A territorial perspective of SME’s default prediction models

2018 ◽  
Vol 35 (4) ◽  
pp. 542-563 ◽  
Author(s):  
Linda Gabbianelli
Keyword(s):  
Financial Institutions ◽  
Prediction Models ◽  
Financial Ratios ◽  
Business Failure ◽  
Competitive Strategies ◽  
Content Type ◽  
Default Prediction ◽  
Qualitative Variables ◽  
The Relationship ◽  
Accuracy Rates

Purpose The purpose of this paper is to test whether the qualitative variables regarding the territory and the firm–territory relationship can improve the accuracy rates of small business default prediction models. Design/methodology/approach The authors apply a logistic regression to a sample of 141 small Italian enterprises located in the Marche region, and the authors build two different default prediction models: one using only financial ratios and one using jointly financial ratios and variables related to the relationship between firm and territory. Findings Including variables regarding the relationships between firms and their territory, the accuracy rates of the default prediction model are significantly improved. Research limitations/implications The qualitative variables data collected are affected by subjective judgments of respondents of the firms studied. In addition, neither other qualitative variables (such as those regarding competitive strategies, or managerial skills) are included nor those variables regarding the relationships between firms and financial institutions are included. Practical implications The study suggests that financial institutions should include territory qualitative variables, and, above all, qualitative variables regarding the firm–territory relationship, when constructing business default prediction models. Including this type of variables, it could be able to reduce the tendency to place unnecessary restrictions on credit. Originality/value The field of business failure prediction modeling using variables regarding the relationship between firm–territory is a unexplored area as it count of a very few studies.

scholarly journals Empirics of Korean Shipping Companies’ Default Predictions

Risks ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 159
Author(s):  
Sunghwa Park ◽  
Hyunsok Kim ◽  
Janghan Kwon ◽  
Taeil Kim
Keyword(s):  
Logit Model ◽  
Prediction Models ◽  
Predictive Ability ◽  
Financial Ratios ◽  
Probability Of Default ◽  
Shipping Industry ◽  
Market Participants ◽  
Korean Economy ◽  
Default Prediction ◽  
Financial Characteristics

In this paper, we use a logit model to predict the probability of default for Korean shipping companies. We explore numerous financial ratios to find predictors of a shipping firm’s failure and construct four default prediction models. The results suggest that a model with industry specific indicators outperforms other models in predictive ability. This finding indicates that utilizing information about unique financial characteristics of the shipping industry may enhance the performance of default prediction models. Given the importance of the shipping industry in the Korean economy, this study can benefit both policymakers and market participants.

Collective action among rural poor

2019 ◽  
Vol 37 (1) ◽  
pp. 20-43
Author(s):  
George Okello Candiya Bongomin ◽  
John C. Munene ◽  
Joseph Mpeera Ntayi ◽  
Charles Akol Malinga
Keyword(s):  
Collective Action ◽  
Financial Institutions ◽  
Financial Intermediation ◽  
Financial Inclusion ◽  
Content Type ◽  
Mediating Role ◽  
The Poor ◽  
Bootstrap Approach ◽  
The Relationship

PurposeThe purpose of this paper is to establish the mediating role of collective action in the relationship between financial intermediation and financial inclusion of the poor in rural Uganda.Design/methodology/approachThe paper uses structural equation modeling (SEM) through bootstrap approach constructed using analysis of moment structures to test for the mediating role of collective action in the relationship between financial intermediation and financial inclusion of the poor in rural Uganda. Besides, the paper adopts Baron and Kenny’s (1986) approach to establish whether conditions for mediation by collective action exist.FindingsThe results revealed that collective action significantly mediates the relationship between financial intermediation and financial inclusion of the poor in rural Uganda. The findings further indicated that the mediated model had better model fit indices than the non-mediated model under SEM bootstrap. Furthermore, the results showed that both collective action and financial intermediation have significant and direct impacts on financial inclusion of the poor in rural Uganda. Therefore, the findings suggest that the presence of collective action boost financial intermediation for improved financial inclusion of the poor in rural Uganda.Research limitations/implicationsThe study used quantitative data collected through cross-sectional research design. Further studies through the use of interviews could be adopted in future. Methodologically, the study adopted use of SEM bootstrap approach to establish the mediating effect of collective action. However, it ignored the Sobel’s test and MedGraph methods. Future studies could adopt the use of alternative methods of Sobel’s test and MedGraph. Additionally, the study focused only on semi-formal financial institutions. Hence, further studies may consider the use of data collected from formal and informal institutions.Practical implicationsPolicy makers and managers of financial institutions should consider the role of collective action in promoting economic development, especially in developing countries. They should create structures and design financial services and products that promote collective action among the poor in rural Uganda.Originality/valueAlthough several scholars have articulated financial inclusion based on both the supply and demand side factors, this is the first study to test the mediating role of collective action in the relationship between financial intermediation and financial inclusion of the poor in rural Uganda using SEM bootstrap approach. Theoretically, the study combines the role of collective action with financial intermediation to promote financial inclusion. Financial intermediation theory ignores the role played by collective action in the intermediation process between the surplus and deficit units.

