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The purpose of this paper is to, first, analyse to what extent the default probability based on structural models provides additional information and that accounting ratios do not contemplate. Second, to design hybrid models by including the default probability from structural models as explanatory variable, in addition to accounting ratios, in order to evaluate the differences in the accuracy of default predictions using an accounting‐based model and a hybrid model.

The authors calculated the scores from the accounting models annually during the period from 2003 to 2007 and estimated several structural models.

The results show that the market information obtained from the structural models includes additional information not reflected in the accounting information. Also, it can be concluded that including default probability from structural models as an explanatory variable allows the out‐sample predictive capacity of accounting‐based models to be improved.

The study highlights the importance of combining a structural model with an accounting model rather than expending energy on determining which of the two provides a greater predictive capacity. In fact, recent literature demonstrates no superiority of one approach over the other because both approaches capture different aspects related to the risk of bankruptcy in companies and they should be combined to improve credit risk management.

This study expands on the existing literature on the probability of business failure in the real estate sector. The authors present a comparative analysis of the accuracy of default predictions using accounting‐based models and hybrid models which will consider the default probability implicit in market information.

This study sheds light on the relation between intellectual capital and firm performance. The study argues that traditional performance measurement based on accounting is still able to explore the relation between intellectual capital and performance.

The study was conducted at 198 firms from two Gulf Cooperation Council countries: Kingdom of Saudi Arabia and Kingdom of Bahrain for the period 2014–2016. To measure intellectual capital, the value added intellectual coefficient model was adopted along with two measures of performance: accounting-based performance which is return on assets and market-based performance which is Tobin’s Q, in addition to the Random-Effects Regression.

Study findings came up with evidences that support the relationship between intellectual capital and accounting-based performance, but negates any relation between intellectual capital and market-based performance. The findings also revealed different results, between Saudi Arabia’s and those of Bahrain.

The study contributes to the debate on the validity of relating intellectual capital to the traditional accounting-based performance.

This paper aims to examine the impact on executive compensation (both cash and in total) of regulation, size of sales and number of employees, and nature of the business in terms of new‐economy vs traditional.

This study uses the ExecuComp database as the information source. Regression analysis is used to test hypotheses that focus on firm size in terms of sales, market and accounting returns, and the number of firm employees. The sample consists of 455 US firms from 25 industries, and covers the period 1996‐2002.

Firm size and market‐based return are the most significant explanatory variables in affecting executive compensation. More limited support was found for accounting‐based returns, as was changes in the number of employees.

Findings of this study may be limited by the temporal context. Around the turn of this century may have been an unusual time in America's corporate history. The economic outlook of the late 1990s may be fundamentally different from the one facing firms now or in the future. Consequently, future research will be needed to determine to what extent these results can be generalized to periods of different economic prospects.

This study examines the impact of firms' operational characteristic on Chief Executive Officer (CEO) compensation.

The paper aims to find out the information content of performance measures from accounting and value-based measures that best explain the total shareholder return.

To achieve this aim, static and dynamic panel data regression analysis is applied to the sample of 56 Indian companies taken from the Nifty Midcap 100 Index, between 2012 and 2019.

It is found that accounting-based measures have more relative information content in predicting total shareholder return as compared to value-based measures. Economic value added (EVA) and cash value added (CVA) do not add to the information content provided by accounting-based measures. A combination of accounting-based measures and value-added intellectual coefficient (VAIC) adds marginally to the information content provided by accounting-based measures in explaining the total shareholder return. Dynamic panel regression analysis shows that return on assets (ROA), return on capital employed (ROCE), return on equity (ROE) and EVA have a significant impact on total shareholder return.

In this study, along with EVA, other measures from value-based measures, i.e. CVA are empirically tested to explain the total shareholder return. Intellectual capital efficiency computed by VAIC is also empirically tested along with accounting-based measures, EVA, CVA and market value added (MVA). To bring robustness to findings, data are tested by using dynamic panel regression analysis.

This paper aims to investigate the relevance of two groups of valuations models as follows: the accounting models based on the residual income (RIM) and the standard market model, on equity price, return and volatility relevance.

The models are tested on companies traded on Palestine exchange from 2009 to 2018, using panel regression analysis. Two-price and two-return models derived from RIM to compare with the market model and four volatility models.

The standard RIM outperformed other models in equity price modeling. The dividend discount model (DDM) outperformed the rest of the models in terms of return estimation. However, the authors find that the market model can explain equity variance better than RIM and DDM models.

For investors, market beta does not necessarily capture all relevant factors of value and traditional financial statements are still important in providing relevant information and different models are used for different values perspectives (price, return and volatility).

Previous studies focus on comparing the price and return relevance of accounting-based models (RIM and cash flow models). Three aspects differentiate this paper and contribute to its originality, namely, the uniqueness of the context, incorporating the market model into the picture along with the accounting-based models and adding Volatility dimensions of relevance.

