Modeling asset allocation and liability composition for Indian banks

2014 ◽  
Vol 40 (7) ◽  
pp. 700-723 ◽  
Author(s):  
P.K. Viswanathan ◽  
M. Ranganatham ◽  
G. Balasubramanian
Keyword(s):  
Sensitivity Analysis ◽  
Goal Programming ◽  
Asset Allocation ◽  
Banking Sector ◽  
Programming Model ◽  
Balance Sheet ◽  
Model Framework ◽  
Content Type ◽  
Goal Programming Model ◽  
Indian Banks

Purpose – Asset liability management is a multi-dimensional set of activities. Against this backdrop, the purpose of this paper is to build a goal programming model for optimally determining the asset allocation and liability composition for Indian Banks. Design/methodology/approach – The conceptual model framework has been developed and then tested for four banks that typically represent the Indian banking sector. Published balance sheet data were used for the model that span over 1995-2009. The veracity of the model has been tested in terms of its ability to project the optimum asset allocation and liability composition for the year 2010. Findings – The model has been able to generate the optimum asset and liability mix that meets the goals set on the key drivers. The solution provided is realistic and compatible with the actual figures. Sensitivity analysis including current and savings account and interest rate changes has been successfully performed to study impact they cause on profitability. Research limitations/implications – The model provides an overall approach to asset allocation and liability composition based on past data reflecting the preferences and priorities of the banks with regard to their outlook on setting targets. This may change. The variables like return and risk are stochastic in nature. Practical implications – The model demonstrated in this paper would be useful to the policy makers in any bank for decision support and planning in view of its ability to incorporate a large number of constraints. Changes in profit could be instantaneously captured through sensitivity analysis. Originality/value – The goal programming model used here is invariant to the type of bank and year of consideration.

Stressed assets, off-balance sheet business activities and performance of Indian banking sector: a DEA double bootstrap approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Shahid Zaman ◽  
Anup Kumar Bhandari
Keyword(s):  
Technical Efficiency ◽  
Commercial Banks ◽  
Banking Sector ◽  
Balance Sheet ◽  
Capital Adequacy ◽  
Bank Efficiency ◽  
Content Type ◽  
Bootstrap Approach ◽  
Double Bootstrap ◽  
Indian Banks

Purpose This paper examines the technical efficiency (TE) of Indian commercial banks during 1998–2015. Design/methodology/approach This study uses mathematical programming-based data envelopment analysis (DEA) methodology to measure technical efficiency of Indian banks. Further, Simar and Wilson (2007) double bootstrap procedure is applied to examine the determinants of efficiency of the Indian banks, by examining the effects of various bank specific and other contextual variables. Findings The results indicate substantial upward bias in the conventional efficiency estimates of the Indian commercial banks. Needless to note, such upward bias is consistent with the theoretical postulates. The bootstrapped regression results show that increasing capital adequacy ratio is positively associated with bank efficiency. The popular belief that non-performing assets have a dampening effect on performance of banks is validated. Among others, ownership category is observed to be an important determining factor of bank efficiency. Specifically, state-owned banks (SOBs) are relatively lagging behind the foreign banks. Moreover, larger banks are observed to have a significantly higher level of efficiency, therefore, recent official policy initiatives toward consolidation of SOBs are validated. Originality/value As this study uses Simar and Wilson (2007) bootstrap approach, it enables the authors to have an estimate of the extent of bias in the traditional DEA TE scores. It also helps us drawing consistent inferences by rectifying the problem of serial correlation in the conventional second stage regression in this regard.

scholarly journals Ranking the Performance of Universities: The Role of Sustainability

2021 ◽  
Vol 13 (23) ◽  
pp. 13286
Author(s):  
Christoph Burmann ◽  
Fernando García ◽  
Francisco Guijarro ◽  
Javier Oliver
Keyword(s):  
Higher Education ◽  
Sensitivity Analysis ◽  
Goal Programming ◽  
Programming Model ◽  
Decision Makers ◽  
University Rankings ◽  
Teaching And Research ◽  
Sustainability Criteria ◽  
Goal Programming Model

