HOW TO USE EVA IN THE OIL AND GAS INDUSTRY

The use of EVA in the oil industry has lagged behind that in most other industries because the accounting information reported by oil and gas concerns does such a poor job of representing management's effectiveness in adding value for shareholders. The essence of the problem is that the exploration activities of oil companies create assets whose changes in value are recognized by the stock market long before they are reflected on income statements or balance sheets. As a result, all accountingbased performance measures, including generic measures of EVA (which are derived from accounting information), fail to provide meaningful goals, decision tools, or compensation benchmarks.
This article provides a new, EVAbased framework for performance measurement and incentive compensation for oil and gas firms—and for companies in extractive industries in general. The authors show that, when adjusted by a publicly available measure of hydrocarbon reserve value known as "SEC-10," EVA's ability to explain annual stock returns rises from under 10% to almost 50%. Moreover, because SEC-10 has several important limitations as a measure of reserve value, there is considerable additional room for improving EVA's explanatory power. And the actual implementation of an EVA financial management system for an individual oil company can and should be based on more precise estimates of reserve value than those provided by SEC-10.
To this end, the authors provide an approach to hydrocarbon reserve valuation that captures the "real option" value of undeveloped reserves. By incorporating real option values, this new EVA financial management system for oil companies aligns management's incentives with the goal of creating shareholder wealth by rewarding managers for creating real option value as well as current cash flow—and by forcing managers to consider the optimal "exercise" of such strategic options.     

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.     

Crude oil price volatility and equity return predictability: A comparative out-of-sample study

We evaluate the predictive power afforded by crude oil price volatility relative to widely used variables in the financial literature, such as the dividend yield, earnings-to-price ratio, the default yield spread as well several crude oil price-based variables. From a statistical viewpoint, predictions employing the suggested crude oil price volatility-based measures display a similar pattern as predictions using dividend ratios and interest rates, namely, they have relatively weak out-of-sample power. However, we find that gains in utility for an investor that uses predictions produced under the model employing crude oil price log-realized semivolatilities are statistically significant higher than an investor relying on predictions produced under the competitors as well as the historical average benchmark. We discuss and explain the reasons for our results. Overall, we argue that it is hard not to justify more attention to crude oil price semivolatilities relative to widely used financial and macroeconomic variables.     

Investigating the nature of interaction between crypto-currency and commodity markets

This paper investigates the dynamic relationship and volatility spillovers between cryptocurrency and commodity markets using different multivariate GARCH models. We take into account the nature of interaction between these markets and their transmission mechanisms when analyzing the conditional cross effects and volatility spillovers. Our results confirm the presence of significant returns and volatility spillovers, and we identify the GO-GARCH (2,2) as the best-fit model for modeling the joint dynamics of various financial assets. Our findings show significant dynamic linkages and volatility spillovers between gold, natural gas, crude oil, Bitcoin, and Ethereum prices. We find that gold can serve as a safe haven in times of economic uncertainty, as it is a good hedge against natural gas and crude oil price fluctuations. We also find evidence of bidirectional causality between crude oil and natural gas prices, suggesting that changes in one commodity's price can affect the other. Furthermore, we observe that Bitcoin and Ethereum are positively correlated with each other, but negatively correlated with gold and crude oil, indicating that these cryptocurrencies may serve as useful diversification tools for investors seeking to reduce their exposure to traditional assets. Our study provides valuable insights for investors and policymakers regarding asset allocation and risk management, and sheds light on the dynamics of financial markets.     

Predicting default of Russian SMEs on the basis of financial and non-financial variables

This article develops two models for predicting the default of Russian Small and Medium-sized Enterprises (SMEs). The most general questions that the article attempts to answer are ‘Can the default risk of Russian SMEs be assessed with a statistical model?’ and ‘Would it sufficiently demonstrate high predictive accuracy?’ The article uses a relatively large data set of financial statements and employs discriminant analysis as a statistical methodology. Default is defined as legal bankruptcy. The basic model contains only financial ratios; it is extended by adding size and age variables. Liquidity and profitability turned out to be the key factors in predicting default. The resulting models have high predictive accuracy and have the potential to be of practical use in Russian SME lending.     

