Non-banking Financial Companies Research Articles

비은행권 금융회사 대출의 가계연체율 결정요인에 관한 연구

The purpose of this study is to analyze the dynamic interrelationship between household overdue rate of the banking and the non-banking financial companies, the factors of the household’s ability to pay, and the factors of household’s equity capital. We set up the model based on the solvency hypothesis and the equity capital hypothesis to analyze the effect of household overdue rate in banking and non-banking sectors. We identified the dynamic effects between related variables through impulse response analysis and variance decomposition analysis using the VAR model. The result of the analysis is as following. First, the household overdue rate in the banking sector was affected only by the previous one. Because the rate remains very low, the household default risk in the banking sector is very low. Second, the household overdue rate in the non-banking sector was more affected by household consumption than household income. Third, the household overdue rate in the non-banking sector was more affected by changes in house prices in metropolitan area than non-metropolitan area. Forth, the household overdue rate in the non-banking sector was more affected by changes in house prices in single-family house than apartment and multi-units house. According to these research results, it is necessary to stabilize the price control and reduce the burden of the financially vulnerable in terms of managing the risk of household overdue rate of non-banking companies. Furthermore, the government should strengthen integrative financial regulations of non-banking financial companies to manage potential household default risks.

ANALYSIS AND PROGNOSTICATION OF BANKRUPTCY AMONG BSE (BOMBAY STOCK EXCHANGE INDIA) LISTED COMPANIES

When a company faces difficulty in maintaining stability in financial performance of the company, there is a vast chances of bankruptcy occurrence. There are two possible ways when the company cannot financially survive, one is debt restructuring and another one is proceeding for bankruptcy. The objective of the research is to predict the sample companies which have the chance of bankruptcy or not. The research design and sampling method for this project is descriptive research and non-probability sampling method. Using Sample size of 186 Non-Banking Financial companies’ secondary data that is five years financial report, the Altman Z score analysis (Multi Discriminant Analysis) was conducted with a help of Microsoft office Excel 2021. Based on the analysis results, 11 out of 186 companies were in unsafe zone in 2021. Similarly, 14 out of 186 companies were in unsafe zone in 2020, and 8 out of 186 companies were in unsafe zone in 2019.

A Comparative Study of Machine Learning Algorithms for Predicting Loan Default and Eligibility

Loosening up credits to corporates and individuals for the smooth working of creating economies as is INDIA unpreventable. As a growing number of customers apply for credits in the banks and non-banking financial companies (NBFC), it is really pursuing banks and NBFCs with confined financing to contraption a standard objective and safe framework to credit money to its borrowers for their financial necessities. Also, starting late NBFC inventories have persevered through a basic ruin similarly as the stock expense. It has added to a sickness that has also spread to other money related stocks, horribly impacting the benchmark of late. In this paper, an endeavour is made to assemble the risk related to picking the sensible person who could repay the credit on time thus keeping the bank’s non-performing assets (NPA) on hold. This is refined by dealing with the records of the customer who secured credits from the bank into a readied man-made intelligence model which could yield a definite result. The prime point of convergence of the paper is to choose whether or not it will be ensured to allocate the loan to a particular person.

Factors Affecting Social and Financial Efficiency of MFIs: DEA and Tobit Approach

This study aims to examine the factors that affect the social and financial efficiency of Indian Microfinance Institutions (MFIs). Relative efficiency of 38 non-banking financial companies (NBFC)-MFIs has been measured using the non-parametric data envelopment analysis (DEA) approach. The determinants of social and financial efficiency of MFIs have been identified using censored Tobit regression. The study findings indicate that NBFC-MFIs are financially more efficient than social. Regression analysis results reveal that age and size are important variables of financial efficiency while leverage and size are social efficiencies. This may be the first extensive study measuring the determinants of efficiency on financial and social dimensions separately for Indian NBFC-MFIs. This study contributes to the scarce literature by providing the role of efficiency in the sustainability of MFIs in the long run.