University rankings assess the performance of universities in various fields and aggregate that performance into a single value. In this way, the aggregate performance of universities can be easily compared. The importance of rankings is evident, as they often guide the policy of Higher Education Institutions. The most prestigious multi-criteria rankings use indicators related to teaching and research. However, many stakeholders are now demanding a greater commitment to sustainable development from universities, and it is therefore necessary to include sustainability criteria in university rankings. The development of multi-criteria rankings is subject to numerous criticisms, including the subjectivity of the decision makers when assigning weights to the criteria. In this paper we propose a methodology based on goal programming that allows objective, transparent and reproducible weighting of the criteria. Moreover, it avoids the problems associated with the existence of correlated criteria. The methodology is applied to a sample of 718 universities, using 11 criteria obtained from two prestigious university rankings covering sustainability, teaching and research. A sensitivity analysis is carried out to assess the robustness of the results obtained. This analysis shows how the weights of the criteria and the universities’ rank change depending on the λ parameter of the goal programming model, which is the only parameter set by the decision maker.

scholarly journals MATHEMATICAL MODELING OF ASSET LIABILITY MANAGEMENT IN BANKS USING GOAL PROGRAMMING AND AHP

2020 ◽  
Vol 4 (4) ◽  
pp. 1-19
Author(s):  
Jyoti Tanwar ◽  
Arun Kumar Vaish ◽  
N V M Rao
Keyword(s):  
Goal Programming ◽  
Banking Sector ◽  
Programming Model ◽  
Balance Sheet ◽  
Annual Reports ◽  
Primary Data ◽  
Asset Liability Management ◽  
Liability Management ◽  
Priority Weights ◽  
Gp Model

Asset Liability Management has gained popularity in the banking sector. Earlier banks focused on asset allocation, but now the management of assets and liabilities is equally essential. Asset liability management targets the optimum distribution of funds in assets and managing liabilities so that banks can earn higher profits and minimize risk. In this paper, the optimization of assets and liabilities of Indian banks has been concentrated using mathematical models. Combining the Analytical Hierarchy Process (AHP) and Goal Programming (GP) model has been used to solve the optimization problem. AHP is a multi-criteria decision-making approach for deriving priority weights. Goal Programming is a linear programming model to solve complex issues having multiple objectives. In this paper, the primary data gathered from Bank senior managers have been analyzed using the AHP approach to derive weights for criteria. These weights are assigned to goals in goal programming to prioritize the goals. Secondary data on OBC bank is used in goal programming from 2010-2019 collected from OBC bank's annual reports and RBI websites. The findings show that OBC bank has the scope of improving its assets and liabilities position to increase its profit and minimize the risk. The model generates an optimum balance sheet that achieves the set goals and satisfies all the statutory and planning constraints. The same model can be useful for scheduled commercial banks in India with modifications concerning banks' targets and controls. The model developed in this paper is helpful for bank managers in planning and forecasting. AHP and GP's combined approach is unique in this paper, which uses experts' knowledge and applies it in the model. The model is created on the bank's realistic goals and constraints after carefully considering the issues faced by bank officials. The paper is limited to the Indian Banking system as other countries have different balance sheet structures and constraints.

Developing a fuzzy goal programming model for health, safety and environment risks based on hybrid fuzzy FMEA-VIKOR method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Masoumeh Nabizadeh ◽  
Mohammad Khalilzadeh ◽  
Sadoullah Ebrahimnejad ◽  
Mohammad Javad Ershadi
Keyword(s):  
Goal Programming ◽  
Programming Model ◽  
Fuzzy Goal Programming ◽  
Time Cost ◽  
Vikor Method ◽  
Content Type ◽  
Health Safety ◽  
Goal Programming Model ◽  
Fuzzy Goal ◽  
Safety And Environment