This study aims to examine how geographic diversification affects firms’ risk by introducing the franchising strategy as a moderator.

The panel regression analysis was conducted with a sample of US restaurant firms. Specifically, a two-way random (or fixed) effects model clustered by firm was used to test hypotheses.

Findings show that geographic diversification does not significantly affect restaurant firms’ risk. However, franchising aggravates the negative effect of geographic diversification on restaurant firms’ risk, which contradicts the traditional theories of franchising.

The results are expected to contribute to the diversification literature in the hospitality management by providing in-depth evidence for the effects of geographic diversification strategies on firms’ risk. Specifically, the study provides relevant theories for explaining the effect of geographic diversification in the restaurant context by examining franchising, a prominent strategy in the restaurant industry.

The results encourage restaurant firms to improve their managerial capability to react to changes in a geographically wider scope of markets and develop franchising contracts specifically to prevent misbehavior and moral hazard on the part of franchisees.

Considering the lack of research on the effect of geographic diversification on restaurant firms’ risk, this study examines not only the link between geographic diversification and firms’ risk but also a contingent factor, franchising.

The purpose of this paper is to determine whether the original and the revised versions of the existing prediction models are the best tools for assessing the going concern assumption of a firm in the creditor-oriented regime.

The analysis begins from estimating the classification accuracy of the original versions of the bankruptcy, going concern and liquidation prediction models. At the second step, the revised versions of the aforesaid existing prediction models are developed. At the third step, the accounting-based going concern prediction model is proposed by using multiple discriminant analysis for the creditor-oriented regime. The sample contains the financial ratios of manufacturing firms for the period 1997–2014.

The finding indicates that the five discriminatory variables, which belong to “income statement” and “statement of financial position,” of the proposed model are not only useful for evaluating the going concern assumption of a firm, but also give aid for evaluating the financial fraud risk of a firm as compared to the original and revised versions of the prediction models that are developed for the debtor-oriented regime.

The external validity of the proposed prediction model can be tested on the large data sets of the countries where the liquidation provisions are a part of their local corporate law.

The proposed accounting prediction model will be helpful for the internal and external auditors in order to determine the going concern assumption at planning, performing and evaluation stages.

The proposed accounting-based going concern prediction model is based on liquidated firms.

Financial ratios are routinely used as predictors in modelling tasks where accounting information is required. The purpose of this paper is to discuss such use, showing how to improve the effectiveness of ratio-based models.

First, the paper exposes the inadequacies of ratios when used as multivariate predictors. It then develops a theoretical foundation and methodology to build accounting-based models. Experiments then verify that this methodology outperforms the conventional methodology.

From plausible assumptions about the cross-sectional behaviour of accounting data, the paper shows that the effect of size, which ratios remove, can also be removed by modelling algorithms, which facilitates the discovery of meaningful predictors and leads to markedly more effective models.

The paper covers cross-sectional modelling only, accounting identities and other interactions between line items are ignored, the methodology is especially appropriate in tasks where the effectiveness of the model is seen as a valued quality.

The need to select ratios among many alternatives is avoided, models become more accurate and robust, less biased and less likely to generate missing values, model construction is less arbitrary.

The paper provides a solid foundation for accounting-based modelling, by developing a whole new methodology that can end the uncritical use of modelling remedies currently prevailing and release the full relevance of accounting information when utilised to support investments and other value-bearing decisions.

This chapter aims at examining financial distress issue by designing a comprehensive model to explain and predict financial distress in Egypt. This comprehensive model incorporates accounting ratios, market-based ratios and macroeconomic ratios. The sample of the existing research includes all the listed firms in two main sectors: basic resources and chemicals. Using logistic regression model, the results showed that adding market ratios and macroeconomic ratios enhances the predictability of the model and accounting information are not sufficient to explain financial distress.

We extend the recent literature concerning accounting based valuation models to investigate financial firms from six European countries with substantial financial sectors: France, Germany, Italy, Netherlands, Switzerland and the UK. Not only are these crucial industries worthy of study in their own right, but unusual accounting practices, and inter‐country differences in those accounting practices, provide valuable insights into the accounting‐value relationship. Our sample consists of 7,714 financial firm/years observations from 1,140 companies drawn from 1989–2000. Sub‐samples include 1,309 firm/years for banks, 650 for insurance companies, 1,705 for real estate firms, and 3,239 for investment companies. In most countries we find that the valuation models work as well or better in explaining cross‐sectional variations in the market‐to‐book ratio for financial firms as they do for industrial and commercial firms in the same countries, although Switzerland is an exception to this generalization. As expected, the results are sensitive to industrial differences, accounting regulation and accounting practices. In particular, marking assets to market value reduces the relevance of earnings figures and increases that of equity.