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Literature Review On Funds Mobilization Strategies


In 1961, the Non-Banking Finance Companies (NBFCs) in India were brought into loose yet legalized regulatory framework from largely unregulated framework. The regulation of these institutions was found to be necessary for ensuring efficacy of credit and monetary policy, safeguarding depositors’ interests and ensuring healthy growth of this sector (Vasudev 1998). The government constituted various committees to suggest the regulatory framework for the non-banking financial sector. The Bhabatosh Datta Study Group (1971), James Raj Study Group (1975), Chakravarthy Committee (1987) and Narashimham Committee (1991) were the important committees to suggest transformation of unregulated non-banking financial sector into regulated one. The inherent strengths of NBFCs such as high-level customer contact and satisfaction, geographical proximity, strong recovery mechanism were the drivers of their performance. Higher rates of interest on deposits offered by them afforded better opportunity of channelizing domestic savings into the financial markets (Ingres 2005). NBFCs had, practically, not been subjected to entry barriers, limitations on fixed assets and holding inventories in the form of gilt investments as they are now (Thiyagarajan, Arulraj 2005). Moreover, the NBFCs are in a consolidation phase now.

As the financial stability was threatened with many of the NBFCs offering unimaginable rates of interest of public deposits, their financial viability was adversely affected because of unrestricted lending even in the case of group exposure. The Narashimam committee on banking sector reforms (Narashimam 1991) suggested the guidelines for banking system should be extended to bring the non-banking finance companies within the ambit of an effective regulatory framework. Khanna Committee (Khanna P.R.1997) and Vasudev Committee (Vasudev 1998) have recommended exclusive regulatory framework for these companies such as mandatory registration, capital adequacy, linking of net owned funds to deposits and application of prudential asset classification and income recognition norms. Non-Banking Financial Companies are reclassified broadly as Asset Finance Companies, Loan Companies and Investment Companies (Reserve Bank 2006).

With the application of the tighter regulations through Reserve Bank of India Directions 1998 (applicable to NBFCs); the advantages enjoyed by NBFCs vis-à-vis banks have eroded (Khan M.Y 2002). The purpose of the paper is to focus on the trends in funds mobilization and to identify the relationships among various types of funds and profitability of Asset Finance Companies (AFC, Equipment and Leasing NBFCs). Further from the literature, it is discernible that the NBFCs have started feeling the heat in the form of increased competition. In order to ward off the competition, they have been resorting to innovation in lending, diversification and exploration of new markets. From various studies published by the Reserve Bank of India (RBI), it is clear that the regulations have affected the financial strength of non-banking finance sector with many smaller finance companies have wound up their activities or merged with viable non-bank finance companies. Kim and Santomero (1988) and Kendall and Levonian (1992) have examined how the design of risk-based capital standards influences bank risk taking. Risk taking behavior influences the strategies to fund assets of banks, long-term costly funds are mobilized when profitability through high cost earning assets are in product mix. Public deposits are the easy yet costly source of financing to meet lending requirements.

Hence, the paper focuses on the key sources of funds and the mediating role played by short-term funds. The authors feel that it is necessary to develop a sound model to identify the mediational role played by short-term funds. In the succeeding sections, the paper will present the emerging trends in funds mobilization, the data, methodology employed, and finally structural equation model to capture mediating effects.

II Literature Review


Capital management refers to balancing the level of capital in such a manner that growth of assets and liabilities is sustainable without eroding public confidence or profitability. Sinkey (1992) uses Hempel and Yawitz's (1977) framework to arrive at a similar conceptual framework. The bank's primary objective of maximizing shareholders' wealth is depicted as being shaped by owners' preferences, management's attitudes and decisions, and society; also listed are six policy strategies to achieve that objective. Management’s attitudes and decisions, the regulatory and economic environment, and the objective of maximizing investors’ wealth (shareholders), in turn, influence these policies. The success of these policy strategies depends on the riskiness of a bank's balance sheet, that is, the nature of assets and the concentration of loan portfolios (Sinkey 1992). The primary objective of the board is to increase the realizable value of the equity whether on liquidation or through trading. The market value is conditioned by many factors viz., endogenous factors, namely, management attitudes, strategies, long-term business prospects and quality of assets and exogenous factors such as political, socio-economic environment and/or external shocks, therefore is subject to volatility (Kantawala 2004). According to Harker and Zenios (1998), financial performance of an institution – observable but non-actionable – can be affected by its performance along the axis of service delivery and financial intermediation. They concluded that the drivers of performance can be classified into, strategy, execution of strategy and the environment. Further, they felt diversified institutions benefit from opportunities for internal resource allocation and, therefore, can hold less capital and do more lending than more focused institutions. From the environment angle, they felt that drivers are innovation, regulations and technology

