Peer Group Analysis Research Articles

Bank crisis resolution in India

PurposeThe academic literature has examined why bank runs happen based on the work of 2022 Nobel Prize-winning economists Diamond and Dybvig. They have found the source of banking/financial crisis in terms of mismatch between liabilities (deposits being short term and savers wanting to short-term access to their money) and assets (long term and illiquid). The Lakshmi Vilas Bank (LVB) crisis intensified when it came under Prompt Corrective Action (PCA) of the Reserve Bank of India (RBI). This situation provides the opportunity to study whether the elements embodied in the theoretical models like Diamond and Dybvig hold true for LVB crisis. This study aims to examine the reasons for the demise of LVB in India using DuPont financial model, peer group analysis and time series structural break in crucial financial parameters.Design/methodology/approachThe study examines the reason for insolvency of LVB using financial ratios, financial models (DuPont), financial distress model (Z-score) and asset-liability management. The study also adopts univariate structural break models using quarterly financial data covering the key financial measures used in the RBI’s PCA framework.FindingsLVB crisis is like Diamond–Dybvig model, in the sense, savers requiring short-term access to their money (liquidity for their deposits) on the information of high non-performing assets, which further deteriorates the illiquid nature of loan portfolio (assets) of banks. The study finds its profit margin (net interest margin and non-interest margin) and managerial efficiency had started deteriorating since 2018. The study finds that LVB’s main weakness lies in its limited credit appraisal ability, its monitoring and weak internal controls. Lending to sensitive sectors (like real estate, capital markets and commodities) and exposure to large business groups also contributed to its weakness. The study also finds huge, elevated asset-liability mismatch, especially in the short-term maturity buckets. Using univariate econometric time series model, the study also confirms financial weakness being evident much earlier than the time when resolution was undertaken by the RBI through PCA.Research limitations/implicationsThe study has implications for analysing and monitoring financial distress of banks. The study also has implications for devising banking regulation and supervision.Originality/valueThe study brings in a perspective of the banking regulations using the application of PCA framework on a listed private sector bank. The authors combine an accounting ratio model and combine risk measures that could identify the incipient risks in a bank. The authors believe this will help in refinement of banking regulations and better monitoring mechanisms.

EXPERIENCES OF INNER STRENGTH AT THE TIME OF MILD COGNITIVE IMPAIRMENT DIAGNOSIS

Abstract Inner strength is a person’s internal process of moving through challenging circumstances, such as receiving a diagnosis of Mild Cognitive Impairment (MCI). This study describes experiences of inner strength using qualitative methodologies to identify themes within semi-structured dyadic and individual interviews with persons diagnosed with MCI within 12 months at a Memory Center and their care partners. We analyzed data in NVivo using reflexive thematic analysis methods. Trustworthiness was maintained through vetted interview guides, verbatim transcription, field notes, peer group analysis, and audit trails. One overarching theme and three subthemes explained inner strength. An overarching theme, Finding Ways to Live with It, described how participants live within the circumstances of MCI. Three subthemes were Defining Strength by Recalling the Past, Seeking Relief and Dwelling in It, and Finding Purpose & Meaning. Implications include supporting inner strength at the time of MCI diagnosis through reminiscence therapy and meaning making interventions.

Healthcare fraud detection using primitive sub peer group analysis

AbstractHealthcare fraud is a significant problem greatly affecting the quality of healthcare services. Manual auditing of insurance claims extends to the delay in finding fraudulent behaviors causing huge financial loss and also putting the patients' health conditions at risk. Since the past decade, the automation of fraud detection using machine learning techniques has become a prominent research topic. Several automated fraud detection systems using machine learning techniques have been proposed so far. However, developing a healthcare fraud detection system that is adaptive to the systematic changes is still missing. Therefore, in this article, we develop primitive sub peer group analysis (PSPGA) for identifying the suspicious behaviors in health insurance claims. PSPGA is inspired by peer group analysis, a popular unsupervised learning technique, which identifies suspicious behaviors based on local pattern analysis. PSPGA distinguishes between the concept drifts and the sudden drifts and flags the sudden drifts as fraudulent. Moreover, PSPGA makes the fraud detection system adaptive to the concept drifts by considering the updates for peer groups over time.