Extrapolation of Loan Default using Predictive Analytics: A Case of Business Analysis

The research assesses the validity of a customer's appropriateness for a loan using a machine learning approach called predictive modeling. Banks and Non-Banking Financial Companies (NBFCs) are at danger of significant Non-Performing Assets (NPAs) due to customer non-payment of loans (Non-Performing Assets). The data for this study came from Kaggle, and eight different prediction models were employed to determine if the borrower would be able to repay the loan. Adaboost, κ-Nearest Neighbors (k-NN), Logistic Regression, Support Vector Machines (SVM), Decision Tree, Naive Bayes, Neural Networks, and Random Forest (RF) are the eight models, respectively. The purpose is to back up decisions made on the basis of factual evidence rather than subjective reasons. Classification Accuracy, Precision, Recall, and F-1 scores are the four performance parameters used to determine the results. With 70% and 30% respectively, the dataset is separated into train and test datasets. The whole analysis is done in two phases, with the first being a full model that is trained on 70% of the train data and the second being observed on 30% of the test data. The purpose of this study is to see how objective characteristics influence borrowers to default on loans, to identify the most common reasons for default, and to predict which customers would default. There are two evaluations we did for the research, wherein, first we took overall train set and make predictions using predictive modeling. The Adaboost predictive model delivers the greatest results, with a recall rate of 0.384, classification accuracy of 59.2 percent, true-positive rate of 69.74 percent. Second, we performed feature selection and discovered that Credit History with 31 percent had the utmost impact on loan default detection. By partitioning the dataset into Credit_History 1 and 0, we discovered that Credit History 1 produces superior results, with a rate of 0.444, 60.5 percent classification accuracy, and a true-positive rate of 68.7%.

A comparative analysis of msme finance in india

The Micro, Small and Medium Enterprises (MSME) sector is one of the dynamic sectors of the Indian economy in terms of productivity, contribution to GDP, and employment. Their role in nurturing entrepreneurship through innovations and generating employment opportunities has been commendable. Access to financial resources has been a catalyst as well as a challenge to the growth of this sector both in the short-term and long-term. The purpose of the study is to describe the sources of finance available for MSMEs in India and to comparatively analyze the trend in the provision of credit by the Scheduled Commercial Banks (SCB) and Non-Banking Financial Companies (NBFC) to the MSMEs. The findings of the study provide evidence that in India MSMEs look at the informal sources offinance predominantly compared to the formal financial institutions. Among the formal sources of finance, the NBFCs have outperformed the SCBs.

Voluntary Disclosures of Indian Microfinance Institutions

The microfinance sector is growing at an astonishing rate in India. Reserve Bank of India, from time to time, comes up with a lot of directions to maintain transparency and governance in the microfinance sector. The current study focuses on the voluntary disclosure (VD) of the Non-Banking Financial Companies- Microfinance Institutions in India. The study constructs an unweighted index to measure the VD scores of the firms. The study also estimates the technical efficiency of the firms in meeting dual objectives of social and financial efficiency of the microfinance institutions. The empirical testing derived the association of the VD score and the efficiency from the data sourced from 2015 to 2019. The findings suggest a negative association between VD and efficiency. The study's major contribution is the type of VD disclosed by the firms. Finding also suggests that firms consider discretionary disclosure a wasteful activity. The dual performance is low but shows a gradual increase. The study is relevant for the policymakers and managers to check the disclosure strategy and the patterns of these firms, which mainly depend on the donors for raising funds for their sustainability.

Do we want more NBFC's to fail? Learnings from the Indian Financial System

Financial Intermediation plays an important role in the growth and development of the economy. The active participation of the non-banking financial companies' role is as crucial as the commercial banks in India. The repeated failure of these entities posed numerous questions upon the potential functioning of the NBFC's in India.

PROFITABILITY OF A FIRM AND ITS DETERMINANTS - EVIDENCE FROM INDIAN NBFC SECTOR

Over the past few years, the NBFC sector in India has witnessed a noteworthy transformation by catering to employment generation, infrastructure development, wealth creation and financial upliftment of the weaker sections. With the growing credit of NBFCs, their profitability becomes an important aspect for their existence. Hence, the current student is aimed at identifying the determinants of profitability of Non-Banking Financial Companies (NBFCs) in India. The data pertaining to top 10 NBFCs listed on BSE for a period of five years from FY2015-16 to FY2019-20 was considered as sample for the study. Two different regression models were used for the data analysis where ROA and ROE were used as dependent variables. Capital adequacy, asset quality, operating profitability and net interest margin were used as independent variables in both the models. It was found that capital adequacy and operating profitability has significant impact on ROA. Whereas the factors significantly impacting ROE were capital adequacy, operating profitability and net interest margin.