The book value is conditioned primarily by the endogenous factors such as profit generated, management attitude to share the wealth of the company with the shareholders and is more likely to stay stable. Further it is based on historical cost. Therefore, the increase in book value could be a best indicator of performance (Khan M.Y 2003). Although adoption of the risk-based standards has focused attention on capital levels, little if any attention has been given to the corresponding level of risk in bank portfolios and how the adoption of the risk-based capital standards may have impacted bank risk levels (Kevin Jacques & Peter Nigro 1997). Andres Almazan (2001) says “the regulatory shocks as a rebalancing of the optimal capital–expertise balance for banks in order to provide answers to this question about the effects of deregulation”.

Fries, Neven, and Seabright (2002) examine the effects of financial sector reform on the performance and competition of the banking sectors. In countries that have made significant progress on financial reforms, these authors find that banks make reasonable margins on loans, offer competitive rates on deposits, and make negative returns on equity, on average. In counties that have not proceeded very far in reforming their financial sectors, banks achieve high rates of return on equity but mainly at the expense of depositors, who are held hostage to low, sometimes negative, real returns on their accounts for lack of alternatives. Large block holdings are not homogenous and we therefore distinguish between types of stake holdings. Major sources of funds to these companies are bank borrowings and public deposits (Taxmann 2004). With the stock market scam, the exposure of Non-Banking Finance Companies in the form of lending on equities, initial public offerings are severely restricted to protect the interests of the depositors (Ingres 2006).

Many organizations in Canada that survived government funding cutbacks of the 1990s are financially fragile because they are now dependent on a complex web of unpredictable, short-term, targeted project funding that may unravel at any time (Katherine Scott 2003). In their model, banks in US that offer liquidity services (i.e., transactions accounts and related products) maximize their profits by making relationship loans, while banks making non-relationship loans or marketable “transactions” loans are better off using purchased fund financing—thus, long-term assets and liabilities (relationship loans, core deposits) tend to appear together on the balance sheets of relationship lenders, while short-term assets and liabilities (transactions loans, purchased funds) gravitate toward the balance sheets of transactions lenders (Fama 1980). Loan securitization has led to a strategic dichotomy in the banking industry, with large banks and small banks having quite different approaches to intermediation (DeYoung, Hunter, and Udell 2004). Small community banks are more likely to evaluate credit applications based on “soft” information about the borrower that cannot be used in an automated underwriting model, hold the loan in its portfolio, and fund the loan with core deposits. The researchers, therefore, focus on raising the public deposits as the major funding strategy. However, they fail to recognize the debentures, bank borrowings are forming core to the financing strategy in recent days. Can equity be accessed easily? Can it be a financing source? Many literatures support the view but researchers felt that the IPOs/Rights cannot be easy mode of raising funds in bear market conditions. It is the management attitude that form the core of the funding strategy (Hempel and Yawitz 1977)

Liquidity management demands the presence of short-term assets that can be quickly converted to cash to meet unexpected deposit withdrawals or funding needs, and liquidity requirements of the Supervising Bank. Interest rates and monetary growth within the economy have to be monitored as part of this process. Houston and James (1996), Johnson (1997), Krishnaswami, Spindt and Subramaniam (1998), and Cantillo and Wright (2000) found the existence of positive relationship between the use of public debt financing and firm characteristics such as size, leverage, age, and amount issued. The bank management will attempt to hold enough short-term assets to meet anticipated liquidity requirements, without unnecessarily lowering the profit performance due to the generally lower yields associated with such assets Sinkey (1992). The key issues in this paper raised 1. What are the interdependencies between various funding strategies? 2. How best their interdependencies can be modeled? 3. Which is the mediating variable?

