Failure prediction of Chinese A-share listed companies : comparisons using logistic regression model and neural network analysis : a thesis presented in partial fulfilment of the requirements for the degree of Master of Business Studies in Finance at Massey University, Palmerston North, New Zealand

This study compares the relative prediction accuracy of corporate failure between two prediction methods –logistic regression model and neural network analysis– based on a sample of 3598 observations and companies data obtained from the Chinese A- Share market during the period 1991 to 2002. Seven criteria have been set up to define failure according to attributes of Chinese listed companies. Using forty financial ratios and seven misclassification cost ratios of Type I and Type II error, two models achieve ranges of minimal misclassification cost at optimal cut-off points for two years prior to business failure; The logistic regression model is slightly superior to neural network analysis. Compared with random prediction, both models are efficient. In addition, the study points out that Total Asset Turnover (TATR), Cash Ratio (CASR), Earning per Share (EPS), Total Debt to Total Asset (TDTA), Return on Assets (ROA) and the natual log of Total Market Value (MVLN) could be significant financial indictors of corporate failure. Results of the study have important implications in credit evaluation, internal risk control and capital market investment guidelines

The relationships between corporate meeting planner's personality traits and their choices of meeting places

This study is to determine the influence of personality on the novelty preference for corporate meeting destination choice. The Big-Five model of personality which consists of five traits namely openness, conscientiousness, extraversion,agreeableness, and neuroticism was employed to operationalise the personality construct. A total of 75 corporate meeting planners drawn from public listed service organisations were involved. The main method of data collection was questionnaire survey and multiple regression analysis was employed as the main statistical analysis. The results revealed that only openness (positively) and agreeableness (negatively) contributed significantly to the prediction of novelty preference for corporate meeting destination choice. This study, which also seeks to determine the relationships between some demographical variables and novelty preference, found that demographical information is not a good predictor of meeting destination choice. The main implication of this study is pertaining to the segmentation and targeting of the corporate meeting market. This study also helps in bridging the gap between tourism marketing and organisational research

Design Analysis and Implementation of Stock Market Forecasting System using Improved Soft Computing Technique

In this paper, a stock market prediction model was created utilizing artificial neural networks. Many people nowadays are attempting to predict future trends in bonds, currencies, equities, and stock markets. It is quite challenging for a capitalist and an industry to forecast changes in stock market prices. Due to the numerous economic, political, and psychological aspects at play, forecasting future value changes on the stock markets is quite challenging. In addition, stock market forecasting is a difficult endeavor because it relies on a wide range of known and unknown variables. Many approaches, including technical analysis, fundamental analysis, time series analysis, and statistical analysis are used to attempt to predict the share price; however, none of these methods has been demonstrated to be a consistently effective prediction tool. Artificial neural networks (ANNs), a subfield of artificial intelligence, are one of the most modern and promising methods for resolving financial issues, such as categorizing corporate bonds and anticipating stock market indexes and bankruptcy (AI). Artificial neural networks (ANN) are a prominent technology used to forecast the future of the stock market. In order to understand financial time series, it is often essential to extract relevant information from enormous data sets using artificial neural networks. An outcome prediction neural network with three layers is trained using the back propagation method. Analysis shows that ANN outperforms every other prediction technique now available to academics in terms of stock market price predictions. It is concluded that ANN is a useful technique for predicting stock market movements globally

Firm’s Characteristics as Predictors of Corporate Failure: Evidence from UK Companies using Logistic Regression

Analysis of credit risk and increased competition in financial market has improved the motivation of a prediction model of corporate failure. However, there are fewer researches dependent on UK companies. This paper builds up a prediction model of corporate by investigating firm’s characteristics. These characteristics include financial ratios and firm’s size. All financial data used in this study is collected from UK manufacturing companies over five years from 2003 to 2007. Based on the empirical findings of logistic regression, probability ratios, liquidity ratios and firm’s size are estimated to have significant impacts on prediction of corporate failure. In fact, if there are increases of these ratios, the probability of corporate failure will decrease. The predictive power of this logistic analysis is high though there are some limitations. Keywords: Corporate Failure, Financial Ratios, Logistic Regressio

PENGARUH CORPORATE GOVERNANCE, PROFITABILITAS, LEVERAGE, LIKUIDITAS, DAN OPERATING CAPACITY TERHADAP PREDIKSI FINANCIAL DISTRESS (Studi Empiris pada Perusahaan Manufaktur yang Terdaftar di BEI Tahun 2012-2014)