Costs of capital under credit risk

Credit risk analysis represents a growing field in financial research since decades. However, in company valuation – to be more precise, in cost of capital computations – credit risk is merely taken into consideration at the level of the debt beta approach. Our paper proves that applications of the debt beta approach suffer from unrealistic assumptions. As an advantageous approach, we develop an alternative framework to determine costs of capital based on Merton’s model. We present (quasi-) analytic formulas for costs of equity and debt which are consistent with Modigliani-Miller theory in continuous-time and discrete-time settings without taxes. Our framework is superior to the debt beta approach regarding the quantity and quality of required data in peer group analysis. Since equity and debt are represented by options in Merton’s model, we compute expected option rates of return without resorting to betas. Thereby, our paper also contributes to the option pricing literature.

AppFA: A Novel Approach to Detect Malicious Android Applications on the Network

We propose AppFA, an Application Flow Analysis approach, to detect malicious Android applications (simply apps) on the network. Unlike most of the existing work, AppFA does not need to install programs on mobile devices or modify mobile operating systems to extract detection features. Besides, it is able to handle encrypted network traffic. Specifically, we propose a constrained clustering algorithm to classify apps network traffic, and use Kernel Principal Component Analysis to build their network behavior profiles. After that, peer group analysis is explored to detect malicious apps by comparing apps’ network behavior profiles with the historical data and the profiles of their selected peer groups. These steps can be repeated every several minutes to meet the requirement of online detection. We have implemented AppFA and tested it with a public dataset. The experimental results show that AppFA can cluster apps network traffic efficiently and detect malicious Android apps with high accuracy and low false positive rate. We have also tested the performance of AppFA from the computational time standpoint.

E&P Notes (March 2018)

E&P Notes (March 2018)

Electric load shape benchmarking for small- and medium-sized commercial buildings

Small- and medium-sized commercial buildings owners and utility managers often look for opportunities for energy cost savings through energy efficiency and energy waste minimization. However, they currently lack easy access to low-cost tools that help interpret the massive amount of data needed to improve understanding of their energy use behaviors. Benchmarking is one of the techniques used in energy audits to identify which buildings are priorities for an energy analysis. Traditional energy performance indicators, such as the energy use intensity (annual energy per unit of floor area), consider only the total annual energy consumption, lacking consideration of the fluctuation of energy use behavior over time, which reveals the time of use information and represents distinct energy use behaviors during different time spans. To fill the gap, this study developed a general statistical method using 24-h electric load shape benchmarking to compare a building or business/tenant space against peers. Specifically, the study developed new forms of benchmarking metrics and data analysis methods to infer the energy performance of a building based on its load shape. We first performed a data experiment with collected smart meter data using over 2000small- and medium-sized businesses in California. We then conducted a cluster analysis of the source data, and determined and interpreted the load shape features and parameters with peer group analysis. Finally, we implemented the load shape benchmarking feature in an open-access web-based toolkit (the Commercial Building Energy Saver) to provide straightforward and practical recommendations to users. The analysis techniques were generic and flexible for future datasets of other building types and in other utility territories.

Implementasi Model Pembelajaran Multikultural Untuk Meningkatkan Kompetensi Mahasiswa (Studi Pada Mahasiswa PGMI di IAIN Salatiga)

Theoretically, multicultural education is a concept of education that provides equal opportunity regardless of gender, class, ethnic, racial, religious, and cultural background of learners. The learning model is carried out by stages: self-exploration studies, exploratory presentation, peer group analysis, expert opinion, and reflection. This study aims to improve the competence of mastery of IPS educational materials through multicultural learning model. The research findings show that the implementation of multicultural learning model can improve the competence of mastery of IPS Education materials for PGMI students at IAIN Salatiga. This is evidenced in the average class pre cycle activity of 60.25; Cycle I of 77.87 and in the second cycle of 82.22. Based on the analysis it is concluded that the implication of multicultural learning model can significantly improve student competence.

