scholarly journals Investigating the Simultaneity of Corporate Hedging and Debt Policies: Empirical Evidence from Indonesia

2005 ◽  
Vol 7 (2) ◽  
pp. 179 ◽  
Author(s):  
Iman Sofian Suriawinata
Keyword(s):  
Financial Distress ◽  
Primary Objective ◽  
Corporate Hedging ◽  
Listed Firms ◽  
Distressed Firms ◽  
Financially Distressed ◽  
Tax Benefits ◽  
Debt Capacity ◽  
Pooled Sample ◽  
Debt Policies

The primary objective of this paper is to investigate the simultaneity of corporate hedging and debt policies. Using a pooled sample of Indonesian non-financial listed firms covering the periods of 1996-2001, the present study finds evidence that corporate hedging and debt policies are simultaneously determined. That is, the use of debts motivate firms to hedge; but simultaneously, hedging increases debt capacity and induces firms to borrow more in order to take advantage of the tax benefits arising from additional debt capacity. Another important finding is that financially distressed firms –as indicated by their debt restructuring programs– are less motivated to hedge, because such firms will see that the option values of their equity will increase as their cash-flow volatilities increase. Therefore, financially distressed firms tend not to hedge; or at least, hedge lesser compared to those of firms that do not experience financial distress.

scholarly journals Feature selection methods and sampling techniques to financial distress prediction for Vietnamese listed companies

2019 ◽  
Vol 16 (1) ◽  
pp. 276-290 ◽  
Author(s):  
Loan Thi Vu ◽  
Lien Thi Vu ◽  
Nga Thu Nguyen ◽  
Phuong Thi Thuy Do ◽  
Dong Phuong Dao
Keyword(s):  
Feature Selection ◽  
Financial Distress ◽  
Stock Exchange ◽  
Sampling Techniques ◽  
Selection Methods ◽  
Listed Firms ◽  
Financial Distress Prediction ◽  
Svm Model ◽  
Distressed Firms ◽  
Financially Distressed

The research is taken to integrate the effects of variable selection approaches, as well as sampling techniques, to the performance of a model to predict the financial distress for companies whose stocks are traded on securities exchanges of Vietnam. A firm is financially distressed when its stocks are delisted as requirement from Vietnam Stock Exchange because of making a loss in 3 consecutive years or having accumulated a loss greater than the company’s equity. There are 12 models, constructed differently in feature selection methods, sampling techniques, and classifiers. The feature selection methods are factor analysis and F-score selection, while 3 sets of data samples are chosen by choice-based method with different percentages of financially distressed firms. In terms of classifying technique, logistic regression together with SVM are used in these models. Data are collected from listed firms in Vietnam from 2009 to 2017 for 1, 2 and 3 years before the announcement of their delisting requirement. The experiment’s results highlight the outperformance of the SVM model with F-score selection method in a data sample containing the highest percentage of non-financially distressed firms.

Financial Distress, Earnings Benchmark and Earnings Management Practices

2021 ◽  
pp. 097226292110109
Author(s):  
Karan Gandhi
Keyword(s):  
Panel Data ◽  
Earnings Management ◽  
Financial Distress ◽  
Management Practices ◽  
Real Earnings Management ◽  
Public Companies ◽  
Large Panel ◽  
Distressed Firms ◽  
Contradictory Evidence ◽  
Financially Distressed

Prior research exhibits contradictory evidence on earnings management practices, both accrual and real, undertaken by the firms in state of financial distress. This study uniquely examines the issue in the presence of earnings-increasing earnings management motivation- meeting earnings benchmark of avoiding losses. For examining the issue, this study analyzes large panel data of Indian public companies for the period 2000–2016. The findings indicate prevalence of earnings-decreasing real earnings management practices, that is, decrease in overproduction and increase in spending on discretionary expenses, in financially distressed firms despite there being motivation to increase earnings to avoid losses. No evidence of accrual earnings management practices has been observed in such firms.

scholarly journals Predicting Firms’ Financial Distress: An Empirical Analysis Using the F-Score Model

2021 ◽  
Vol 14 (5) ◽  
pp. 199
Author(s):  
Mahfuzur Rahman ◽  
Cheong Li Sa ◽  
Md. Abdul KaiumMasud
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
The United States ◽  
Research Database ◽  
Negative Consequences ◽  
Return On Assets ◽  
Listed Firms ◽  
Distressed Firms ◽  
Score Model ◽  
Relationship Of

