BANKRUPTCY DISCRIMINATION WITH REAL VARIABLES

This paper reconsiders the accepted usage of nondeflated financial ratios in statistical models to differentiate between failed and nonfailed firms. Non-deflated ratios are hypothesized to inadequately reflect inter-temporal macroeconomic fluctuations that affect the ability of firm's to survive. Using a sample of 124 oil and gas companies between the period 1982–1988, the going concern assumption is evaluated with statistical logit models using either nondeflated or deflated financial ratios. Deflated company ratios are created by transforming data with price indices or by creating market value ratios. Empirical results suggest that a superior bankruptcy early warning model is developed for the oil and gas industry by creating real financial and reserve ratios and by introducing external factors, such as oil prices, interest rates and accounting method, as independent predictors. Overall classification accuracy is approximately 95 percent.     

The use of a bankruptcy forecasting model to analyze corporate behavior in Israel

Most models used in predicting business failure actually rank the firms involved according to the probability of their financial distress Such ranking may also be used to explain patterns of corporate behavior and economic developments, as is shown in this paper Israeli manufacturing corporations are ranked here according to a multivanate index of risk based on five financial ratios that have been shown to successfully predict financial distress three years prior to the actual failure. Greater use of supplier credit and recourse to more than one bank are significantly linked to a higher degree of risk as measured by the index Extension of customer credit is conversely related to this risk status of the firm, while there does not seem to be a clear and significant relationship between such status and the growth in sales.     

Foreign affiliates of the world largest financial groups: Locations and governance

This paper has two objectives. The first is the documentation of the relative importance of the largest financial groups in the world. The list of top 50 financial groups ranked by total assets in 2003 is compared to a similar list available for 1986. The second objective is to examine where financial firms are expanding their operations and to document some of the factors that may explain the most-favoured locations of these financial groups.The results of this study have two important implications. First, they indicate that location-specific advantages such as size, human capital and cultural distance, do provide an explication of the internationalization of financial firms. Second, they show that good governance has a strong impact on the choice of countries by firms.     

METHODOLOGICAL IMPLICATIONS OF NON-NORMALLY DISTRIBUTED FINANCIAL RATIOS

Empirical studies have shown distributions of financial ratios are skewed. An explanation for this is given and it is argued that in such circumstances comparison of a fmancial ratio with some norm (e.g. industry average) is likely to misinform. It is also shown that where financial ratios are used as inputs to statistical models normality is irrelevant but a method of transformation into a normal distribution is provided whereby original interrelationships are preserved. Finally, because of the inadequacies of financial ratios, it is shown how regression analysis may be used in financial statement analysis.     

Using partial least square discriminant analysis to distinguish between Islamic and conventional banks in the MENA region

The deterioration of bank profitability poses a threat not only to the interests of consumers and internal staff members but also affects investors who may equally suffer from significant financial losses. It is important to establish an effective system which assists investors in their investment choices. In prior literature, traditional models have been developed, but achieved short‐term performances such as logistic regression and discriminant analysis. This paper applies a partial least squares discriminant analysis (PLS‐DA) to distinguish between conventional and Islamic banks in the Middle East and North Africa (MENA) region based on the financial information for the period 2005–2011. This method can successfully identify the non‐linearity and correlations between financial indicators. The results demonstrate superior performance of the proposed method. On one hand, our model can select all financial ratios to distinguish between banks and at the same time identify the most important variables in the distinction process. On the other hand, the proposed model has high levels in terms of accuracy and stability.     

Industry Distributional Characteristics of Financial Ratios: An Acquisition Theory Application

This study explores the importance of capturing industry‐specific distributional characteristies in analyses based on financial ratios. As a test case, the study replicates Palepu (1986), who employs financial ratios in logit models to investigate the usefulness of six acquisition hypotheses in predicting takeover targets. Without adjustment for industry‐specific distributional characteristics, this study's findings are only consistent with one of the six acquisition hypotheses. After adjusting for distributional properties, the results are consistent with four of the six acquisition hypotheses. Furthermore, the adjusted model produces a classification accuracy significantly greater than chance, as well as significantly greater than that observed for the unadjusted model.     

Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach

We develop a state-of-the-art fraud prediction model using a machine learning approach. We demonstrate the value of combining domain knowledge and machine learning methods in model building. We select our model input based on existing accounting theories, but we differ from prior accounting research by using raw accounting numbers rather than financial ratios. We employ one of the most powerful machine learning methods, ensemble learning, rather than the commonly used method of logistic regression. To assess the performance of fraud prediction models, we introduce a new performance evaluation metric commonly used in ranking problems that is more appropriate for the fraud prediction task. Starting with an identical set of theory-motivated raw accounting numbers, we show that our new fraud prediction model outperforms two benchmark models by a large margin: the Dechow et al. logistic regression model based on financial ratios, and the Cecchini et al. support-vector-machine model with a financial kernel that maps raw accounting numbers into a broader set of ratios.     

Adoption of IFRS in Spain: Effect on the comparability and relevance of financial reporting

EU Regulation 1606/2002 requires application of International Financial Reporting Standards (IFRS) by groups listed on European stock markets. In Spain, listed groups are now obliged to prepare consolidated financial information under IFRS, and legislative changes to bring local rules into line with international standards have been tabled.In this context, the potential impact of IFRS is fraught with uncertainty. Our study of IBEX-35 companies focuses on the effects of the new standards on comparability and the relevance of financial reporting in Spain. We address these objectives by seeking significant differences between accounting figures and financial ratios under the two sets of standards (i.e. Spanish accounting standards and IFRS).The results obtained show that local comparability has worsened. The study reveals that local comparability is adversely affected if both IFRS and local accounting standards are applied in the same country at the same time. Reforms to bring local rules into line with international standards are therefore urgent. We also find that there has been no improvement in the relevance of financial reporting to local stock market operators because the gap between book and market values is wider when IFRS are applied. While there has been no gain in terms of the usefulness of financial reporting in the short-term, improved usefulness may be achieved in the medium to long-term.     

Using accounting ratios to distinguish between Islamic and conventional banks in the GCC region

This study determines whether it is possible to distinguish between conventional and Islamic banks in the Gulf Cooperation Council (GCC) region on the basis of financial characteristics alone. Islamic banks operate under different principles, such as risk sharing and the prohibition of interest, yet both types of banks face similar competitive conditions. The combination of effects makes it unclear whether financial ratios will differ significantly between the two categories of banks. We input 26 financial ratios into logit, neural network, and k-means nearest neighbor classification models to determine whether researchers or regulators could use these ratios to distinguish between the two types of banks. Although the means of several ratios are similar between the two categories of banks, non-linear classification techniques (k-means nearest neighbors and neural networks) are able to correctly distinguish Islamic from conventional banks in out-of-sample tests at about a 92% success rate.     

Is Polaris a HOG? A Case Study*

The main usefulness of a general purpose financial statement centers on its comparability to the financials produced by an entity's competition. This case works for both undergraduate and graduate students because it offers comparisons between two well known recreation industry companies. Financial statement analysis is a lesson which serves as an appropriate capstone to financial accounting education. Analyzing the financial statements of competing entities explains “why” accountants must implement the intricate “how” which produces the statements and the resulting decision models such as ratios and common‐sized statements. Compared to previous generations, current students will be more responsible for managing their own retirement funds. This case can help students start to appreciate investment analysis by providing enough detail for any level student to conduct financial statement evaluations that make comparisons to find successful fundamental business strategies.