Impact of Efficiency on Voluntary Disclosure of Non-Banking Financial Company—Microfinance Institutions in India

This paper investigates how the financial and social efficiency of firms influence the extent of the voluntary disclosure of Non-Banking Financial Companies–Micro Financial Institutions (NBFC-MFI). The study constructed an unweighted index of voluntary disclosure to estimate the level of voluntary disclosure of all of the included firms from the years 2015–2019. The financial and social efficiency, which is analogous to the technical efficiency of production theory and analyses both sustainability and outreach, respectively, was estimated using data envelopment analysis (DEA). The panel data analysis was completed, and a positive association of financial efficiency was estimated. The social efficiency was found to have no relationship to the voluntary disclosure level. This paper contributed to the literature by providing new determinants of voluntary disclosure. The study examines the econometric model and suggests that financially sustainable firms that utilize these resources well are more open to outsiders, while socially efficient firms are reluctant to voluntary disclosure, which also includes social activities, and consider this as a wasteful activity. The findings of this study are relevant to industry practitioners and regulators, who need to think upon the sustainability of this crucial sector by meeting the dual objectives of financial and social performance. This study is helpful to all stakeholders as well as for the government, who can use the results to design additional rules for the NBFC–MFI. This study will also help firms to design disclosure strategies to ascertain goodwill and less cost of capital, with easy access to funds.

Productivity growth, catching-up and technology innovation in microfinance institutions in India: evidence using a bootstrap Malmquist Index approach

PurposeThis paper aims to examine the total factor productivity (TFP) change and its components: efficiency change and technical change in microfinance institutions (MFIs) in India operating from 2005 to 2018. The study also scrutinizes the variations in productivity levels across the distinct organizational form and size groups of MFIs. In addition to this, the authors identify the contextual factors that determine TFP growth, catching-up and technology innovation in MFIs.Design/methodology/approachThe study employs a smooth homogeneous bootstrap estimation procedure of Simar and Wilson (1999) for obtaining reliable estimates of Malmquist indices –productivity and its components – in a data envelopment analysis (DEA) framework for individual MFIs. In order to identify the determinants of productivity change and its components, the study follows Simar and Wilson's (2007) guidelines and applies a bootstrap truncated regression model. The double bootstrap procedure performs well, both in terms of allowing correct estimation of bias and deriving statistically consistent productivity estimates in the first and root mean square errors in the second stage of the analysis.FindingsThe empirical results reveal that the MFIs have shown average productivity growth of 6.70% during the entire study period. The observed productivity gains are primarily contributed by a larger efficiency increase at the rate of 4.80%, while technical progress occurs at 2.3%. Nonbanking financial companies (NBFC)-MFIs outperformed non-NBFC-MFIs. Small MFIs show the highest TFP growth in terms of size groups, followed by the large MFIs and medium MFIs. The bootstrap truncated regression results suggest that the credit portfolio, size and age of MFIs matter in achieving higher productivity levels.Practical implicationsThe practical implication drawn from the study is that the Indian MFI industry might adopt the latest technology and innovations in the products, risk assessment and credit delivery to improve their productivity levels. The industry must focus on enhancing the managerial skill of its employees to achieve a high productivity level.Originality/valueThis study is perhaps the initial attempt to explain the productivity behavior of MFIs in India by deploying a statistically robust double bootstrap procedure in the DEA-based Malmquist Productivity Index (MPI) framework. The authors estimate the bias-adjusted productivity index and its decompositions, which represent more reliable and statistically consistent estimates. For contextual factors responsible for driving productivity change, the study deploys a bootstrap truncated regression approach.