Financial performance of firms is very important to bankers, shareholders, potential investors, and creditors. The inability of firms to meet their liabilities will affect all its stakeholders and will result in negative consequences in the wider economy. The objective of the study is to explore the applicability of a distress prediction model which uses the F-Score and its components to identify firms which are at high risk of going into default. The study incorporates a prediction model and vast literature to address the research questions. The sample of the study is collected from publicly listed firms of the United States. In total, 81 financially distressed firms wereextracted from the UCLA-LoPucki Bankruptcy Research Database during 2009–2017. This study found that the relationship of the F-Score and probability of firms going into financial distress is significant. This study also demonstrated that firms which are at risk of distress tend to record a negative cash flow from operations (CFO) and showed a greater decline in return on assets (ROA) in the year prior to default. This study extends the existing literature by supporting a model which has not been widely used in the area of financial distress predictions.

Fixing the Core, Earnings Management, and Sustainable Emergence From Financial Distress: Evidence From China’s Special Treatment System

2021 ◽  
pp. 0148558X2110511
Author(s):  
Jiao Jing ◽  
Kenneth Leung ◽  
Jeffrey Ng ◽  
Janus Jian Zhang
Keyword(s):  
Earnings Management ◽  
Financial Distress ◽  
Operating Efficiency ◽  
Special Treatment ◽  
Multiple Stakeholders ◽  
Listed Firms ◽  
The Core ◽  
Distressed Firms ◽  
Core Earnings

Throughout their business life cycle, firms may experience financial distress. Successful emergence from such distress is important to their multiple stakeholders. Using a sample of publicly listed firms in China that emerged from Special Treatment (an indicator of delisting risk), we focus on the key actions such firms take prior to emergence, namely, fixing the core of the business and earnings management. We examine how these actions are associated with sustainable emergence, which we define as emergence from Special Treatment without reentry in the next 5 years. Consistent with the expectation that shortcut fixes to problems do not yield a long-term solution, we find that repairing the core of the business by improving operating efficiency is positively associated with sustainable emergence, whereas earnings management is negatively associated with it. We also find that the positive (negative) association between fixing the core (earnings management) and sustainable emergence is pronounced only for state-owned enterprises. Our article adds to the limited literature that examines issues related to distressed firms’ sustainable turnaround.

scholarly journals Financially Distressed Firms: Environmental, Social, and Governance Reporting in Indonesia

2021 ◽  
Vol 13 (18) ◽  
pp. 10156
Author(s):  
Iman Harymawan ◽  
Fajar Kristanto Gautama Putra ◽  
Bayu Arie Fianto ◽  
Wan Adibah Wan Ismail
Keyword(s):  
Financial Distress ◽  
General Information ◽  
Disclosure Quality ◽  
Exact Matching ◽  
Coarsened Exact Matching ◽  
Distressed Firms ◽  
Financially Distressed ◽  
Two Stages ◽  
Rolling Windows ◽  
The Relationship

This study examines the relationship between financial distress and environmental, social, and governance (ESG) disclosure. We hypothesize that financially distressed firms are tempted to enhance ESG disclosure as it provides higher performance in terms of financial and market perspectives. ESG disclosure needs substantial resources, which financially distressed firms may not be able to provide. In Indonesian settings, we find that financially distressed firms have lower ESG disclosure quality than non-distressed firms. Our results are robust due to lagged variable, Heckman’s two stages, and coarsened exact matching regression showing consistent results. Furthermore, our results are consistent with three years of rolling windows of financial distress and all sections of ESG reporting, except the general information section. This study extends the scope of prior studies by focusing on firms’ eagerness to provide higher quality ESG disclosure, particularly distressed firms.

scholarly journals Cash Flow Information And The Prediction Of Financially Distressed Mining, Oil And Gas Firms: A Comparative Study

2011 ◽  
Vol 10 (3) ◽  
pp. 78 ◽  
Author(s):  
Terry J. Ward
Keyword(s):  
Comparative Study ◽  
Cash Flow ◽  
Financial Distress ◽  
Oil And Gas ◽  
Cash Flows ◽  
Distressed Firms ◽  
Flow Information ◽  
Financially Distressed ◽  
Oil And Gas Firms