B Approaches to Profit Maximization

The management should focus on bringing down the level of non-performing loans to maximize and that is the best strategy to ensure financial health. The lending of this sector is conditioned with uncertainty of recovery, the prudential norms prescribing the income recognition on the basis of provisioning requirements have put these companies on par with banks besides affecting the profitability due to non-performing assets norms (Kshisagar 2003). The cross-country empirical evidence for Asia suggests that the limited openness necessarily results in slower institutional development, greater fragility and higher costs of financial services. World-over, the financial services industry has made tremendous progress following the supply-leading approach (Nisture Rupa Rege 2001). Hempel and Yawitz, who have addressed the principle of shareholders’ return maximization in the context of financial institutions, start off from the premise that "Wealth maximization is the maximization of the discounted cash benefits to shareholders" (Hempel and Yawitz 1977, p.20). Hempel and Yawitz focus on maximizing shareholders’ return rather than on identifying funding sources that might maximize the profits.

The focus on the cost of equity capital avoids dealing with complicated taxation issues. With the relaxation of foreign investment in financial sector, the cost of financing is coming down and the avenues to raise the funds are increasing (Stulz 1999). The social costs weigh more than corporate financial costs, nowadays. The society expects the financial institutions to spend substantial earnings towards the society in the form of welfare measures (Marc Orlitzky 2003). If lending to high risk borrowers is potentially profitable for banks, they would have incentives to evade the interest rate controls, using some other mechanism such as charging fees (Brownbridge.M 2000).

Certainly, the banking profits are affected by the manner in which a given rate of growth in the money supply is implemented (Greenbaum 1976). The poor productivity growth was attributed to higher costs of funding because of high market rates, elimination of deposit rate ceilings, and increased competition from nonbank financial intermediaries, which increased demand for funds, reduced the supply of deposits, and increased the convenience banks provided through more branches (Bauer, Berger, and Humphrey 1993). Additionally, foreign banks are accused to cherry pick the most profitable investments only and leave most risky projects to local intermediaries, which enhances the fragility of the domestic banking sector (see Goldberg, Dages, Kinney 2000). Banks operating in a more competitive environment have higher costs than their counterparts in less competitive environments, unless the quality of services is compared (Lawrence J White 1976). It is not the cost of capital that affects the profitability but the avenue of raising it certainly affects. Taxation is seen as one of the main reason affecting profitability but taxation has allowances for costs. It is the interest rate risk associated with the source of finance that critically determines the sustainability of the profits. Previous works fail to clearly bring out the sources of funds, their interdependencies and their influence on profitability. In this paper, authors attempt focus on the interdependencies of fund sources with profit by developing a path model.


The trends in funds profile of the non-banking finance companies are shown in figure 1. The funds mobilization of asset finance companies, one among the NBFCs, has been severely affected with the enforcement of new regulations ever since 1997. The major of funds thereto, was deposit mobilized from general public. The profit arising out of core activity is positively skewed till 2000-01 except for brief spell during 1998-99, when the Reserve Bank of India enforced the new regulations.

Fig. 1 Sources of funds and their trends

Thereafter, the leasing and hire purchase activity of the companies became unattractive due to competition from banks and other financial entities (Rakesh Mohan 2004). Public deposits, though fell sharply in 1998-99, is positively skewed with the restrictions on bank exposure to NBFCs. Debentures, which are normally the attractive avenue to raise funds, has proven to be lackluster pasture for the NBFCs. The current liabilities, provisions for asset degradation and other liabilities form major sources that are utilized to maximize the operating profits.

The rise in funding cost can be attributed to the high inflation, economic growth, dramatic rise in consumption finance, and credit risk associated with (Rakesh Mohan 2004). However, the profit arising out of core operations such as leasing, hire purchase financing and investing activities is falling in the recent years. This is compensated by the diversification into non-fund based activities such as money transfer, forex operations, portfolio management, venture capital consultancy etc.

