Does intellectual capital help predict bankruptcy?

2018 ◽  
Vol 19 (2) ◽  
pp. 321-337 ◽  
Author(s):  
Velia Gabriella Cenciarelli ◽  
Giulio Greco ◽  
Marco Allegrini
Keyword(s):  
Performance Measures ◽  
Intellectual Capital ◽  
Financial Stability ◽  
Prediction Models ◽  
Predictive Ability ◽  
Time Frame ◽  
Bankruptcy Prediction ◽  
Superior Performance ◽  
Content Type

Purpose The purpose of this paper is to explore whether intellectual capital affects the probability that a particular firm will default. The authors also test whether including intellectual capital performance in bankruptcy prediction models improves their predictive ability. Design/methodology/approach Using a sample of US public companies from the period stretching from 1985 to 2015, the authors test whether intellectual capital performance reduces the probability of bankruptcy. The authors use the VAIC as an aggregate measure of corporate intellectual capital performance. Findings The findings show that the intellectual capital performance is negatively associated with the probability of default. The findings also indicate that the bankruptcy prediction models that include intellectual capital have a superior predictive ability over the standard models. Research limitations/implications This paper contributes to prior research on intellectual capital and firm performance. To the best of the knowledge, this is the first study to show that the benefits of intellectual capital extend from superior performance to long-term financial stability. The research can also contribute to bankruptcy studies. By using a time frame covering decades, the findings suggest that intellectual capital performance measures can be included in bankruptcy prediction models and can effectively complement traditional performance measures. Originality/value This paper highlights that intellectual capital is associated with long-term financial stability and a lower bankruptcy risk. Firms realising the potential of their intellectual capital can produce a virtuous circle between higher performance and greater financial stability.

A review of literature on risk prediction tools for hospital readmissions in older adults

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Michelle Louise Gatt ◽  
Maria Cassar ◽  
Sandra C. Buttigieg
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Hospital Readmissions ◽  
Predictive Ability ◽  
Chronic Obstructive ◽  
Risk Category ◽  
Extensive Literature ◽  
Content Type ◽  
Prediction Tools ◽  
Readmission Risk

Purpose The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.Design/methodology/approach Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.Findings Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.Research limitations/implications Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.Originality/value This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

An empirical investigation of buyer–supplier relationship typologies and their behavioral and performance outcomes

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jin Li ◽  
Linlin Chai ◽  
Chanchai Tangpong ◽  
Michelle Hong ◽  
Rodney D. Traub
Keyword(s):  
Performance Measures ◽  
Online Survey ◽  
Performance Outcomes ◽  
Superior Performance ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Content Type ◽  
Behavioral Dynamics ◽  
And Performance ◽  
Supplier Relationship

Purpose This study aims to examine empirically the existence of four classical and four emerging buyer–supplier relationship (BSR) types and how they differ in terms of behavioral dynamics and performance measures. Design/methodology/approach This study uses an online survey to collect data from 371 purchasing managers in the USA. Findings A cluster analysis statistically supports the existence of five of these eight BSR types, including strategic/bilateral partnership, market/discrete, supplier-led collaboration, captive supplier/buyer dominant and captive buyer/supplier dominant BSRs. Further, ANOVA tests show that these five BSRs differ in terms of behavioral outcomes and performance measures. Research limitations/implications This study is based on a cross-sectional survey so it cannot examine how these BSR types may evolve over time, and it is not suitable to examine some rare types of BSRs. In addition, this study does not consider contextual factors that may moderate the influence of BSR types on the behavioral dynamics and performance measures. Practical implications Managers should consider the potential to be able to develop and enhance a strategic/bilateral relationship with their supply chain partners, which in at least some circumstances can lead to superior performance results. Similar observations can be made with respect to supplier-led and, to a lesser degree, buyer-led collaboration. Originality/value Most existing research of the BSR types is largely a product of theoretical classifications, and there is also a lack of research of their performance implications. This study fills these gaps in the literature.

Financial health of Syariah and non-Syariah banks: a comparative analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Elok Heniwati ◽  
Nella Yantiana ◽  
Gita Desyana
Keyword(s):  
Comparative Analysis ◽  
Financial Stability ◽  
Influential Factors ◽  
Financial Health ◽  
Short Term ◽  
Liquid Asset ◽  
Content Type ◽  
Explanatory Variables ◽  
Insolvency Risk

Purpose This paper aims to investigate whether Syariah banks are more financially stable than non-Syariah banks and check the differential impact of explanatory variables in financial health and efficiency in the context of Indonesia. Design/methodology/approach By using unbalanced panel data from Bankfocus over the period 2011–2018, regression analysis is performed with two response variables representing financial health, ZSCORE for return on average assets, liquid asset to deposit and short-term funding ratio. A number of control variables are used as tools to confirm the hypotheses. To check the robustness of the findings, a model with different specifications has been used. Findings The results indicate that while Syariah banks present higher insolvency risk (less health) for long-term activity, the opposite is true for short-term activity. Other findings show that Syariah and non-Syariah banks contribute differently to the national system of financial stability owing to varying influential factors on the bank’s health. Originality/value This paper presents a comparative analysis between the financial stability of Syariah banks and that of non-Syariah banks in Indonesia by building an empirical framework that allows the author to examine the differential effects of each underlying feature on financial stability in Syariah and non-Syariah banks.