Methodological issues in the use of financial ratios

It appears that the extensive use of financial ratios by both practitioners and researchers is often motivated by tradition and convenience rather than resulting from theoretical considerations or from a careful statistical analysis. Basic questions, such as: Is the control for firm size, a major objective of the ratio form, called for by the theory examined; what is the structural relationship between the examined variables and size; and what is the optimal way to control for industry-wide factors, are rarely addressed by users of financial ratios. The major purpose of this study is to discuss the conditions under which conventional tools, such as financial ratios and measures of industry central tendency, achieve the intended objectives of analysis (e.g., size control). Various issues related to financial analysis, such as spurious correlation due to a common denominator, the choice of an optimal size variable, and the treatment of outlier observations, are also examined.     

An empirical study of the usefulness of accounting ratios to describe levels of insolvency risk

This study aims to explore the usefulness of accounting ratios to describe levels of insolvency risk. Previous studies have used the statistical technique of discriminant analysis to derive models for preducting whether a firm will or will not fail. This study will use the same statistical technique but with three differences (a) the ratios to be used in the discriminant analysis are selected by a method which ensures that no arbitrary limit is placed on their number, (b) because the significance of accounting ratios can vary from industry to industry, four industries are separately analysed; manufacturing, retail, property and finance, (c) the statistical probabilities yielded by the analysis are used to measure a firm's current level of insolvency risk. The study is concluded by interpreting the characteristic patterns of insolvency risk which emerge; an analysis of the factors causing the differences in these patterns throws new light on the causes, symptoms, and remedies of financial distress     

Using client performance measures to identify pre-engagement factors associated with qualified audit reports in Greece

The purpose of this paper is to test the extent to which client (corporate) performance measures can be used to enhance the ability to discriminate between the choice of a qualified or unqualified (clean) audit report. Audit firms face the risk of losing the client if they issue a qualification. On the other hand, failing to qualify exposes the auditor to potential lawsuits and loss of reputation. We examined the financial statements, auditors' opinions, and financial statements notes for companies in Greece that received a qualified audit report and for those that received an unqualified audit report. We modeled the auditor's qualification using a multicriteria decision aid classification method (UTADIS—UTilités Additives Discriminates) and compared it with other multivariate statistical techniques such as discriminant and logit analysis. The qualification decision is explained by financial ratios and by nonfinancial information such as the client litigation. The developed models are accurate in classifying the total sample correctly with rates of almost 80%.     

Firm Size,Book-to-Market Ratio,and Security Returns: A Holdout Sample of Financial Firms

Fama and French (1992) document a significant relation between firm size, book-to-market ratios, and security returns for nonfinancial firms. Because of their initial interest in leverage as an explanatory variable for security returns, Fama and French exclude from their analysis financial firms, thus creating a natural holdout sample on which to test the robustness of their results. We document that the relation between firm size, book-to-market ratios, and security returns is similar for financial and nonfinancial firms. In addition, we present evidence that survivorship bias does not significantly affect the estimated size or book-to-market premiums in returns. Our results indicate data-snooping and selection biases do not explain the size and book-to-market patterns in returns.     

Proportionate Growth and the Theoretical Foundations of Financial Ratios

The article proposes a theoretical framework for understanding financial ratios, showing that the multiplicative character of the financial variables from which financial ratios are constructed is a necessary condition of valid ratio usage, not just an assumption supported by evidence. Also, by assuming that firm size is a measurable statistical effect, the article offers an informed reappraisal of the limitations of financial ratios, particularly the well–known limitation of proportionality. The article is divided into two parts, one where ratio components are viewed as deterministic vari– ables and the other where they are random. Such an approach allows the characteristics of ratios to be more easily understood before generalizing the relationship between ratio components to encompass randomness. In the second part, when variability introduced by firm size is treated as a random effect, it is shown that if the accounting variables Y and X used to calculate a financial ratio Y/X are exponential Brownian motion, and if continuous growth rates are equal and proportionate to firm size, this may lead to ratios which are asymmetric but which do not necessarily drift.     