Non-compliance behavior of de-risking force the EU to enhance anti-money laundering regulation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kalle Johannes Rose
Keyword(s):  
Money Laundering ◽  
Financial Institutions ◽  
Law And Economics ◽  
Negative Impact ◽  
Regulatory Framework ◽  
Content Type ◽  
Functional Methods ◽  
Compliance Behavior ◽  
The Relationship ◽  
The Eu

Purpose Recent research shows that because of money-laundering risks, there has been an increase in the off-boarding of certain types of corporate clients in the financial sector. This phenomenon known as “de-risking” has been argued to have a negative impact on society, because it increases the possible risk of money laundering. The purpose of this paper is to analyze whether the de-risking strategy of financial institutions results in an expansion of the regulatory framework concerning anti-money laundering focusing on off-boarding of clients and, if so, is there a way to avoid further regulation by changing present behavior. Design/methodology/approach This paper applies functional methods to law and economics to achieve higher efficiency in combating money laundering. Findings In this paper, it is found that the continuing of de-risking by financial institutions because of the avoidance strategy of money-laundering risks will inevitably result in further regulatory demands regarding the off-boarding process of clients. The legal basis for the introduction of further regulatory intervention is that some of the de-risking constitutes a direct contradiction to the aim of the present regulatory framework, making the behavior non-compliant to the regulation. Originality/value There has been very little research concerning de-risking related to money laundering. The present research has focused on the effect on society and not the relationship between the financial institutions and the regulator. This paper raises an important and present problem, as the behavior of the financial institutions constitute a response from the regulator that is contradicting the thoughts behind the behavior of the financial institutions. It is found that the paper is highly relevant if an expansion of regulation is to be hindered.

scholarly journals The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms

2017 ◽  
Vol 12 (12) ◽  
pp. 251 ◽  
Author(s):  
Francesco Ciampi
Keyword(s):  
Central Italy ◽  
Manufacturing Firms ◽  
Financial Ratios ◽  
Small Enterprise ◽  
Small Enterprises ◽  
Units Of Analysis ◽  
Default Prediction ◽  
Multivariate Discriminant ◽  
Industry Sectors ◽  
Accuracy Rates

The existing literature has proved the effectiveness of financial ratios for company default prediction modelling. However, such researches rarely focus on small enterprises (SEs) as specific units of analysis. The aim of this paper is to demonstrate that SE default prediction should be modelled separately from that of large and medium-sized firms. In fact, a multivariate discriminant analysis was applied to a sample of 2,200 small manufacturing firms located in Central Italy and a SE default prediction model was developed based on a selected group of financial ratios and specifically constructed to capture the specificities of SEs’ risk profiles. Subsequently, the prediction accuracy rates obtained by this model were compared with those obtained from a second model based on a sample of 3,200 manufacturing firms situated in Central Italy which belong to all dimensional classes. The findings are the following: 1) evaluating the probability of default of SEs separately from that of larger firms improves prediction performance; 2) the predictive power of the discriminant function improves if it takes into account the different profiles of firms operating in different industry sectors; 3) this improvement is much greater for SEs compared to larger firms.

scholarly journals A unified framework for incorporating decision making into explanations of business failure

2015 ◽  
Vol 115 (7) ◽  
pp. 1341-1357 ◽  
Author(s):  
Joseph Amankwah-Amoah
Keyword(s):  
Human Capital ◽  
Prediction Models ◽  
Decision Makers ◽  
Business Failure ◽  
Unified Framework ◽  
Firm Level ◽  
Content Type ◽  
Fine Grained ◽  
Institutional Linkages ◽  
Learning From Others

Purpose – The purpose of this paper is to examine how decision-maker attributes unfold to precipitate organisational failure. The analysis brings to light how key attributes such as information-processing capabilities and human capital decay interact to bring about business decline and exit. Design/methodology/approach – The study is based on an integrated review and conceptualisation of the literature. Findings – The study articulates how a set of attributes of decision makers, i.e. human capital obsolescence, powerlessness, meaninglessness and institutional linkages, contributes to organisational failure. Research limitations/implications – The paper concludes by setting out an array of strategies of learning from others’ failures. Originality/value – In spite of a growing body of research on organisational failure, scholars have placed overwhelming emphasis on ecological explanations and business failure prediction models. The study moves beyond the ecological explanations to offer a more fine-grained analysis of firm-level factors that precipitate business failure.

