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 Financial Distress Prediction Through Cash Flow Ratios Analysis

2019 ◽  
Vol 10 (3) ◽  
pp. 63 ◽  
Author(s):  
Amrizah Kamaluddin ◽  
Norhafizah Ishak ◽  
Nor Farizal Mohammed
Keyword(s):  
Cash Flow ◽  
Financial Distress ◽  
Cash Flows ◽  
Efficiency Ratio ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Statement Of Cash Flows ◽  
Relationship Of ◽  
Solvency Ratio ◽  
The Relationship

The purpose of this study to examine the relationship of cash flow ratios in predicting financial distress companies, with industrial and consumer product companies in Bursa Malaysia as the sample. The study on financial distress is critical as it can lead to bankruptcy, which may adversely affect the economy of the country. Therefore it is worth exploring any indicators that can identify the possibility of financial distress in the company. The tools enable to address the potential problems that can mitigate from distressed financial position.  Most prior studies in Malaysia focus on traditional financial ratios, while this study exploits the strength of cash flow ratios. The liquidity ratio, solvency ratio, efficiency ratio and profitability ratio utilized in this study are derived from the statement of cash flows. The Altman Z-score is used to measure the level of the financial distress. The findings show mixed relationships between solvency ratio and financial distress and a negative significant relationship between profitability ratio and financial distress, whilst efficiency ratio has no relationship with the financial distress. These results suggest that cash flow ratios are reliable tools to predict financial distress for Malaysian context. The study is useful in giving insights to the stakeholders in their decision making.

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 Financial Distress Prediction Using Hybrid Machine Learning Techniques

2020 ◽  
pp. 1-12
Author(s):  
Suduan Chen ◽  
Zong-De Shen
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Financial Distress ◽  
Machine Learning Techniques ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Learning Techniques ◽  
Distress Prediction ◽  
Hybrid Machine ◽  
Financially Distressed

The purpose of this study is to establish an effective financial distress prediction model by applying hybrid machine learning techniques. The sample set is 262 financially distressed companies and 786 non-financially distressed companies, listed on the Taiwan Stock Exchange between 2012 and 2018. This study deploys multiple machine learning techniques. The first step is to screen out important variables with stepwise regression (SR) and the least absolute shrinkage and selection operator (LASSO), followed by the construction of prediction models, as based on classification and regression trees (CART) and random forests (RF). Both financial variables and non-financial variables are incorporated. This study finds that the financial distress prediction model built with CART and variables screened by LASSO has the highest accuracy of 89.74%.

scholarly journals The impacts of free cash surplus flows, audit quality and ownership on earnings management: The Jordan case

2018 ◽  
Vol 15 (4-1) ◽  
pp. 222-230 ◽  
Author(s):  
Ahmed Mushref Salim Al-Omush ◽  
Ali Mohammad Al-Attar ◽  
Walid Muhammad Masadeh
Keyword(s):  
Earnings Management ◽  
Cash Flow ◽  
Financial Distress ◽  
Audit Quality ◽  
Listed Companies ◽  
Cash Flows ◽  
Fund Flows ◽  
Mixed Support ◽  
Cash Flow Statement ◽  
Statement Of Cash Flows

This paper primarily aims to identify and evaluate the effect of Free Cash Surplus flows, Audit Quality and the ownership on Earnings Management. The study shows that financial distress has a significant impact on earnings management for samples on the Jordanian listed companies during (2003-2016). The Cash Flow Statement provides information on the flow of cash in and out of the organization over a specific period. It shows how an organization spends its money (cash outflows) as well as the source of the money (cash inflows). The Cash Flow Statement – additionally alluded to as the statement of cash flows or fund flows, which is one of the financial statements that is often utilized in the measurement of an organization’s financial performance and overall wellbeing. The study also investigates the prevalence of both accrual and base earnings management for the empirical corporate finance which claims that the better corporate governance constraints between earnings management and the relation of high free-cash -flows firms the more will the increase will be at the income management and the earnings management. Although, the research has addressed the issues of earnings management and the real activities handling; this research paper put these two issues together. The analysis provides a mixed support when using different earnings management detection models. The findings of this study could serve as a guideline to a proper and understanding of earnings management to public listed companies, regulators, and various stakeholders

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.