With the recent norms on restricting banks exposure to funding non-banking finance companies, the bank borrowings by them has significantly fallen down. Moreover, high cost of funds from banks and conditions imposed by the banks are forcing these companies to look for cheaper source of funds. With the fall in inflation coupled with southward movement of real rate of interest, the companies are exploring to mobilize funds in the capital, public deposits, and increased appropriation to internal accruals (Khan M.Y 2003).


Based on the previous research into funding strategies and strategies to maximize the profitability, a model capturing the mediating role of various funding options is conceived by the authors using structural equation. In the previous researches, either the working capital management strategies or the strategies for maximizing profitability or shareholders’ return are extensively studied. However, it is felt that a model would be of immense use for the academics and the industry, if it captures the important strategies available for practitioners. Therefore, an attempt in that direction is made in this paper.

In figure 2, a conceptual model for structural equation modeling technique is presented for the academics, industry manager. This model has been proved empirically in findings section. It is suggested that the academics can use this model with a fair amount of care as this designed for a particular industrial setting i.e., non-banking finance industry and is limited to those located in a single state (Tamil Nadu). This particular state is chosen for study keeping in view the economic progress made by the state. Industrial profile of the state is that it has good mix of industries ranging from automobile, textile, software, manufacturing, i.e., sugar, cement, mining and energy equipment manufacturing etc. As India is progressing in this sector, it is felt that the state having good mix of the industries will be representative of Indian industries especially non-banking finance sector. Besides, the education sector is making strident progress with new generation educational institutions in various faculties. The quality of health care offered by the hospitals places this state on medical tourism map globally. The non-banking finance companies operating in this state have product mix of automobile financing, consumer financing, capital market financing, equipment leasing and hire purchase financing, tourism financing & consultancy, education sector and health care financing. Hence, the study of non-banking finance companies operating in this sector will represent the Indian scenario on ownership pattern, product mix, consumption behavior and geography. Therefore, the findings of this study can be generalized to India as a whole.

In order to capture the interdependence of various funding strategies to maximize the profit, the authors have conceived the following model in fig 2.


The statistical technique employed is a pooled or combined data approach. Methodology described as pooled “is one in which data are elements of both time series and cross section data” (Gujarati Damodar 2004). A time series is a set of observations on the values that a variable takes at different times. Such data may be collected at regular time intervals, like annually. Cross-sectional data “is one used to collect data on all variables at one point of time” (O’Sullivan & Rassel 1999). The design is based on financial data, which are published. The secondary data is considered as the most appropriate to the measure the various funding strategies from financial variables. Only the asset finance companies (which lends atleast 60% of its funds to asset creation) in Tamil Nadu. The secondary data is the most appropriate research design as it can enable the researcher to identify the divergence in practice and collect information on financial variables over a time horizon. The information obtained from the asset finance companies can then be generalized to an entire population (Kerlinger & Lee 2000).

Data were collected by means of collection of annual reports which are secondary data in the form of published documents from various sources viz., companies’ published annual reports, statutory returns filed with Registrar of Companies, publications of associations and rating agencies from 1994-95 to 2003-04. The investment finance companies, loan finance companies, housing finance companies, and miscellaneous non-banking finance companies were excluded. Of more than 150 finance companies operating in the state, only 43 asset finance companies are registered and reporting to Reserve Bank as on 31.3.2006. Asset finance companies with public deposits of less than Rs.100 lakhs and the public sector asset finance companies were dropped from the purview.

In this paper, the above method of stratification (table 1) was applied and the companies in the sample have been chosen randomly from among the strata (table 2).

A Selection of Variables

Available literature on funding and profit maximization focus on assessing cost of capital, deposits mobilized from the public/customers and management attitude. There is not much in literature focusing on interdependence of profit with funding sources. As far as the non-banking finance companies operating in India are concerned, following funding options are alone available (Rakesh Mohan 2004, Khan M.Y 2003).