Financial distress is the decline stage of the company's financial condition that occurs prior to the bankruptcy. This study aims to determine the effect of corporate governance, profitability, leverage, liquidity, and operating capacity on the financial distress prediction on manufacturing companies listed in Indonesia Stock Exchange in 2012-2014. This is done as a warning to companies experiencing financial distress. Data used in this research is secondary data obtained from the Indonesian Capital Market Directory (ICMD) and IDX. The method used for the determination is purposive sampling method, in order to obtain a sample of 176 companies, which are experiencing financial distress of 15 companies and non financial distress of 161 companies. Technique of analysis data used technique of logistic regression analysis. Based on the results of the research showed that institutional ownership and profitability the effect on the prediction of financial distress. While the independent commissioner, audit committee, leverage, liquidity, and operating capacity has no effect on the prediction of financial distress. Keywords : financial distress, corporate governance, financial ratios

Detecting Fraud in Chinese Listed Company Balance Sheets

This study investigates the links between accounting values in Chinese listed companies’ balance sheets and the exposure of their fraudulent activities. Every balance sheet account is proposed to be a potential vehicle to manipulate financial statements. Other receivables, inventories, prepaid expenses, employee benefits payables and long-term payables are important indicators of fraudulent financial statements. These results confirm that asset account manipulation is frequently carried out and cast doubt on earlier conclusions by researchers that inflation of liabilities is the most common source of financial statement manipulation. Prior practices of solely scaling balance sheet values by assets are revealed to produce spurious relationships, while scaling by both assets and sales effectively detects fraudulent financial statements and provides a useful fraud prediction tool for Chinese auditors, regulators and investors

Financial-distress prediction of Islamic banks using tree-based stochastic techniques

Purpose Financial distress is a socially and economically important problem that affects companies the world over. Having the power to better understand – and hence aid businesses from failing, has the potential to save not only the company, but also potentially prevent economies from sustained downturn. Although Islamic banks constitute a fraction of total banking assets, their importance have been substantially increasing, as their asset growth rate has surpassed that of conventional banks in recent years. The paper aims to discuss these issues. Design/methodology/approach This paper uses a data set comprising 101 international publicly listed Islamic banks to work on advancing financial distress prediction (FDP) by utilising cutting-edge stochastic models, namely decision trees, stochastic gradient boosting and random forests. The most important variables pertaining to forecasting corporate failure are determined from an initial set of 18 variables. Findings The results indicate that the “Working Capital/Total Assets” ratio is the most crucial variable relating to forecasting financial distress using both the traditional “Altman Z-Score” and the “Altman Z-Score for Service Firms” methods. However, using the “Standardised Profits” method, the “Return on Revenue” ratio was found to be the most important variable. This provides empirical evidence to support the recommendations made by Basel Accords for assessing a bank’s capital risks, specifically in relation to the application to Islamic banking. Originality/value These findings provide a valuable addition to the limited literature surrounding Islamic banking in general, and FDP pertaining to Islamic banking in particular, by showcasing the most pertinent variables in forecasting financial distress so that appropriate proactive actions can be taken. </jats:sec

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

Aggregate economy risk and company failure: An examination of UK quoted firms

Considerable attention has been directed in the recent finance and economics literature to issues concerning the effects on company failure risk of changes in the macroeconomic environment. This paper examines the accounting ratio-based and macroeconomic determinants of insolvency exit of UK large industrials during the early 1990s with a view to improve understanding of company failure risk. Failure determinants are revealed from estimates based on a cross-section of 369 quoted firms, which is followed by an assessment of predictive performance based on a series of time-to-failure-specific logit functions, as is typical in the literature. Within the traditional for cross-sectional data studies framework, a more complete model of failure risk is developed by adding to a set of traditional financial statement-based inputs, the two variables capturing aggregate economy risk - one-year lagged, unanticipated changes in the nominal interest rate and in the real exchange rate. Alternative estimates of prediction error are obtained, first, by analytically adjusting the apparent error rate for the downward bias and, second, by generating holdout predictions. More complete, augmented with the two macroeconomic variables models demonstrate improved out-ofestimation- sample classificatory accuracy at risk horizons ranging from one to four years prior to failure, with the results being quite robust across a wide range of cut-off probability values, for both failing and non-failed firms. Although in terms of the individual ratio significance and overall predictive accuracy, the findings of the present study may not be directly comparable with the evidence from prior research due to differing data sets and model specifications, the results are intuitively appealing. First, the results affirm the important explanatory role of liquidity, gearing, and profitability in the company failure process. Second, the findings for the failure probability appear to demonstrate that shocks from unanticipated changes in interest and exchange rates may matter as much as the underlying changes in firm-specific characteristics of liquidity, gearing, and profitability. Obtained empirical determinants suggest that during the 1990s recession, shifts in the real exchange rate and rises in the nominal interest rate, were associated with a higher propensity of industrial company to exit via insolvency, thus indicating links to a loss in competitiveness and to the effects of high gearing. The results provide policy implications for reducing the company sector vulnerability to financial distress and failure while highlighting that changes in macroeconomic conditions should be an important ingredient of possible extensions of company failure prediction models