“God Hates Your Feelings”: Neutralizing Emotional Deviance within the Westboro Baptist Church

Drawing on individual and peer-group interviews, participant observations, and analysis of media content, we examine the habitual emotional deviance and neutralization techniques employed by the Westboro Baptist Church. We argue that their notoriety is attributed to their persistent, intentional, and unapologetic disregard for cultural feeling rules including incongruous emotional responses to events, fusion of culturally oppositional sentiments, and denigration of “sacred” groups and symbols. Interviews reveal that while claiming to embrace their deviant status, members engage in common neutralization techniques including denial of responsibility for the pain and discomfort they cause, appealing to higher loyalties, and denial of victimization.

Costs of Capital under Credit Risk

In cost-of-capital computations, credit risk is only taken into consideration at the level of the debt beta approach. We show that applications of the debt beta approach in company valuation suffer from unrealistic assumptions about the market index and the cost of debt. As an advantageous approach, we present (quasi-) analytic formulas for costs of equity and debt based on Merton’s model in different settings. In our approach, we integrate credit risk in cost-of-equity and debt calculations to receive the credit risk-adjusted weighted average cost of capital. In addition, we compare both the quantity and the quality of the required data in a peer group analysis of our approach with the data requirements of the debt beta approach. Further, we discuss the valuation errors that result from the debt beta approach’s assumptions regarding the cost of debt. A valuation of Apple Inc as if it were not publicly traded illustrates the procedures of our approach.

MODEL PEMBELAJARAN PENDIDIKAN KARAKTER DENGAN PENDEKATAN MULTIKULTURAL DALAM PENDIDIKAN KEWARGANEGARAAN DI PERGURUAN TINGGI

Abstract: This research aims to develop a learning model character education with a multicultural approach used in Civic Education. The resulting learning model is poured in the form of modules and visualized in the form of instructional videos. Framework of a prototype model of learning by taking into account the competence of the subjects that must be owned by the students, faculty compe-tence in multicultural approach, the cultural background of students, and the characteristics of learning the nuances of multicultural material. Strategies that can be used, among others: strategies and learning activities are combined with the concept attainment strategy, the strategy value analy-sis, and strategies of social analysis. Drafting of the learning can be done through five main stages, namely content analysis, the analysis of cultural background, mapping materials, organizing materi-als, and then poured in Civic Education learning format. In the implementation of character education learning model with a multicultural approach can be done through the following phases: Exploration Studies, Presentations, Peer Group Analysis, Expert Opinion, and Reflections and Recommenda-tions. Keywords: character education, multicultural, and civic education

Big-data for building energy performance: Lessons from assembling a very large national database of building energy use

Building energy data has been used for decades to understand energy flows in buildings and plan for future energy demand. Recent market, technology and policy drivers have resulted in widespread data collection by stakeholders across the buildings industry. Consolidation of independently collected and maintained datasets presents a cost-effective opportunity to build a database of unprecedented size. Applications of the data include peer group analysis to evaluate building performance, and data-driven algorithms that use empirical data to estimate energy savings associated with building retrofits. This paper discusses technical considerations in compiling such a database using the DOE Buildings Performance Database (BPD) as a case study. We gathered data on over 750,000 residential and commercial buildings. We describe the process and challenges of mapping and cleansing data from disparate sources. We analyze the distributions of buildings in the BPD relative to the Commercial Building Energy Consumption Survey (CBECS) and Residential Energy Consumption Survey (RECS), evaluating peer groups of buildings that are well or poorly represented, and discussing how differences in the distributions of the three datasets impact use-cases of the data. Finally, we discuss the usefulness and limitations of the current dataset and the outlook for increasing its size and applications.