<span>This study tests whether cash flow information is more useful to creditors in predicting financially distressed mining, oil and gas firms than it is in predicting financial distress in other industries. The results of this study suggest that cash flows are more useful to creditors in predicting financially distressed mining, oil and gas firms than they are predicting financially distressed firms in other industries. Results also show that different cash flows are useful in predicting financial distressed mining, oil and gas firms than are useful in predicting financially distressed control firms.</span>

scholarly journals Application of Altman Bankruptcy prediction model in Ghana

2019 ◽  
Vol 7 (1) ◽  
pp. 63-72
Author(s):  
Alhassan Musah ◽  
Josephine Agyimaa Agyirakwah
Keyword(s):  
Stock Exchange ◽  
Final Analysis ◽  
Bankruptcy Prediction ◽  
Financial Statement ◽  
Z Score ◽  
Listed Firms ◽  
Distressed Firms ◽  
Financially Distressed ◽  
Score Model ◽  
Ghana Stock Exchange

The study examined the applicability of the Altman Z-score model in predicting bankrupt companies or financially distressed companies on the Ghana Stock Exchange. A sample 10 listed firms were selected and one other company to be used for validation purposes. The validation process involved data for 2016 and 2017 for Aluworks which represented a distressed company and GOIL Ghana Limited which represented non-distressed company. The final analysis was based on a random sample of 10 listed firms using their 2017 financial statement. The results of the initial prediction showed 50 percent of the companies were correctly predicted whiles the others were misclassified. Additional analyses showed that the size of the company influence the probability of bankruptcy whiles the nature of the business does not. The conclusion drawn shows that the Altman Z-score cannot accurately predict financially distressed firms in Ghana but can still be useful in giving signals.

scholarly journals Is The Scaling Measure Used For Cash Flows Important In Predicting Financially Distressed Firms?

2011 ◽  
Vol 9 (4) ◽  
pp. 134 ◽  
Author(s):  
Terry J. Ward
Keyword(s):  
Cash Flow ◽  
Financial Distress ◽  
Predictive Ability ◽  
Cash Flows ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Distressed Firms ◽  
Financially Distressed ◽  
Statement Of Cash Flows ◽  
Operating Activities

This paper attempts to determine whether the measure used to scale the three net cash flows reported on a statement of cash flows affects binary financial distress prediction results. The results of this study suggest that the scaling measure used does affect the incremental predictive ability of each cash flow. Results indicate that tone should scale cash flow from operating activities by current assets, cash flow from investing activities by sales, and cash flow from operating activities by owners equity.

scholarly journals PREDICTING FINANCIAL DISTRESS IN THE MALAYSIAN MARKET: HAZARD MODEL VERSUS THE LOGIT MODEL

2021 ◽  
Vol 3 (7) ◽  
pp. 01-19
Author(s):  
Ahmad Harith Ashrofie Hanafi ◽  
Rohani Md-Rus ◽  
Kamarun Nisham Taufil Mohd
Keyword(s):  
Logistic Regression ◽  
Stock Market ◽  
Financial Distress ◽  
Logit Model ◽  
Hazard Model ◽  
Financial Ratios ◽  
Logit Models ◽  
Listed Firms ◽  
Distressed Firms ◽  
Model Versus

The increasing numbers of financially distressed firms in the Malaysian market demonstrate the importance of predicting financial distress among firms in Malaysia. Using firm financial ratios, this study focuses on predicting financial distress using the hazard model and logistic regression (logit model) based on the Malaysian market. This study used listed firms on the Malaysian stock market from 2000 to 2018 to create two sets of data comprising the main sample and holdout sample in order to compare the predictability between hazard and logit models. The results clearly show that the hazard model is better compared to the logit model in predicting financial distress for the Malaysian market since more variables were found to be significant in addition to the model being more consistent in terms of accuracy.

An Analysis of Financial Distress Prediction of Selected Listed Companies in Colombo Stock Exchange

2021 ◽  
Vol 13 (2) ◽  
pp. 48-70
Author(s):  
Kennedy Degaulle Gunawardana
Keyword(s):  
Neural Network ◽  
Financial Distress ◽  
Stock Exchange ◽  
Sri Lankan ◽  
Cross Sectional ◽  
Listed Firms ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Distressed Firms ◽  
One Year

The main objective of the study is to predict financial distress and developing a prediction model using accounting related variables in selected listed firms in Sri Lanka. Decision criteria for financial distress has been selected based on the existing literature on financial distress prediction applicable to the Sri Lankan firms. A sample of 22 financially distressed firms along with 33 financially non-distressed firms have been used to conduct this study. Artificial neural network was used as the basic approach to the study in predicting financial distress. A neural network to predict financial distress was developed with an accuracy of 85.7% one year prior to its occurrence. The second analysis conducted was the panel regression considering five years of cross-sectional data for the sample of companies selected. This analysis was able to identify a significant relationship of leverage, price-to-book ratio and Tobin's Q ratio to the prediction of financial distress of a firm.

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