Purpose The activities of the oil industry from discovery to distribution of oil products have adverse effects on human and environment. Thus, the companies that are active in this industry should identify and manage their risks. The purpose of this paper is to prioritize the identified risks based on different measures such as cost, occurrence, etc. Then, selecting the most important corrective actions using goal-programming approach is another objective of this study. Design/methodology/approach To identify the health, safety and environment (HSE) risks, the Fuzzy Delphi method was used. The failure mode and effects analysis (FMEA) and fuzzy Vlse Kriterijumsk Optimizacija Kompromisno Resenje (VIKOR) methods covering the deficits of FMEA were used to rank the HSE risks. Unlike similar researches, in the proposed FMEA–VIKOR method, the risk priority number was not calculated. In addition to severity, occurrence and detection, the parameters such as time, cost and quality, being considered for ranking the risks, were weighted by the Eigenvector method. Then, a fuzzy goal-programming model was developed for determining the best solutions of risk response. Findings The research findings indicated that the most important risks include fire and blast because of tank and pipeline, leakage of connections and pipelines and industrial waste. Also, the most important risk responses include using and strengthening the alarm and fire extinguishing systems, using fiberglass tanks to prevent pipeline corrosion, using modern technology to have more efficient oil refining. Originality/value The main contribution of this paper is using hybrid approach of FMEA–VIKOR for risk ranking by considering different measures such as time, cost and quality besides severity, occurrence and detection. Providing a fuzzy goal-programming framework for determining the main risk responses is another value for this research.

The intellectual capital performance of the Indian banking sector

2007 ◽  
Vol 8 (1) ◽  
pp. 96-123 ◽  
Author(s):  
G. Barathi Kamath
Keyword(s):  
Intellectual Capital ◽  
Banking Sector ◽  
Measurement Techniques ◽  
Balance Sheet ◽  
Value Added ◽  
Policy Implications ◽  
Specific Reference ◽  
Content Type ◽  
Domestic Banks ◽  
Indian Banks

PurposeThe paper seeks to estimate and analyze the Value Added Intellectual Coefficient (VAIC™) for measuring the value‐based performance of the Indian banking sector for a period of five years from 2000 to 2004.Design/methodology/approachAnnual reports, especially the profit/loss account and balance‐sheet of the banks concerned for the relevant years, were used to obtain the data. A review is conducted of the international literature on intellectual capital with specific reference to literature that reviews measurement techniques and tools, and the VAIC™ method is applied in order to analyze the data of Indian banks for the five‐year period. The intellectual or human capital (HC) and physical capital (CA) of the Indian banking sector is analysed and their impact on the banks' value‐based performance is discussed.FindingsThe study confirms the existence of vast differences in the performance of Indian banks in different segments, and there is also an improvement in the overall performance over the study period. There is an evident bias in favour of the performance of foreign banks compared with domestic banks.Research limitations/implicationsAll 98 scheduled commercial banks are studied as per the information provided by the Reserve Bank of India (RBI)/India's Apex bank. Regional rural banks (RRBs), a segment of the indian banking sector, are not dealt with in the study since their number is large (more than 200), but they contribute only 3 percent of the market of Indian banks. This paper is a landmark in Indian banking history as it approaches performance measurement with a new dimension.Practical implicationsThe paper has strong theoretical foundations, which have a proven record and applications. The methodology adopted has been research tested. Domestic banks in India are provided with a new dimension to understand and evaluate their performance and benchmark it with global standards. The paper also has policy implications, as it reflects the lop‐sided growth of a few sections in the Indian banking segment.Originality/valueThe paper represents a pioneering and seminal attempt to understand the implications of the business performance of the Indian banking sector from an intellectual resource perspective.

scholarly journals A Multicriteria Goal Programming Model for Ranking Universities

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 459
Author(s):  
Fernando García ◽  
Francisco Guijarro ◽  
Javier Oliver
Keyword(s):  
Goal Programming ◽  
Programming Model ◽  
Decision Makers ◽  
University Rankings ◽  
Central Tendency ◽  
Performance Variables ◽  
World University Rankings ◽  
Goal Programming Model ◽  
Objective Quantification

This paper proposes the use of a goal programming model for the objective ranking of universities. This methodology has been successfully used in other areas to analyze the performance of firms by focusing on two opposite approaches: (a) one favouring those performance variables that are aligned with the central tendency of the majority of the variables used in the measurement of the performance, and (b) an alternative one that favours those different, singular, or independent performance variables. Our results are compared with the ranking proposed by two popular World University Rankings, and some insightful differences are outlined. We show how some top-performing universities occupy the best positions regardless of the approach followed by the goal programming model, hence confirming their leadership. In addition, our proposal allows for an objective quantification of the importance of each variable in the performance of universities, which could be of great interest to decision-makers.

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