Equity financing

Debenture Issues

Bank & Institutional Borrowing

Public Deposit Mobilization

Creation of Operating Liabilities i.e., Current Liabilities, Contingent Provision, Short Term bank overdraft etc

Due to restrictions on public deposits mobilization and banks unwillingness to extend soft loans to these companies, their funding ability is severely crippled (Khan M.Y 2003). Hence, authors studied the effects of above funding methods on the profits. Unlike banks, the avenues of finance is very much limited. There are only few large sized companies operate in this sector compared to banking sector. Moreover, the midsized and smaller companies have no or limited access to equity financing, equity capital has been excluded from study for the sake of studying the interdependencies of funds across the industry. Operating assets, which are uses of the aboves funds, are included in the study to assess its impact on the profitability. It is, therefore, felt that funding can be classified as Long-Term & Short Term funds, Operating Liablities. Based on this classification, the variables are included in structural equation model in fig 3.

B Modelling Techniques


The main study used structural equation modeling (SEM) because of two advantages: “(1) estimation of multiple and interrelated dependence relationships, and (2) the ability to represent unobserved concepts in these relationships and account for measurement error in the estimation process” (Hair et al.,1998, p.584). In other words, a series of split but independent multiple regressions were simultaneously estimated by SEM. Therefore, the direct and indirect effects were identified (Tate 1998). However, a series of separate multiple regressions had to be established based on “theory, a priori experience, and the research objectives to distinguish which independent variables predict each dependent variable” (Hair et al, 1998, p.584). In addition, because SEM considers a measurement error, the reliability of the predictor variable was improved. Structural Equation Modeling was conducted with AMOS 16.0 (an upgraded version of AMOS 7.0). AMOS 7.0 (Arbuckle and Wothke, 2006), a computer program for formulating, fitting and testing structural equation models to observed data was used for SEM and the data preparation was conducted with SPSS 15.0.

Linear structural equation models (SEMs) are widely used in sociology, econometrics, management, biology, and other sciences. A SEM (without free parameters has two parts: a probability distribution (in the Normal case specified by a set of linear structural equations and a covariance matrix among the “error” or “disturbance” terms), and an associated path diagram corresponding to the casual relations among variables specified by the structural equations and the correlations among the error terms. It is often thought that the path diagram is nothing more than a heuristic device for illustrating the assumptions of the model. However, in this research, the researcher will show how path diagrams can be used to solve a number of complex problems in structural equation modeling.

Structural equation models with latent variables (SEM) are more and more often used to analyze relationships among variables in marketing and consumer research (refer Bollen 1989, Schumacker & Lomax 1996, or Batisa-Foguet & Coenders 2000, for an introduction and Bagozzi 1994 for applications to marketing research). Some reasons for the widespread use of these models are their parsimony (they belong to the family of linear models), their ability to model complex systems (where simultaneous and reciprocal relationships may be present, such as relationship between profitability and funding strategies). As is usually recommended, a confirmatory factor analysis (CFA) model is first specified to account for the measurement of relationships from latent to observable variables.In our case, the bank borrowings (BB), debentures (DEP), operating assets (OA), public deposit (PD), other operating liabilities such as current liabilities, surplus available, internal accruals in the form of provisions (OPL) are the observable variables and short-term loans (STL) is the mediator on the endogenous variable, core operating profit before tax (COPBT).


The authors developed following path model to study the mediating effects of funding strategies and profit maximization and their interdependencies. After specifying the model, the hypotheses were formulated for empirical analysis and testing.

Fig.3 Path Model based on Conceptual


Hypothesis are numbered from 1 to 11 and denoted by notation H as H1 to H11 in the above figure. Alternative hypothesis (Null Hypothesis) for all the above hypotheses is that the variables under study have no significant influence or no relationship with each other.

H1: Bank borrowings mediated through short-term liabilities significantly influence Core

Operating Profit.

H2: Debentures mediated through short-term liabilities positively influence Core Operating Profit.

H3: Core Operating Profit is significantly influenced by Operating Assets upon being mediated by

Short-term liabilities.

H4: Public Deposits mediated through short-term liabilities significantly influence Core

Operating Profit.