Financial Crisis Prediction Capability of Financial Ratios

Bankruptcy of a business firm is an event which results substantial losses to creditors and stockholders. A model which is capable of predicting an upcoming business failure will serve as a very useful tool to reduce such losses by providing warning to the interested parties. This was the main motivation for Beaver (1966) and Altman (1968) to construct bankruptcy prediction models based on the financial data (Deakin 1972). This research study also initiated with a great interest on this subject to investigate the predictive capability of financial ratios for forecasting of corporate distress and bankruptcy events. The current global financial climate demands even the best international companies to constantly monitor their financial situation and their related companies with which they cooperate. Globalization process has delivered a complex network of relationships in the business environment. Due to increase in complexity of related business environment, forecasting the financial health of companies nowadays became increasingly important and worthwhile to analyse (Korol 2013). Bankruptcy is a continuous process, which can be distinguished into several stages, starting from the emergence of the first signs of financial crisis, through blindness and ignorance towards the financial and nonfinancial symptoms of crisis in a firm, to inappropriate activities that lead to the final phase of the crisis, which is bankruptcy. The Bankruptcy process cycle may take up to 5–6 years which is not a sudden phenomenon and impossible to predict, however the earlier warning signals can be detected and corrective measures may avoid the ultimate bankruptcy event depending on the preparation and reactions of the management to tackle the bankruptcy (Korol 2013). Due to the recent worldwide corporate financial crisis the need to reform the existing financial architecture has been intensified. Objective of business crisis prediction is to build models that can read the risk factors from the past observations and evaluate business crisis risk of companies with a much broader scope (Lin et al. 2011). Ozkan cited in Lin et al. 2011 mentioned that financial indicators has been reviewed by number of researchers as a major basis for predicting financial distress and some common methodologies including peer group analysis, comprehensive risk assessment systems, and statistical and econometric analysis. Premachandra (2009) argued that bankruptcy prediction is important because corporate failure imposes significant direct and indirect costs on stakeholders. Warner cited in Premachandra (2009), evidence suggests that direct bankruptcy costs (such as court costs, lawyers and accountants fees) may be as low as 5%, or (Altman cited in Premachandra 2009) can shoot up to 28% when both direct and indirect costs (such as lost sales, lost profits, higher cost of credit, inability to issue new securities and lost investment opportunities) are considered. Therefore the early detection of potential bankruptcy is very important due to corporate decision makers make their decisions in a world of dynamic technology development, imperfect knowledge and uncertainty (Premachandra 2009). Niewrzedowski cited in Korol (2013) indicated that as per statistical analysis by Huler-Hermes, the number of potential bankruptcy has been increased in USA by 54%, in Spain by 118% and in the UK by 56%. Therefore the importance of early warning of potential bankruptcy has been increased along with the overall increase of bankruptcy risk in companies around the world.

Peer group analysis for introducing weather derivatives for a city

Due to changes in global climate, infrastructure damage caused by extreme weather has been increasing worldwide, and Korea also has not escaped such damage. Countries such as the United States, Japan, Canada and Australia have introduced weather insurance and derivatives in order to hedge potential risks of damage caused by climate change. Korea has also introduced weather insurance, but weather derivatives have not yet been introduced. In this paper, we do the research for introducing weather derivatives for Seoul, Korea. Weather derivatives need to reflect the climatic conditions of the region. In this paper, we apply a peer group analysis to introduce weather derivatives for Seoul, Korea, based on the climatic conditions of the region. In addition, we propose a simple pricing method for the weather derivative using both historical weather data of Seoul and its peer cities. Our study results can help other cities who want to introduce weather derivatives for them.

Model Pengelolaan Teaching Factory Sekolah Menengah Kejuruan

This study aims to identify and develop management models Factory Teaching Vocational School through a series of tests and validation of the model with the approach of reasearch and Development. Teaching Factory Management Models at Vocational Schools is a concept that combines learning management approach learning Competency Based Training (CBT) and Production Based Training (PBT) in the real atmosphere so that the process of achieving competence or skill skills life skills (life skills) in accordance with the standards of graduate work in the industrial world. In particular, this study aims to formulate design management model development Teaching Factory Vocational School. To achieve the research objectives above using the approach of Research and Development, through preliminary studies, design development models, testing and validation. Preliminary study phase is planned to take the flow / stage as follows: the study of literature, study / collection of field data, and a description and analysis of field findings (factual), namely: (1) the description of the characteristics of vocational school with a standard performance indicators that can be poured as the content of the development of Teaching Factory, (2) description of the characteristics and potential development group management model Tefa as an outcome groups to develop and implement management models Tefa for Vocational Schools (3) description of the characteristics of potential stakeholders to support the development and implementation TEFA management model, and (4) preparation of the management model TEFA for Vocational Schools excellence and innovative. Trial design done on a limited scale model group. Then the model was accompanied by outcome group to develop and implement a model TEFA for Vocational Schools to determine whether the model developed can be applied correctly. Data analysis in this study, the preliminary study stage, the findings or the facts about productive learning programs implemented are described in the form of data presentation, and then analyzed (interpreted) qualitative. The process and results of design development done in a peer-group analysis and expert judgment. And the results of trials presented in the form of data as well as design (applicability) were analyzed by descriptive qualitative. Teaching Factory management model developed can be applied in the management of Teaching Factory for Vocational School and serves as a guide for managers Teaching Factory (principals, teachers, industry partners) and can produce a constructive contribution in a partnership between Vocational Schools with industry.

