An Empirical Study on Predicting Financial Risk of Listed Companies

2010 ◽  
Vol 108-111 ◽  
pp. 1267-1271 ◽  
Author(s):  
Yan Li Chen ◽  
Li Hui Chen
Keyword(s):  
Financial Crisis ◽  
Financial Reporting ◽  
Early Warning ◽  
Financial Risk ◽  
Listed Companies ◽  
Financial Ratios ◽  
Growth Ratio ◽  
Revenue Growth ◽  
Discriminant Model ◽  
Set Up

Financial crisis early warning analysis is a matter of grave social and economic concern. It is important for enterprises, commercial banks and various investors. This is an exploratory study to determine if financial ratios of crisis companies differ from those of no crisis companies. The crisis firms (n=63) were then matched with no crisis firms on the basis of firm size, time period, and industry. Using this matched-pairs design, choose 63 listed companies, which are marked ST companies because of abnormal financial standing in Shanghai and Shenzhen in 2006, form the financial crisis samples, and choose some similar sized listed companies in same industry as matching samples, Taking the index of property liabilities ratio, audit opinion, finance lever ratio, gross property net profit ratio, sales revenue growth ratio and cash flux to current liability ratio as the final variants, set up the discriminant model by Fisher’ coefficient, conduct the case analysis of financial crisis early warning. These results provide empirical evidence of the limited ability of financial ratios to detect and predict crisis financial reporting.

Research on financial risk early warning of listed companies based on neural network

2021 ◽  
Author(s):  
Wenqi Zhao ◽  
Jinyi Li ◽  
Junwei Wang ◽  
Wenjing Zhao
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Financial Risk ◽  
Listed Companies

scholarly journals Institutional Shareholdings and Financial Characteristics of Malaysian Listed Companies

2020 ◽  
Author(s):  
Shamsul Naharabdullah ◽  
Mohd Azlan Yahya ◽  
Faisol Elham
Keyword(s):  
Institutional Investors ◽  
Listed Companies ◽  
Financial Ratios ◽  
Short Term ◽  
Growth Ratio ◽  
Primary Motivation ◽  
The Mean ◽  
Financial Characteristics ◽  
Institutional Shareholders

This study attempts to investigate the extent to which the financial characteristics of firms are related to institutional shareholdings. The primary motivation to carry out the study comes from an earlier paper by Hessel and Norman (1992), which showed that seven financial ratios discriminated between strongly-held and institutionally-neglected firms. As an extension of the study, the present study seeks to investigate the seven financial ratios among Malaysian companies by identifying differences in the means of the seven ratios between a group of companies with substantial institutional shareholdings against another group of companies with negligible institutional shareholdings. The findings, from a sample of KLSE listed companies, broadly support the findings by Hessel and Norman (1992), in which firms with significant institutional shareholdings exhibited a significantly higher profitability ratio against firms that were neglected by institutional investors.. This suggested that institutional investors placed greater emphasis on a firm's short-term results. Our evidence also did not indicate institutional shareholders' direct involvement in ensuring a firm's long-term growth and competitiveness, as shown by the insignificant differences in the mean of growth ratio between firms that had significant institutional shareholdings and those that were neglected by institutional investors.  

The extraction of early warning features for the predicting financial distress based on XGboost model and shap framework

2021 ◽  
pp. 2141004
Author(s):  
He Yang ◽  
Emma Li ◽  
Yi Fang Cai ◽  
Jiapei Li ◽  
George X. Yuan
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Financial Distress ◽  
Prediction Accuracy ◽  
Financial Risk ◽  
Learning Algorithm ◽  
Listed Companies ◽  
Gradient Boosting ◽  
Distress Risk ◽  
Extreme Gradient Boosting

The purpose of this paper is to establish a framework for the extraction of early warning risk features for the predicting financial distress based on XGBoost model and SHAP. It is well known that the way to construct early warning risk features to predict financial distress of companies is very important, and by comparing with the traditional statistical methods, though the data-driven machine learning for the financial early warning, modelling has a better performance in terms of prediction accuracy, but it also brings the difficulty such as the one the corresponding model may be not explained well. Recently, eXtreme Gradient Boosting (XGBoost), an ensemble learning algorithm based on extreme gradient boosting, has become a hot topic in the area of machine learning research field due to its strong nonlinear information recognition ability and high prediction accuracy in the practice. In this study, the XGBoost algorithm is used to extract early warning features for the predicting financial distress for listed companies, with 76 financial risk features from seven categories of aspects, and 14 non-financial risk features from four categories of aspects, which are collected to establish an early warning system for the predication of financial distress. With applications, we conduct the empirical testing respect to AUC, KS and Kappa, the numerical results show that by comparing with the Logistic model, our method based on XGBoost model established in this paper has much better ability to predict the financial distress risk of listed companies. Moreover, under the framework of SHAP (SHAPley Additive exPlanations), we are able to give a reasonable explanation for important risk features and influencing ways affecting the financial distress visibly. The results given by this paper show that the XGBoost approach to model early warning features for financial distress does not only preform a better prediction accuracy, but also is explainable, which is significant for the identification of early warning to the financial distress risk for listed companies in the practice.

Determinants of Internet Financial Reporting Practices: Evidence from the UAE

2013 ◽  
Vol 16 (04) ◽  
pp. 1350026 ◽  
Author(s):  
Hela Miniaoui ◽  
Peter Oyelere
Keyword(s):  
Financial Reporting ◽  
Listed Companies ◽  
Policy Makers ◽  
Reporting Practices ◽  
Insurance Sector ◽  
Industry Sector ◽  
Monetary Authorities ◽  
Depth Study ◽  
Set Up ◽  
Insight Into

The objective of this paper is to undertake an in-depth study of the Internet financial reporting (IFR) practices of UAE-listed companies. The survey is aimed at identifying IFR versus non-IFR companies, and the nature and extent of their IFR practices. Logistic regression analysis was undertaken to establish the determinants of IFR by the companies.The findings of this study identify the size, the leverage, industry sector, and profitability as the most important predictors of IFR adoption by UAE listed-companies. Larger companies with greater leverage are more likely to set up a website and use it for IFR than smaller less leveraged ones. It was noticed that 62% of IFR companies is from banking, investment & finance sector and from insurance sector as well.This study is expected to inform monetary authorities on this subject and give more support to these two industries, as they were peers for other sectors in the UAE. Furthermore, the outcome of this paper will provide policy-makers with insight into the factors that motivate IFR among corporate organizations.

The Research on Long-Term Financial Early-Warning System of Listed Companies

2013 ◽  
Vol 670 ◽  
pp. 216-221 ◽  
Author(s):  
Wei Ming Mou ◽  
Shui Bin Gu
Keyword(s):  
Financial Crisis ◽  
Early Warning ◽  
Early Warning System ◽  
Stock Exchange ◽  
Warning System ◽  
Listed Companies ◽  
Special Treatment ◽  
Warning Model ◽  
First Time

The article takes listed companies as research samples. Firstly, it selects 36 ST or *ST companies listed in Shanghai and Shenzhen Stock Exchange Market, who received special treatment during 2007 to 2009 for the first time and it also chooses another 36 normal companies as paired ones. Then, after using Factor analysis for identifying indexes, the paper go on with utilizing logistic to structure a financial long-term warning model. To verify the effectiveness of the model, the paper selects another 12 financial crisis companies and 12 financial fit companies to test. The results come out to show that establishing an effective long-term financial early-warning system helps enterprises to avoid financial crisis.

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