H5: Operating Liabilities has significant impact on Core Operating Profit when mediated through

Short-term liabilities.

H6: Bank borrowings significantly influence Core Operating Profit.

H7: Debentures can positively and significantly influence Core Operating Profit.

H8: Operating Assets have individual and significant influence on Core Operating Profit.

H9: Public deposits positive relationship with Core Operating Profit.

H10: Operating liabilities significantly influence over Core Operating Profit

H11: The mediator short-term liabilities has a strong influence on Core Operating Profit and its

mediating effects with other variables is significant.

These hypotheses are to be tested using Structural Equation Model and its regression coefficients and is carried out in the succeeding section.


A. Structural Equation Model for Funding Strategies (AFC-FRS)

In hierarchical regression, the predictor variables are entered in sets of variables according to a pre-determined order that may infer some causal or potentially mediating relationships between the predictors and the dependent variables (Francis, 2003). Such situations are frequently of interest in the social sciences. The logic involved in hypothesizing mediating relationships is that “the independent variable influences the mediator which, in turn, influences the outcome” (Holmbeck, 1997). However, an important pre-condition for examining mediated relationships is that the independent variable is significantly associated with the dependent variable prior to testing any model for mediating variables (Holmbeck, 1997). Of interest is the extent to which the introduction of the hypothesized mediating variable reduces the magnitude of any direct influence of the independent variable on the dependent variable. Hence, in this paper, the hierarchical regression model for AFC-FRS is tested.

Fig.4 Regression Model for AFC-FRS


H1: Bank borrowings with r2 of 0.15 mediated through short-term liabilities with r2 of

0.31 positively influence Core Operating Profit. Hypothesis is accepted.

H2: Debentures with r2 of 0.16 mediated through short-term liabilities with r2 of 0.31

positively influence Core Operating Profit. Hypothesis is accepted.

H3: Operating Assets with r2 of 0.17 mediated through short-term liabilities with r2 of

0.31 positively influence Core Operating Profit. Hypothesis is accepted.

H4: Public deposits with r2 of 0.00 mediated through short-term liabilities with r2 of

0.31 has no bearning on the Core Operating Profit. Hypothesis is rejected.

H5: Operating liabilities with r2 of -0.34 mediated through short-term liabilities with r2 of

0.31 has no influence over Core Operating Profit. Hypothesis is rejected.

H6: Bank borrowings has very low insignificant influence over Core operating profit

directly with r2 of 0.07. Hypothesis is accepted but trivial for the study.

H7: Debentures has very low insignificant influence over Core operating profit directly

with r2 of 0.09. Hypothesis is accepted but not considered due to insignificance

H8: Operating Assets have very low insignificant influence over Core operating profit

directly with r2 of 0.02. Hypothesis is accepted but trivial for the study.

H9: Public deposits have negative influence over Core operating profit directly with r2 of

0.05. Hypothesis is accepted.

H10: Operating liabilities have very significant influence over Core operating profit

directly with r2 of 0.15 compared to other variables. Hypothesis is accepted.

H11: Mediator short-term liabilities has a r2 of 0.31with the outcome Core Operating

profit and its mediating effect alongwith other variables with COPBT is confirmed.

The regression analysis reveals that though bank borrowings, debentures, operating assets and public deposits have insignificant impact on COPBT but have considerable influence when mediated through short-term liabilities except public deposits. However, operating liabilities have more pronounced negative influence when mediated through short-term liabilities than its direct considerable positive influence over COPBT. Public deposit does play any significant role in this mediated model. Because operating liabilities carry higher costs and the short-term liabilities too carry high cost. But due to quite comfortable longevity in its retirement, ST liabilities are less costly than operating liabilities. Operating liabilities are very fluid liabilities hence they have far reaching influence over profitability. Public deposit mobilization is severely constrained with requirement of net owned funds and credit rating. Therefore, funding through this avenue is very limited. Hence, the short-term liabilities have mediating effects on profit maximization. Asset Finance Companies may concentrate on raising fair amount of Debentures, Public Deposits and avoid creating operating liabilities. As seen from Appendix B, Confirmatory factor analysis reveals that Chi-square 2 = 22.679 @ p =0.01, Root Mean Square Error of Approximation (RMSEA) = 0.075, Goodness of Fit (GFI) = 0.989, Comparative Fit Index = 0.999 and Normed Fit Index = 0.999. Though RMSEA of 0.5 is considered good fit, RMSEA of 0.075 is a good fit considering sample size being quite low. Other fit indexes are within the acceptable range. Therefore, the regression model is a good model within structural equation modeling environment.