How to balance interfirm relationships? A case from high-speed railway industry

2020 ◽  
Vol 35 (11) ◽  
pp. 1785-1799 ◽  
Author(s):  
Na Zhang ◽  
Xiaopeng Deng ◽  
Bon-Gang Hwang ◽  
Yanliang Niu
Keyword(s):  
High Speed ◽  
Superior Performance ◽  
Interfirm Relationships ◽  
High Speed Railway ◽  
Content Type ◽  
Railway Industry ◽  
Interfirm Cooperation ◽  
Relationship Structures ◽  
Interfirm Competition

Purpose Balancing interfirm relationships is important for firms’ long-term superior performance. However, prior studies mainly focus on interfirm competition or interfirm cooperation separately, ignoring the balance of interfirm relationships. To bridge this gap in knowledge, this study aims to develop a framework to evaluate the balance of interfirm competition and interfirm cooperation and propose strategies to optimize a firm’s interfirm relationships. Design/methodology/approach After an in-depth literature review, a framework was developed for evaluating and optimizing the interfirm relationships. Taking the high-speed railway industry as an example, the proposed framework was implemented. Findings The results of the case confirm that the balancing of interfirm relationships can lead to more superior firm performance. Also, rather than mutual suppression, the interfirm competition and interfirm cooperation present a roughly positive relationship. Originality/value This study would contribute to the existing knowledge body by developing a framework for balancing interfirm relationships. Also, this study can aid practitioners in evaluating and optimizing their interfirm relationship structures.

scholarly journals Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities

2020 ◽  
Vol 13 (5) ◽  
pp. 92
Author(s):  
Katarina Valaskova ◽  
Pavol Durana ◽  
Peter Adamko ◽  
Jaroslav Jaros
Keyword(s):  
Prediction Models ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Confusion Matrix ◽  
Predictive Ability ◽  
Early Warning Systems ◽  
Emerging Countries ◽  
Bankruptcy Prediction ◽  
Financial Health ◽  
Prediction Ability

The risk of corporate financial distress negatively affects the operation of the enterprise itself and can change the financial performance of all other partners that come into close or wider contact. To identify these risks, business entities use early warning systems, prediction models, which help identify the level of corporate financial health. Despite the fact that the relevant financial analyses and financial health predictions are crucial to mitigate or eliminate the potential risks of bankruptcy, the modeling of financial health in emerging countries is mostly based on models which were developed in different economic sectors and countries. However, several prediction models have been introduced in emerging countries (also in Slovakia) in the last few years. Thus, the main purpose of the paper is to verify the predictive ability of the bankruptcy models formed in conditions of the Slovak economy in the sector of agriculture. To compare their predictive accuracy the confusion matrix (cross tables) and the receiver operating characteristic curve are used, which allow more detailed analysis than the mere proportion of correct classifications (predictive accuracy). The results indicate that the models developed in the specific economic sector highly outperform the prediction ability of other models either developed in the same country or abroad, usage of which is then questionable considering the issue of prediction accuracy. The research findings confirm that the highest predictive ability of the bankruptcy prediction models is achieved provided that they are used in the same economic conditions and industrial sector in which they were primarily developed.

Bankruptcy prediction using multivariate grey prediction models

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Chung Hu ◽  
Peng Jiang ◽  
Hang Jiang ◽  
Jung-Fa Tsai
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Financial Data ◽  
Grey Prediction ◽  
Grey System ◽  
Content Type ◽  
Business Environments ◽  
The Face ◽  
Financial Decision

PurposeIn the face of complex and challenging economic and business environments, developing and implementing approaches to predict bankruptcy has become important for firms. Bankruptcy prediction can be regarded as a grey system problem because while factors such as the liquidity, solvency and profitability of a firm influence whether it goes bankrupt, the precise manner in which these factors influence the discrimination between failed and non-failed firms is uncertain. In view of the applicability of multivariate grey prediction models (MGPMs), this paper aimed to develop a grey bankruptcy prediction model (GBPM) based on the GM (1, N) (BP-GM (1, N)).Design/methodology/approachAs the traditional GM (1, N) is designed for time series forecasting, it is better to find an appropriate permutation of firms in the financial data as if the resulting sequences are time series. To solve this challenging problem, this paper proposes GBPMs by integrating genetic algorithms (GAs) into the GM (1, N).FindingsExperimental results obtained for the financial data of Taiwanese firms in the information technology industries demonstrated that the proposed BP-GM (1, N) performs well.Practical implicationsAmong artificial intelligence (AI)-based techniques, GBPMs are capable of explaining which of the financial ratios has a stronger impact on bankruptcy prediction by driving coefficients.Originality/valueApplying MGPMs to a problem without relation to time series is challenging. This paper focused on bankruptcy prediction, a crucial issue in financial decision-making for businesses, and proposed several GBPMs.