Nonlinear effects of climate policy uncertainty and financial speculation on the global prices of oil and gas

With the acceleration of global energy transition and financialization, intense climate policy uncertainty and financial speculation have significant impacts on the global energy market. This paper uses TVP-VAR-SV models to analyze the nonlinear effects of climate policy uncertainty (CPU), financial speculation, economic activity, and US dollar exchange rate on global prices of crude oil and natural gas respectively, and then compare the time-varying response of oil prices and gas prices to six representative CPU peaks. The results show that responses of energy prices to various shocks have significant nonlinear effects: the time-varying effect of CPU on energy prices from positive to negative over time is significant, and financial speculation has the opposite effects on oil and gas prices. The effect from economic activity is mainly positive, while the effects of US dollar exchange are negative and stable. These results provide important implications for policymakers and investors dealing with high levels of climate policy uncertainty, financial speculation, and global economic activity.     

WHY HEDGE? SOME EVIDENCE FROM OIL AND GAS PRODUCERS

Despite the prevalence of corporate risk management, there are no widely accepted explanations for why companies hedge or how shareholders benefit from hedging. This article provides some evidence on these issues by reporting the results of a study of the risk management policies of 100 oil and gas producers from 1992 to 1994.
The first notable finding is the considerable variety of the hedging policies of the oil and gas producers. For example, in 1993 slightly more than half of the companies did not hedge, while a quarter of the firms in the sample hedged more than 28' of their production, and some firms hedged almost 100'. The second main finding was that the extent of hedging was related to a variety of factors, largely those related to financing costs. In particular, companies with higher leverage—and thus presumably facing greater difficulties in accessing the capital markets—tended to hedge a larger fraction of their output than firms with lower leverage ratios. This result is consistent with the idea that corporations manage risks to help ensure they have sufficient capital to finance their investment opportunities and to reduce the likelihood that low oil and gas prices will push them into financial distress. Under either of these interpretations, financial theory would suggest that corporate hedging increases shareholder value. Whether it actually does so is a matter for future research.     

Feedforward neural networks in the classification of financial information

Financial research has given rise to numerous studies in which, on the basis of the information provided by financial statements, companies are classified into different groups. An example is that of the classification of companies into those that are solvent and those that are insolvent. Linear discriminant analysis (LDA) and logistic regression have been the most commonly used statistical models in this type of work. One feedforward neural network, known as the multilayer perceptron (MLP), performs the same task as LDA and logistic regression which, a priori, makes it appropriate for the treatment of financial information. In this paper, a practical case based on data from Spanish companies, shows, in an empirical form, the strengths and weaknesses of feedforward neural networks. The desirability of carrying out an exploratory data analysis of the financial ratios in order to study their statistical properties, with the aim of achieving an appropriate model selection, is made clear.     

Factors Affecting the Probability of Bankruptcy: A Managerial Decision Based Approach

The majority of classification models developed have used a pool of financial ratios combined with statistical variable selection techniques to maximize the accuracy of the classifier constructed. Rather than follow this approach, this article seeks to provide an explicit economic basis for the selection of variables for inclusion in bankruptcy models. This search to develop an economic theory of bankruptcy augments the existing bankruptcy prediction literature. Variables which occur in bankruptcy probability expressions derived from the solution of a stochastic optimizing model of firm behaviour are 'proxied' by variables constructed from financial statement data. The random nature of the lifetime of a single firm provides the rationale for the use of duration or hazard-based statistical methods in the validation of the derived bankruptcy probability expressions. Results of the validation exercise confirm that the majority of variables included in the empirical hazard formulation behave in a way that is consistent with the model of the firm. The results highlight the need for developments in the measurement of earnings dispersion.     