Corporate bankruptcy prediction models applied to emerging economies

2007 ◽  
Vol 17 (4) ◽  
pp. 295-311 ◽  
Author(s):  
Ariel R. Sandin ◽  
Marcela Porporato
Keyword(s):  
Information Content ◽  
Emerging Economies ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Data Availability ◽  
Business Failure ◽  
Multiple Discriminant Analysis ◽  
Public Companies ◽  
Content Type ◽  
Profitability Ratios

PurposeThe paper's aim is to test the usefulness of ratio analysis to predict bankruptcy in a period of stability of an emerging economy, such as the case of Argentina in the 1990s.Design/methodology/approachFinancial profiles of 22 bankrupt and healthy companies are examined and a model is built using the multiple discriminant analysis technique, thus providing comparability with previous studies.FindingsThe set of models tested in this paper show that the financial data of Argentine companies in the 1990s do have information content, but the model to use depends on the preferences of the decision maker. Comparing models it is observed a common use of solvency ratios in terms of total assets and profitability ratios in terms of sales.Research limitations/implicationsData availability constitutes the primary limitation of this and similar studies, here is reflected in the sample size: 11 healthy and 11 bankrupt.Practical implicationsThe model can be used to assist investors, creditors, and regulators in Argentina and other emerging economies to predict business failure. The Z ′‐score model of Altman can be used for public companies in emerging economies because it pays attention to solvency indicators, but in rapid changing environment, profitability ratios should also be considered.Originality/valueThe incremental information content of profitability and solvency in predicting bankruptcy is examined and a simple and reliable failure prediction model for large Argentinean firms is developed. Also this paper offers a classification method that is publicly available to all investors and creditors interested in Argentinean companies.

scholarly journals The art of deterrence: Singapore’s anti-money laundering regimes

2018 ◽  
Vol 25 (2) ◽  
pp. 467-498
Author(s):  
Veltrice Tan
Keyword(s):  
Money Laundering ◽  
Financial Institutions ◽  
Financial Institution ◽  
Deterrent Effect ◽  
Limited Data ◽  
Content Type ◽  
Effective Deterrent ◽  
State Of Affairs ◽  
The Relationship ◽  
Practical Implications

Purpose In light of the recent 1MDB Scandal in Singapore, this research paper aims to examine the deterrent effect of Singapore’s sanctions against money laundering within financial institutions. Design/methodology/approach Case laws and legislations are examined as are relevant reports by regulators. Findings Singapore’s anti-money laundering (AML) regimes may not act as an effective deterrent against money laundering activities within financial institutions. This is due to the overreliance on the theory of deterrence-based thinking, the lack of an “enforcement pyramid” and economic factors which influence regulators to be lenient towards financial institutions. Research limitations/implications There are limited data available in relation to regulators in Singapore and the prevalence of money laundering activities within Singapore’s financial institution. Any discussions within this article is based on the impressionistic observations of this author, which may not reflect the true state of affairs in Singapore. Practical implications Those who are interested in examining the relationship between money laundering and the deterrent effect of sanctions against financial institutions will have an interest in this topic. Originality/value The value of the paper is to demonstrate that Singapore’s AML regimes may not act as an effective deterrence against money laundering activities within financial institutions.

Regulatory relationships and incentives: from Riggs Bank to HSBC

2017 ◽  
Vol 59 (5) ◽  
pp. 729-739 ◽  
Author(s):  
Patrick John O’Sullivan
Keyword(s):  
United States ◽  
Financial Institutions ◽  
Banking Sector ◽  
Financial Institution ◽  
Academic Research ◽  
Regulatory Relationship ◽  
Content Type ◽  
The Usa ◽  
The One ◽  
The Relationship

