particular when increased high risk determinants and rigth ventricular faillure were present, These features represents an extracardiac findings useful to understand systemic venous congestion impact on lymphatic system.

Literature Survey and Research Agenda of Risk Determinants in Indian Equities and Machine Learning

Notwithstanding the financial slowdown and severity of the Coronavirus pandemic during 2020, several retail investors ventures directly to the secondary equities market, setting off gigantic purchasing. A review of SEBI data indicates that over 6 million new dematerialization accounts between April and September 2020 are about 125 percent growth on year on year basis. At the same time, data reported by AMFI shows net outflows from equity funds by retail investors. These data points indicate that retail investors may have opted to invest using direct stock investments instead of relying on the equity mutual fund manager. Equity Investment is a dynamic process requiring and require considering different variables in selecting and, more importantly, avoiding stocks. The cornerstone of wealth creation is to invest in stores at a price considerably smaller than their intrinsic value. The very foundation of creating long-term wealth using equities is deeply embedded. One is buying businesses at a price substantially below its intrinsic value (intrinsic value indicates the entity's future cash flows after estimating the number of accounting risk, macro-economic, managerial, and behavioral risk determinants). This Literature review, therefore, is organized to cover Behavioral, Accounting, Macro-economic, Volatility, and Management theories and Forecasting and ML techniques for clustering, predictions, and classification to support risk decisions using different models, e.g., ARIMA, LSTM, VAR, Facebook Prophet, ARCH and GARCH family models, etc. The literature review also establishes that the concept of risk is highly subjective and is perceived by different investors differently; it is not always entirely objective and outside the beliefs, cognitive and socio-cultural considerations requiring careful assessment before making investment decisions. However, examining the critical risk indicators would allow investors to make a more informed decision. The research gap and identified agenda for further review were defined and assessed using valuable ABCD and SWOT management frameworks. Consequently, the literature investigation findings are analyzed by offering recommendations for creating a comprehensive research agenda pertinent to long-term equity investors in the Indian Equity market.

Credit Risk and Operational Risk Determinants of Conventional and Islamic Banking in Malaysia and Bahrain

The focus of this study on both Islamic and conventional banks is to consider the unique duality banking system in the countries. Data is obtained from annual reports and websites of selected banks. Financial statements for 10 years beginning from 2009 until 2018 were used for extracting data for analyzing the credit and operational risk using E-view software. Furthermore, panel data analysis were used in combination with time series and cross section. The findings reveal that the determinants of credit risk and operational risk are difference between banking in Malaysia and Bahrain. The banking system will have a solid risk management system that will assist Malaysia and Bahrain to develop a strong economy as well as the possible adoption of risk management by the international banking system.

Analysis of the determinants of bank liquidity risk: The case of Islamic banks in the UAE

This article aims to examine the principal parameters that impact the liquidity risk incurred by Islamic banks in the UAE. The study examines annual data from four Islamic banks in the UAE. The Data is extracted from their annual activity reports and financial results. A multiple linear regression model is used to assess the impact of six bank-specific variables (Return on Equity, return on assets, size of the bank, liquidity gaps, non-performing loans and capital adequacy ratio) on the liquidity risk of UAE Islamic banks. The designed model shows that ROA and NPL negatively impact the liquidity risk of the studied banks, while the other determinants, namely size, ROE, liquidity gaps and CAR contribute to the improvement of liquidity of UAE banks. Thus, our empirical results complement the existing studies related to the analysis of liquidity risk determinants incurred by Islamic banks operating in the MENA region, especially Emirati banks.