Modeling corporate financial distress using financial and non-financial variables

2019 ◽  
Vol 61 (3/4) ◽  
pp. 457-484
Author(s):  
Senthil Arasu Balasubramanian ◽  
Radhakrishna G.S. ◽  
Sridevi P. ◽  
Thamaraiselvan Natarajan
Keyword(s):  
Financial Distress ◽  
Confusion Matrix ◽  
Predictive Ability ◽  
Test Sample ◽  
Business Failure ◽  
Content Type ◽  
Financial Variables ◽  
Institutional Holdings ◽  
Financial Distress Prediction ◽  
Distress Prediction

Purpose This paper aims to develop a corporate financial distress model for Indian listed companies using financial and non-financial parameters by using a conditional logit regression technique. Design/methodology/approach This study used a sample of 96 companies, of which 48 were declared sick between 2014 and 2016. The sample was divided into a training sample and a testing sample. The variables for the study included nine financial variables and four non-financial variables. The models were developed using financial variables alone as well as combining financial and non-financial variables. The performance of the test sample was measured with confusion matrix, sensitivity, specificity, precision, F-measure, Types 1 and 2 error. Findings The results show that models with financial variables had a prediction accuracy of 85.19 and 86.11 per cent, whereas models with a combination of financial and non-financial variables predict with comparatively better accuracy of 89.81 and 91.67 per cent. Net asset value, long-term debt–equity ratio, return on investment, retention ratio, age, promoters holdings pledged and institutional holdings are the critical financial and non-financial predictors of financial distress. Originality/value This study contributes to the financial distress prediction literature in different ways. First, there have been, until now, few studies in the area of financial distress prediction in the Indian context. Second, business failure studies in the past have used only financial variables. The authors have combined financial and non-financial variables in their model to increase predictive ability. Thirdly, in most earlier studies, variable institutional holdings were found to affect financial distress negatively. In contrast, the authors found this parameter to be positively significant to the financial distress of the company. Finally, there have hitherto been few studies that have used promoter holdings pledged (PHP) or pledge ratio. The authors found this variable to influence business failure positively.

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.

Analyzing the figures of consolidated statement of cash flows of the world's biggest publicly traded oil and gas corporations

2020 ◽  
Vol 19 (6) ◽  
pp. 1101-1120
Author(s):  
O.V. Shimko
Keyword(s):  
Public Sector ◽  
Cash Flow ◽  
Oil And Gas ◽  
Cash Flows ◽  
Annual Reports ◽  
Capital Expenditures ◽  
Publicly Traded ◽  
The Public ◽  
Statement Of Cash Flows ◽  
Oil And Gas Companies

Subject. The article investigates key figures disclosed in consolidated cash flow statements of 25 leading publicly traded oil and gas companies from 2006 to 2018. Objectives. The focus is on determining the current level of values of the main components of consolidated statement of cash flows prepared by leading publicly traded oil and gas companies, identifying key trends within the studied period and factors that led to any transformation. Methods. The study draws on methods of comparative and financial-economic analysis, as well as generalization of materials of consolidated cash flow statements. Results. The comprehensive analysis of annual reports of 25 oil and gas companies enabled to determine changes in the key figures and their relation in the structure of consolidated cash flow statements in the public sector of the industry. It also established main factors that contributed to the changes. Conclusions. In the period under study, I revealed an increase in cash from operating activities; established that capital expenditures in the public sector of the industry show an overall upward trend and depend on the level of oil prices. The analysis demonstrated that even integrated companies’ upstream segment prevail in the capital expenditures structure. The study also unveiled an increase in dividend payments, which, most of the time, exceeded free cash flows thus increasing the debt burden.

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