Ratio Analysis Using Rank Transformation

This paper presents an alternate method for transforming financial ratios. Ratios are ranked and scaled into a uniform distribution with boundaries between 0 and 1. Conceptually, we suggest that this method solves a number of methodological problems associated with ratios, including constrained choice of regression models, ratio outliers, negative ratios, and non-normal distributions. Scaled ranks of financial ratios are also conceptually appealing because they appear to capture comparative ordinal data about cross-sectional relationships between firms.The study empirically tests scaled rank transformations by examining the association of the transformations with stock returns. Results show that models using relative ranked accounting ratios have more explanatory and predictive power than untransformed, log-transformed and square-root transformed ratios.     

Oil prices and accounting profits of oil and gas companies

This paper investigates the relationship between commodity prices of crude oil, capital structure, firm size and accounting measures of firm performance using a sample of oil and gas firms from 1990 to 2008. We employ estimates based on panel least squares, a fixed effects model and a random effects model. We also use generalized method of moments (GMM) estimators by Arellano and Bond (1991) and Blundell and Bond (1998, 2000). Our findings show that crude oil prices positively and significantly impact the performance of oil and gas firms in North America using accounting measures of performance. The recent financial crisis of 2007 and 2008 negatively influenced oil prices and the financial performance of oil and gas firms. On the other hand, the earlier global crises (Asian financial crisis and 9/11) did not have a significant impact on the return on equity of oil and gas companies. Our primary contribution to the literature is a comprehensive and econometric analysis of the relation between commodity prices and accounting measures of performance oil and gas companies.     

Data preprocessing and data parsimony in corporate failure forecast models: evidence from Australian materials industry

The present study, based on data for delisted and active corporations in the Australian materials industry, is an attempt to develop a systematic way of selecting corporate failure‐related features. We empirically tested the proposed procedure using three datasets. The first dataset contains 82 financial economic factors from the corporation's financial statement. The second dataset comprises 73 relevant financial ratios, which either directly or indirectly measure a corporation's propensity to fail, and are conciliated from the first dataset. The third dataset is a parsimonious dataset obtained from the application of combining a filter and a wrapper to preprocess the first dataset. The robustness of this preprocessed dataset is tested by comparing its performance with the first and second datasets in two statistical (logistic regression and naïve‐Bayes) and two machine learning (decision tree, neural network) classes of prediction models. Tests for prediction accuracies and reliabilities, using the computational (ROC curve, AUC) and the statistical (Cochran's Q statistic) criteria show that the third dataset outperforms the other two datasets in all four predicting models, achieving various accuracies ranges from 81 per cent to 84 per cent.     

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.     

Volatility spillovers between oil prices and the stock market under structural breaks

This paper employs univariate and bivariate GARCH models to examine the volatility of oil prices and US stock market prices incorporating structural breaks using daily data from July 1, 1996 to June 30, 2013. We endogenously detect structural breaks using an iterated algorithm and incorporate this information in GARCH models to correctly estimate the volatility dynamics. We find no volatility spillover between oil prices and US stock market when structural breaks in variance are ignored in the model. However, after accounting for structural breaks in the model, we find strong volatility spillover between the two markets. We compute optimal portfolio weights and dynamic risk minimizing hedge ratios to highlight the significance of our empirical results which underscores the serious consequences of ignoring these structural breaks. Our findings are consistent with the notion of cross-market hedging and sharing of common information by financial market participants in these markets.     

METHODOLOGICAL IMPLICATIONS OF NON-NORMALLY DISTRIBUTED FINANCIAL RATIOS: A COMMENT

This comment argues that proposals to replace financial ratio analysis with regression analysis of the separate ratio components are unnecessary and misguided. Financial ratios are used to predict other variables and not to predict their own components. These ratios also help deal with the size scale problem inherent in regular accounting data, but regression error terms would suffer from the same size effect as the regular data. The actual statistical distribution of financial ratios is an open, important question, but that question in itself does not call for an abandonment of fmancial ratios.