• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.25-31
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• 2020

The purpose of this paper is to examine a financial distress premium in the emerging market. A risk-return trade-off of negative book equity (NBE) and distress firms is empirically analyzed using data from the Stock Exchange of Thailand. This research employs Ohlson's (1980) bankruptcy model as a measurement of distress risk. The results indicate that distress firms outperform solvent firms in the Thai market and deny distress anomaly often found in the developed market. Fama-Frech (1993) three-factor model and Carhart (1997) four-factor model verify the existence of a distress premium in the Thai capital market. Risk-seeking investors demand greater compensation for bearing risks of distress firms' going concern. This paper provides fresh evidence that default risk is a significant explanatory factor in pricing stocks in the emerging market. Also, this study sheds light on the role of NBE firms in asset pricing. Most studies eliminate NBE firms from their sample. However, NBE firms yield superior average cross-sectional returns, albeit with higher volatility. Investors are rewarded with distress risks associated with NBE firms. The outperformance of NBE firms is statistically significant when compared to the overall market. The NBE premium disappears when factoring size, value, and momentum in time-series analysis.

• The Journal of Asian Finance, Economics and Business
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• v.10 no.2
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• pp.175-181
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• 2023

Competition assures improved products and services to meet customers' needs. The soundness of a firm's financial health is crucial for the country's economic well-being. Distressed companies cause investor panic, which has a knock-on effect on the economy and leads to a deterioration in the image and value of the companies. This paper aims to empirically investigate the influence of competition on financial distress (FD) in the healthcare industry using the Altman Zscore values as the proxy for FD. This study uses secondary data from ten healthcare companies operating in India between 2016 and 2020. The study's findings indicate a significant negative relation with the exogenous variables of the study, implying that a higher level of competition enhances a firm's FD or adversely affects financial health. The main implication of the study is two-pronged. Firstly, the firms' managers and decision-makers need not worry about competition as a deterrent to stability. Secondly, the policymakers need not be concerned that high competition may lead to financial stress for the firms. Therefore, this paper concludes that competition is good for firms operating in India.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.5
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• pp.2459-2464
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• 2016

Background: Cancer treatments can have long-term physical, psychological, financial, sexual and cognitive effects that may influence the quality of life. These can vary from urban to rural areas, survival period and according to the type of cancer. We here aimed to describe demographics and psychosocial analysis of cancer survivors three to five years post-treatment in rural Australia and also assess relationships with financial stress and quality of life domains. Materials and Methods: In this cross-sectional study, 65 participants visiting the outpatient oncology clinic were given a self-administered questionnaire. The inclusion criteria included three to five years post-treatment. Three domains were investigated using standardised and validated tools such as the Standard Quality of Life in Adult Cancer Survivors Scale (QLACS) and the Personal and Household Finances (HILDA) survey. Included were demographic parameters, quality of life, treatment information and well-being. Results: There was no evidence of associations between any demographic variable and either financial stress or cancer-specific quality of life domains. Financial stress was however significantly associated with the cancer-specific quality of life domains of appearance-related concerns, family related distress, and distress related to recurrence. Conclusions: This unique study effectively points to psychosocial aspects of cancer survivors in rural regions of Australia. Although the majority of demographic characteristics were not been found to be associated with financial stress, this latter itself is significantly associated with distress related to family and cancer recurrence. This finding may be of assistance in future studies and also considering plans to fulfil unmet needs.

• Journal of Technology Innovation
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• v.19 no.2
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• pp.25-51
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• 2011

This study examine the firms fund R&D expenditures through cash and cash equivalents under financial distress in order to avoid huge adjustment costs that can be brought after R&D expenditures cut-down. Other study divided the firms' financial condition by only firms' year. This study identifies the firms' financial condition not only by a firm's year but also by firm size and Altman's Z-Score and K-Score. The results show that there are statistically negative relationship between R&D expenditures and cash and cash equivalents when firms are under financial distress. The results are same regardless of criteria of classification of firms' financial condition, which is consistent to the hypothesis. Young and small firms and firms with moderate possibility of bankruptcy fund R&D expenditures through cash and cash equivalent compared to the other firms. We can find the new evidence when we classify the firm by Z-Score and K-Score of Altman. The firms with high possibility of bankruptcy can not fund for R&D activities from cash, but only the firms with moderate possibility of bankruptcy fund R&D expenditures through cash and cash equivalent in the condition of financial distress. The evidence suggests that firms fund R&D expenditures by cash and cash equivalent when they are under financial distress. Findings provide an implication on the management of R&D expenditures and liquidity in the firms.

• Journal of Digital Convergence
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• v.2 no.1
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• pp.145-160
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• 2004

P Leasing Company was a steady growing company with positive net income in most years since 1985 when it was established. However, it suddenly experienced a minus net income of 6.8 billion won in 1996. The reason of its deficit was known to be the financial distress of its two major leasing contracts. The total amount of two contracts was 58 billion won witch is about 8% of total amount of its leasing contracts. In this paper, we analyse how the disability of lease payments from the two leasing contracts influence P Leasing Company's financial stability, growth opportunity, and profitability. In addition, by performing ROI analyse, we point out the financial reasons of P Leasing Company's deficit in 1996. We hope our case analysis to help students understand the cash flow of leasing companies. The P Leasing Company case also illustrates the fact that bad leasing contracts would seriously affect the profitability of leasing companies as bad loans would seriously do the profitability of commercial banks.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.573-585
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• 2021