B. Posterior Diagnostic Plots of AFC-FRS Mediated Model

Fig 5 Posterior Frequency Polygon Distribution of CoreOPBT & STL

To check the convergence of Bayesian MCMC method the posterior diagnostic plots are analyzed. The figure 5 shows that the posterior frequency polygon of the distribution of the parameters across the 85,000 samples. The Bayesian MCMC is diagnostic plot in figure 5 reveals that for all the figures the normality is achieved, so the structural equation model fit is accurately estimated.

Fig 6 Posterior Distribution of Autocorrelation of CoreOPBT & STL

The trace plot also called time series plot shows the sampled values of a parameter over time. This plot helps to judge how quickly the MCMC procedure converges in distribution. The following figure (Figure.7) shows the trace plot of AFC-FRS model for the mediated factor STL across 85,000 samples. The figure exhibits rapid up and down variation with no long-term trends or drifts.

Fig 7 Posterior Trace Plot of AFC-FRS Mediated Model (STL-COPBT)

Hence to visualize the relationships among pairs of parameters in three-dimensional the following figures (figure 8 to figure 9) provide bivariate marginal posterior plots of the AFC-FRS model for the mediated factor short-term loans with other dimensions across 50000 samples. From these figures, it reveals that the three-dimensional surface plots also signify the interrelationship between the mediating variable short-term loans with the core operating profitability.

Fig 8 Posterior Bivariate Plot of AFC-FRS Mediated Model (STL-COPBT)

Fig 9 Posterior Bivariate Confidence Plot of AFC-FRS Mediated Model (STL-COPBT)

In the above figure, the confidence interval changes from dark to light region which indicates the confidence level from 10%, 5% and 1% respectively.

With regard to conceived model AFC-FRS, while many management scientists are most familiar with the estimation of these models using software that analyses (e.g., LISREL, AMOS), this paper adopt Bayesian approach for estimation and inference in AMOS 16.0 environment. Since, it offers numerous methodological and substantive advantages over alternative approaches. The table in Appendix A shows the Bayesian convergence distribution of AFC-FRS mediated regression model. In this paper, the authors have adopted the procedure of assessing convergence of MCMC (Markov Chain Monte Carlo) algorithm of maximum likelihood. To estimate the MCMC convergence, the authors adopted two methods namely, convergence in distribution, convergence of posterior summaries. The values of posterior mean accurately estimate the AFC-FRS mediated SEM model. From the above table the highest value of Convergence Statistics (C.S) is 1.000, which is less than the 1.002 conservative measures.


The results indicate that the mediatory role of short-term liabilities is quite insignificant and bank borrowings, operating assets and debentures are associated significantly with the mediator. Asset finance companies shall raise public deposits, debentures and bank borrowings to sustain the operating profit. However, the borrowings in short duration are a key fund raising strategy. According to Harker and Zenios (1998), financial performance of an institution – observable but non-actionable – can be affected by its performance along the axis of service delivery and financial intermediation. In attempting to assess the effect of ownership structure on firm performance, is it more sensible to look at an estimate of what management has accomplished or at an estimate of what management will accomplish? The second difference is in who is actually measuring profit. For the accounting profit rate, this is the accountant constrained by standards set by his profession (Demsetz, Villalonga 2001).

In comparing bank performance among banks from different countries, especially across developed and developing countries, considerable care must be taken to allow for differences in product mix, capital structure, accounting conventions and especially inflation rates before drawing firm conclusions (Vittas Dimitri 1991). The results suggest that strategy is combining various funding strategy with short-term funding one. Care must be taken in formulating funding strategy whether to go for short-term loans or raise public deposits.

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