Purpose The aim of the paper is to examine what type of relationship existed between the Office of the Comptroller of the Currency (OCC) and Riggs Bank in respect of anti-money laundering (AML) compliance. Different commentators have established certain trends in the interaction between a regulator and a regulated entity, and this paper seeks to apply these findings to the relationship between the OCC and Riggs Bank and ascertain where this example lies in the wider domain of regulatory relationships. The paper then examines whether the relationship between the OCC and HSBC United States was similar to the one between the OCC and Riggs Bank or did the regulator adopt a more aggressive supervisory stance. Throughout this work, there is also a focus on the underlying incentives which may adversely affect how a financial institution interacts with a financial regulator and possible solutions to this problem proposed. Design/methodology/approach Research undertaken by commentators was assessed and their findings as the different regulatory relationships that may develop between a regulator and a regulated entity were applied to the interactions between the OCC and two different financial institutions, namely, Riggs Bank and HSBC United States. Examples from the Senate Subcommittee Reports into the AML failings into these financial institutions were examined through the prism of pre-existing regulatory relationship categories. Findings The paper ultimately concludes that the OCC was far too passive in its interactions with both Riggs Bank and HSBC United States and that the primary underlying motivations for both institutions were profit- rather than compliance-led. Research limitations/implications One of the main limitations to this research was the absence of direct input from either personnel from the banking sector in the USA or of regulators from the same jurisdiction. Practical implications This paper proposes a number of practical solutions to recast the relationship between financial regulators and regulated institutions away from the former deferring to the latter to one where the former dictates to the latter. Originality/value This paper seeks to examine an actual regulatory relationship between a financial regulator and two different institutions that is reported in the public domain by applying pre-existing academic research on question of regulatory relationships and see how the practice differs or corresponds with the theory.

Community bank product design within an asymmetric competitive market

2016 ◽  
Vol 34 (5) ◽  
pp. 752-772 ◽  
Author(s):  
Gregory J McKee ◽  
Albert Kagan
Keyword(s):  
Business Cycle ◽  
Financial Institutions ◽  
Cost Efficiency ◽  
Bank Performance ◽  
Community Banks ◽  
Bank Efficiency ◽  
Content Type ◽  
Community Bank ◽  
Relationship Of ◽  
The Relationship

Purpose – The purpose of this paper is to assess product and service arrays of community banks within competitive markets that are impacted by varying sized financial institutions. A cost efficiency model is used to understand the relationship of product offerings and business cycle response upon bank performance. Design/methodology/approach – A cost efficiency model is used to understand the relationship of product offerings and business cycle response upon bank performance. Markets comprised of alternate size and type of financial institutions are compared. Findings – Greater values of X_EFF i when institutions compete are observed in this analysis. Cost efficiency is lowest when community banks are the only institution in the market, and second lowest when credit unions are the only competing institutions. Call report data are analyzed from 1994 to 2013. The number of big banks increases community bank efficiency and efficiency of large banks. Also, the number of community banks does affect big bank cost efficiency. The magnitude of the effect pertaining to the number of community banks upon big bank efficiency is much smaller than that of the number of big banks on community bank efficiency. Originality/value – This study considers cost efficiency and profitability as measures of institution on the performance of a competing institutional type. The modeling approach uses cost efficiency as a method of observing the performance of financial institutions and an explanation of how firms persist, grow, and respond to changes in technology or regulation. The effects of the presence of each type of financial institution on the performance of another type are compared. Situations in which any number of one or more institutional types is present in a market are considered for analysis purposes.

Models for predicting default: towards efficient forecasts

2014 ◽  
Vol 15 (1) ◽  
pp. 52-70 ◽  
Author(s):  
Fernando Castagnolo ◽  
Gustavo Ferro
Keyword(s):  
Hazard Rate ◽  
Prediction Models ◽  
Cohort Analysis ◽  
Reduced Form ◽  
Market Information ◽  
Content Type ◽  
Default Prediction ◽  
Hazard Rate Models ◽  
Distance To Default ◽  
Power Curves

Purpose – The purpose of this paper is to assess and compare the forecast ability of existing credit risk models, answering three questions: Can these methods adequately predict default events? Are there dominant methods? Is it safer to rely on a mix of methodologies? Design/methodology/approach – The authors examine four existing models: O-score, Z-score, Campbell, and Merton distance to default model (MDDM). The authors compare their ability to forecast defaults using three techniques: intra-cohort analysis, power curves and discrete hazard rate models. Findings – The authors conclude that better predictions demand a mix of models containing accounting and market information. The authors found evidence of the O-score's outperformance relative to the other models. The MDDM alone in the sample is not a sufficient default predictor. But discrete hazard rate models suggest that combining both should enhance default prediction models. Research limitations/implications – The analysed methods alone cannot adequately predict defaults. The authors found no dominant methods. Instead, it would be advisable to rely on a mix of methodologies, which use complementary information. Practical implications – Better forecasts demand a mix of models containing both accounting and market information. Originality/value – The findings suggest that more precise default prediction models can be built by combining information from different sources in reduced-form models and combining default prediction models that can analyze said information.

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