The banking sector is one of the most important sectors in Pakistan's struggling economy. Recent studies have recommended that suitable methods can be applied to predict bankruptcy. In this context, this work analyzes Pakistan's banking sector's financial status through the five-factor Altman Z-score model, which determines the probability of bankruptcy for an organization. Banking data has been collected through the Pakistan Stock Exchange (PSX) in the period 2013-2017. The Z-score assessment criteria is defined as: Z> 2.99 - "safe" zone; Z> 1.8 Z>2.98- "grey" zone; and Z <1.8 - "distress" zone. Results show good predictions for the local banking industry, while most foreign Pakistani banks were found bankrupt with the Z-score below 1.1. One of the financial risks investors face when investing in any company is the risk of bankruptcy. One of the most used models for predicting financial distress for any company is Altman's Z-score model. On the other hand, the Z-score analysis suggests that all banking establishments are not bankrupt because they have sufficient ability to control bankruptcy. At the same time, foreign banks failed financially and would not be able to be sustained in the future because they do not have the ability to pay the short-term and long-term debt.

• Information Systems Review
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• v.18 no.2
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• pp.173-198
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• 2016

Financial distress can damage stakeholders and even lead to significant social costs. Thus, financial distress prediction is an important issue in macroeconomics. However, most existing studies on building a financial distress prediction model have only considered idiosyncratic risk factors without considering systematic risk factors. In this study, we propose a prediction model that considers both the idiosyncratic risk based on a financial ratio and the systematic risk based on a business cycle. Ultimately, we build several IT artifacts associated with financial ratio and add them to the idiosyncratic risk factors as well as address the imbalanced data problem by using an oversampling technique and synthetic minority oversampling technique (SMOTE) to ensure good performance. When considering systematic risk, our study ensures that each data set consists of both financially distressed companies and financially sound companies in each business cycle phase. We conducted several experiments that change the initial imbalanced sample ratio between the two company groups into a 1:1 sample ratio using SMOTE and compared the prediction results from the individual data set. We also predicted data sets from the subsequent business cycle phase as a test set through a built prediction model that used business contraction phase data sets, and then we compared previous prediction performance and subsequent prediction performance. Thus, our findings can provide insights into making rational decisions for stakeholders that are experiencing an economic crisis.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.682-693
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• 2019

The traditional classification methods mostly assume that the data for class distribution is balanced, while imbalanced data is widely found in the real world. So it is important to solve the problem of classification with imbalanced data. In Mahalanobis-Taguchi system (MTS) algorithm, data classification model is constructed with the reference space and measurement reference scale which is come from a single normal group, and thus it is suitable to handle the imbalanced data problem. In this paper, an improved method of MTS-CBPSO is constructed by introducing the chaotic mapping and binary particle swarm optimization algorithm instead of orthogonal array and signal-to-noise ratio (SNR) to select the valid variables, in which G-means, F-measure, dimensionality reduction are regarded as the classification optimization target. This proposed method is also applied to the financial distress prediction of Chinese listed companies. Compared with the traditional MTS and the common classification methods such as SVM, C4.5, k-NN, it is showed that the MTS-CBPSO method has better result of prediction accuracy and dimensionality reduction.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.665-673
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• 2021

Default has become an extreme concern in the current world due to the financial crisis. The previous prediction of companies' bankruptcy exhibits evidence of decision assistance for financial and regulatory bodies. Notwithstanding numerous advanced approaches, this area of study is not outmoded and requires additional research. The purpose of this research is to find the best classifier to detect a company's default risk and bankruptcy. This study used secondary data from the Pakistan Stock Exchange (PSX) and it is time-series data to examine the impact on the determinants. This research examined several different classifiers as per their competence to properly categorize default and non-default Pakistani companies listed on the PSX. Additionally, PSX has remained consistent for some years in terms of growth and has provided benefits to its stockholders. This paper utilizes machine learning techniques to predict financial distress in companies listed on the PSX. Our results indicate that most multi-stage mixture of classifiers provided noteworthy developments over the individual classifiers. This means that firms will have to work on the financial variables such as liquidity and profitability to not fall into the category of liquidation. Moreover, Adaptive Boosting (Adaboost) provides a significant boost in the performance of each classifier.

• Korean Journal of Adult Nursing
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• v.24 no.1
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• pp.52-63
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• 2012

Purpose: The purpose of this study was to measure the quality of life (QOL) and to identify the factors influencing QOL in gynecological cancer patients. Methods: The subjects of this study were 242 people who were receiving medical therapy or follow-up after surgery from one general hospital in Daegu. Data were collected from August 1, 2010 to January 31, 2011. A questionnaire including questions on QOL, distress score, distress problem, depression, anxiety, insomnia, perceived health status and body image were completed by the subjects. Results: The mean score of QOL was $70.68{\pm}13.40$. Religion, job, presence of spouse, level of education, household income, financial compensation, disease stage and recurrence were the significant factors related to QOL. Distress score, distress problem, depression, anxiety, insomnia, perceived health status and body image were also significant factors influencing QOL. Sixty eight percent of the variance in subjective overall QOL can be explained by body image, distress problem, distress score, anxiety, level of education and perceived health status (Cum $R^2$=0.689, F=76.316, $p$ <.001). Body image was the most important factor related to QOL. Conclusion: An integrative care program which includes general, disease-related and psychosocial characteristics of patients is essential to improve QOL in gynecological cancer patients.