RIPPLE EFFECT OF THE IL&FS CRISIS

Non-Banking Financial Companies (NBFC) are those financial institutions which cater to the demand of those customers whom banks think as sub-prime. In India these companies referred to as NBFC are currently very vulnerable and it is a concern for both RBI as well as the government. The major reason for the concern is the 2019 crisis in the NBFC sector. The crisis which ended in late 2019 was not an overnight event. The worsening of the conditions of NBFCs started in the mid-2018 and gradually deteriorated. The whole crisis timeline can be divided in 2 parts. The first part being the period in which the uncovering of the situation happens and the second part is the actions taken against the defaulters and measures to revive the sector. In this paper we have done an event study of the announcements made during the first period which showed the uncovering of crisis. The major announcements which took place in that period were considered and how those announcement affected the stock price of the related companies. The reason for doing this study was to assess the ripple effect of an announcement of one company on the company, industry and economy as a whole.

Assessment of the Heterogeneity Influencing the Superior of Non-Banking Finance Company for Assisting Consumer Durable Loans

Customers purchasing behavior has changed radically in the course of the most recent couple of years. The study aimed to identify major factors determining consumer's choice rules of the non-banking financial company (NBFC) for availing consumer durable loans (CDLs). Essential information was gathered from 100 respondents by utilizing a survey including eight factors, recognized dependent on an audit of writing. Principal Component Analysis (PCA) was utilized as an extraction strategy by utilizing factor analysis. Three factors were isolated by using Eigenvalues standards and these three factors were assigned names such as service delivery, terms & conditions, and safety and security of fund respectively. It was suggested from the study that the non-banking financial companies aimed to expand their business should take cognizance of these factors in their marketing strategies.

Liquidity Risks in Non-Banking Financial Companies: An Analysis

Liquidity Risks in Non-Banking Financial Companies: An Analysis

Liquidity Risks in Non-Banking Financial Companies: An Analysis

Liquidity Risks in Non-Banking Financial Companies: An Analysis

Liquidity Risks in Non-Banking Financial Companies: An Analysis

Liquidity Risks in Non-Banking Financial Companies: An Analysis

Time series modelling, NARX neural network and hybrid KPCA–SVR approach to forecast the foreign exchange market in Mauritius

PurposeThis study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.Design/methodology/approachAutoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.FindingsThe results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.Research limitations/implicationsThe foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.Originality/valueThis is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.

Fintech Adoption in Non-Banking Sector: A Technology Acceptance Model Perspective

Fintech or financial technology originated in India as a fairly young sector. Non-banking financial companies (NBFCs) in India performs a vital role in meeting the monetary needs of the individuals and businesses and are now more concentrated on developing disruptive technology-driven solutions that enable the smooth creation, launch, deployment and execution of customized products and services. In this scenario, this study tries to understand the Fintech adoption behaviour of customers of NBFCs. It also aims to understand important demographic trends in Fintech adoption behaviour. The research is basically centered on the Technology Acceptance Model, which combines brand and service trust in order to explain the impact on behavioral intention. This study is undertaken as an exploratory study among the customers of NBFCs in Kerala during a period of three months in 2019. Using Confirmatory Factor Analysis, it’s found that the model is a good fit and hypothesis path analysis proved that all variables that were considered affect behavioral intention positively. It is also found some demographic factors embracing Fintech.

Merger of Public Sector Banks : A Case of Chennai Bank Ltd. and Bhopal Bank Ltd.

Chennai Bank Ltd. is the leading South Indian commercial bank having more than 2,875 small and large size branches all across the Southern states of India. It was established in the year 1966 just after the nationalization of private banks in India. Bhopal Bank Ltd. is one of the oldest banks of India founded by the Nawabs of Bhopal. After nationalization, it started working under the umbrella of the Government of India. Over a decade, increase in the number of non-performing assets, declining contribution of selected public sector banks, and expanding private sector role as banks or non-banking financial companies, etc. are few points of observation, which led to a new idea of merging up of public sector banks. This case study discussed why the Government of India has strategically decided to merge Chennai Bank Ltd. and Bhopal Bank Ltd. and what changes and challenges this merger will bring such as post-merger restructuring, struggle for financial stability, difference in organizational culture, employee layoffs, difference in demographic customer database, and opening up or shutting down of branches due to increase in numbers of branches, etc.

Slowdown and Crisis in the Indian Economy - A Study of the Macroeconomic Developments between the Global Financial Crisis and the COVID-19 Crisis (2011-12 to 2019-20)

Slowdown and Crisis in the Indian Economy - A Study of the Macroeconomic Developments between the Global Financial Crisis and the COVID-19 Crisis (2011-12 to 2019-20)