Schoolinary art: practical cooking skills issues for the future

2015 ◽  
Vol 117 (2) ◽  
pp. 629-650 ◽  
Author(s):  
Dawn Mc Dowell ◽  
Una McMahon-Beattie ◽  
Amy Burns
Keyword(s):  
Low Income ◽  
Policy Decision ◽  
Skills Training ◽  
Time Frame ◽  
Home Economics ◽  
Structured Learning ◽  
Content Type ◽  
Cooking Skills ◽  
The Impact

Purpose – The purpose of this paper is to consider the importance of structured and consistent practical cookery skills intervention in the 11-14-year age group. This paper reviews the impact and development of statutory and non-statutory cooking skills interventions in the UK and considers limitations in relation to life skills training. Currently practical cooking skills are mainly derived from two sources namely the non-statutory sector (community cooking interventions) and the statutory sector (Home Economics teaching). Design/methodology/approach – The paper compares the two interventions in terms of effective long-term outcomes. Non-statutory cooking interventions are generally lottery funded and therefore tend to be single teaching blocks of, on average, six to eight weeks targeting mostly low-income adults and the literature emphasises a deficit of empirical measurement of the long-term impact. In contrast Home Economics classes offer a structured learning environment across genders and socio-economic groups. In addition it is taught over a substantial time frame to facilitate a process of practical skills development (with relevant theoretical teaching), reflection, group communication and consolidation, where according to current educational theory (Kolb, 1984) learning is more thoroughly embedded with the increased potential for longer term impact. Findings – The review identifies the limitations of too many community initiatives or “project-itis” (Caraher, 2012, p. 10) and instead supports the use of the school curriculum to best maximise the learning of practical cooking skills. Originality/value – This review will be of particular value to educationalists and health policy decision makers.

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 Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11262
Author(s):  
Guobin Li ◽  
Xiuquan Du ◽  
Xinlu Li ◽  
Le Zou ◽  
Guanhong Zhang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Dna Binding ◽  
Binding Proteins ◽  
Prediction Models ◽  
Short Term Memory ◽  
Local Features ◽  
Dna Binding Proteins ◽  
Superior Performance

DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence. In this paper, we propose a method, called PDBP-Fusion, to identify DBPs based on the fusion of local features and long-term dependencies only from primary sequences. We utilize convolutional neural network (CNN) to learn local features and use bi-directional long-short term memory network (Bi-LSTM) to capture critical long-term dependencies in context. Besides, we perform feature extraction, model training, and model prediction simultaneously. The PDBP-Fusion approach can predict DBPs with 86.45% sensitivity, 79.13% specificity, 82.81% accuracy, and 0.661 MCC on the PDB14189 benchmark dataset. The MCC of our proposed methods has been increased by at least 9.1% compared to other advanced prediction models. Moreover, the PDBP-Fusion also gets superior performance and model robustness on the PDB2272 independent dataset. It demonstrates that the PDBP-Fusion can be used to predict DBPs from sequences accurately and effectively; the online server is at http://119.45.144.26:8080/PDBP-Fusion/.

Possibilities and limitations of the use of bankruptcy prediction models for insolvency prevention

2021 ◽  
pp. 73-94
Author(s):  
Olga Lvova
Keyword(s):  
Financial Reporting ◽  
Financial Distress ◽  
Financial Stability ◽  
Prediction Models ◽  
Organizational Factors ◽  
Bankruptcy Prediction ◽  
Operating Conditions ◽  
Advantages And Disadvantages ◽  
Qualitative Variables ◽  
The Impact

The paper provides the solution to the problem of an integrated classification of existing bankruptcy prediction based on the content analysis of 270 relevant foreign and Russian publications issued within a period of 1910-2020. The author identifies two main groups of models– normative and positive, with the latter categorized into expert, mixed and objective including traditional statistical models and artificial intelligent techniques; and considers the specific features of certain predicting models, their advantages and disadvantages. He then reveals the economic content of such models and the set of ratios applied for identifying company’s financial distress with the following conclusions: approaches to the variables selection are rarely justified, indicators are usually borrowed from previous models or generated automatically by the database configuration; the accounting approach to bankruptcy forecasting based on financial ratios prevails and has serious limitations for Russian companies; the most significant market, value and qualitative variables indicating a decline in the business financial stability are highlighted. Significant limitations of the general use of bankruptcy prediction models for making decisions aimed at insolvency prevention are identified: the inability to anticipate the impact of informal factors that are irregular, unable to extrapolate, and affect companies in different ways; the need to take into account the economic conditions of the national economy, financial reporting standards, and the level of availability of diverse data; the impossibility of creating a universal indicative basis to identify decline of sustainability of any business due to the high volatility of operating conditions in Russia. Bayesian methods and nowcasting, as well as the development of forecasting models for certain industries, are promising areas for the development of modern approaches to bankruptcy prediction, but the fundamental activity for preventing insolvency is not forecasting by models, but the implementation of continuous monitoring of the overall business performance in relation to influencing market, operational, investment, financial, managerial and organizational factors, taking into account significant qualitative variables.

Sign in / Sign up

Export Citation Format

Share Document