Stock fraud detection using peer group analysis

This study proposes a method to detect suspicious patterns of stock price manipulation using an unsupervised data mining technique: peer group analysis. This technique detects abnormal behavior of a target by comparing it with its peer group and measuring the deviation of its behavior from that of its peers. Moreover, this study proposes a method to improve the general peer group analysis by incorporating the weight of peer group members into summarizing their behavior, along with the consideration of parameter updates over time. Using real time series data of Korean stock market, this study shows the advantage of the proposed peer group analysis in detecting abnormal stock price change. In addition, we perform sensitivity analysis to examine the effect of the parameters used in the proposed method.

A Driver-Based Approach to Airport Valuation

This paper presents the first part of results of extensive research on an alternative concept to airport valuation. It reviews raditional and alternative valuation measures, illustrated by a sample of eight publicly quoted European airports. The main objective is to derive a model taking account of the underlying key value drivers.A peer group analysis shows that only few sector multiples applied by the investors’ community are significantly correlated with key performance indicators based on business fundamentals. By contrast to the results of this alternative driver-based valuation approach, these market multiples are affected by stock market fluctuations and do not adequately reflect the financial position and true value, and hence supports this paper’s view that airports should be valued by recognising key success factors.

A Cluster Analysis Approach for Bank’s Risk Profile: The Romanian Evidence

Cluster analysis, as an exploratory technique, by gathering together those credit institutions sharing similar features in terms of financial intermediation activity, proves to be a complementary tool for the peer group analysis, accomplished at the off-site supervision level. The aim of our study was to include a representative sample of Romanian credit institutions into smaller, homogenous clusters, in order to assess which credit institutions have similar patterns according to their risk profile and profitability. We found that, over the period 2004-2006, the clusters remained relatively stable in terms of similarity of exposure to risks and profitability.

Plastic card fraud detection using peer group analysis

Peer group analysis is an unsupervised method for monitoring behaviour over time. In the context of plastic card fraud detection, this technique can be used to find anomalous transactions. These are transactions that deviate strongly from their peer group and are flagged as potentially fraudulent. Time alignment, the quality of the peer groups and the timeliness of assigning fraud flags to transactions are described. We demonstrate the ability to detect fraud using peer groups with real credit card transaction data and define a novel method for evaluating performance.

Internet banking market performance: Turkey versus the UK

PurposeThe purpose of this paper is to compare and evaluate the internet banking services of Turkey and the UK.Design/methodology/approachThe paper is based on an exploratory research for a sample of nine banks from each country, a web survey is conducted to collect data for each internet bank using an analytical framework based on a three dimensional model.FindingsIt is found that Turkish banks offer a wider range of services from their internet branches compared to British banks, despite the fact that the UK has a more favourable environment for internet banking in terms of the level of sophistication of its banking sector and technological infrastructure. Furthermore, a key difference is observed in the approaches of banks towards the issue of “security”.Research limitations/implicationsThe findings of this paper are reached by the use of a simplified model that only focuses on the transaction side of internet banking. Therefore, further research could encompass other aspects from the provider's perspective of the internet banking environment within a refined model.Practical implicationsThe paper suggests that there is a fundamental difference in the banks' approaches to dealing with security problems and insights can be gained from cross‐country peer group analysis. Furthermore, the study assists researchers and practitioners to evaluate and build upon their e‐business provision.Originality/valueThe paper is the first of its kind, that the authors are aware of, to conduct a cross‐country analysis of internet banking services.