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The purpose of this paper is to introduce new economic psychology theories that can explain fraud, misconduct and non-compliance that may arise from the implementation and enforcement of accounting standards codification (ASC) 805/350, international financial reporting standards (IFRS) 3R and IAS-38.

The approach is entirely theoretical. The paper analyzes existing theories about real options and enforcement of regulations/statutes, and introduces new psychological biases that can arise.

The real options approach suggested for handling the enforcement of goodwill/intangibles regulations is not effective.

The research is limited to international accounting standards board (IASB)/IFRS and financial accounting standards board (FASB) accounting standards.

The critiques and theories developed in the paper can be used in the analysis of selection of disputes for litigation, anti-corruption programs and regulation of transactions that are susceptible to fraud.

The aim of this study is to evaluate of the predictive ability of goodwill and other intangible assets on forecasting corporate profitability. Subsequently, this study compares the efficiency of deep learning model to that of other machine learning models such as random forest (RF) and support vector machine (SVM) as well as traditional statistical methods such as the linear regression model.

Studies confirm that goodwill and intangibles are valuable assets that give companies a competitive advantage to increase profitability and shareholders’ returns. Thus, by using as sample Greek-listed financial data, this study investigates whether or not the inclusion of goodwill and intangible assets as input variables in this modified deep learning models contribute to the corporate profitability prediction accuracy. Subsequently, this study compares the modified long-short-term model with other machine learning models such as SVMs and RF as well as the traditional panel regression model.

The findings of this paper confirm that goodwill and intangible assets clearly improve the performance of a deep learning corporate profitability prediction model. Furthermore, this study provides evidence that the modified long short-term memory model outperforms other machine learning models such as SVMs and RF , as well as traditional statistical panel regression model, in predicting corporate profitability.

Limitation of this study includes the relatively small amount of data available. Furthermore, the aim is to challenge the authors’ modified long short-term memory by using listed corporate data of Greece, a code-law country that suffered severely during the recent fiscal crisis. However, this study proposes that future research may apply deep learning corporate profitability models on a bigger pool of data such as STOXX Europe 600 companies.

Subsequently, the authors believe that their paper is of interest to different professional groups, such as financial analysts and banks, which the authors’ paper can support in their corporate profitability evaluation procedure. Furthermore, as well as shareholders are concerned, this paper could be of benefit in forecasting management’s potential to create future returns. Finally, management may incorporate this model in the evaluation process of potential acquisitions of other companies.

The contributions of this work can be summarized in the following aspects. This study provides evidence that by including goodwill and other intangible assets in the authors’ input portfolio, prediction errors represented by root mean squared error are reduced. A modified long short-term memory model is proposed to predict the numerical value of the profitability (or the profitability ratio) in contrast to other studies which deal with trend predictions, i.e. the binomial output result of positive or negative earnings. Finally, posing an extra challenge to the authors’ deep learning model, the authors’ used financial statements according to International Financial Reporting Standard data of listed companies in Greece, a code-law country that suffered during the recent fiscal debt crisis, heavily influenced by tax legislation and characterized by its lower investors’ protection compared to common-law countries.

Considers that the relationship between a property′s trading potential and the tenant′s ability to pay rent is the basis of one of the five recognised methods of valuation: the profits (or accounts) method. Discusses the basic concept behind the methodology and investigates the circumstances under which surveyors currently use profits within their valuations. Concludes that more discussion between valuers and their clients on how they arrive at their valuations and the definitions of value that they use would be beneficial.

In 2009 and 2010, the Financial Accounting Standards Board (FASB) adopted new accounting standards for nonprofit mergers and acquisitions. The new accounting standards are an example of the constitutive role accounting can play in how people think about economic events, since the FASB defined a new concept (the “inherent contribution”) and required valuation of intangible assets that were often previously unrecognized.

The FASB’s stated goals included minimizing “pooling” accounting and maximizing transparency regarding fair value information, acquired identifiable intangible assets, and the relation between consideration paid and the fair values of identifiable assets acquired. The FASB expected many combinations would involve little or no consideration. It also expressed concern that some organizations would undervalue assets acquired, especially intangible assets.

For a sample of 2012–2017 nonprofit hospital combinations, we find general agreement with the FASB’s expectations. Almost all combinations were accounted for as acquisitions, not mergers, even though there was frequently no consideration paid. More acquirers recorded “inherent contributions” than goodwill, because the net fair value of the acquired hospital’s identifiable assets exceeded the consideration paid. Acquirers ascribed value to assets, such as intangible assets, that would have gone unreported under the prior accounting rules, although lower levels of intangible assets were recognized in nonprofit business combinations, relative to total non-goodwill assets acquired, than in public companies’ acquisitions.

This study empirically investigates the relationship between goodwill assets and capital structure (i.e. debt ratio) of firms and the moderating effect of financial market development on the relationship between goodwill assets and capital structure.

This research applied a quantitative method. The article collects large samples of listed firms from 23 developing and nine developed countries and applied the panel data techniques. This research used firm-level data from the DataStream database for both developed and developing countries. The study uses 4,912 firm-level data from 23 developing countries and 4,303 firm-level data from nine developed countries.

The findings reveal a significant positive relationship between goodwill assets and capital structure in developing countries, but goodwill assets have a significant negative relationship with capital structure in developed countries. Moreover, financial market development positively moderates the relationship between goodwill assets and the capital structure of firms in developing countries. The results inform firm managers that goodwill assets serve as additional collateral to secure debt financing. Moreover, policymakers should formulate a debt market policy that recognizes goodwill assets as additional collateral for the purpose of obtaining debt capital.

The study has several implications. First, goodwill assets are identified as a factor of capital structure in this study. Fixed assets have been identified as one of the drivers of capital structure in previous research, although goodwill assets are seldom included. Second, this article shows that along with demand-side determinants, supply-side determinants also play an important role in terms of the firms' choice about the capital structure. Therefore, firms should take both the demand-side and supply-side factors into consideration when sourcing for external financing (i.e. debt capital).

The study considered goodwill as a component of capital structure. The study analysis includes a large sample of enterprises, including 4,912 big firms from 23 developing countries and 4,303 large firms from nine industrialized or developed countries, which adds to the current capital structure information. Furthermore, a large sample size increases the results' robustness and generalizability.

This paper summarizes a study, undertaken by Arthur Andersen’s Intellectual Property Group in London, to consider the economic and financial issues, principally as they affect the valuation of intellectual property and its suitability as security. The study encompasses a review of available literature, interviews and discussions, and an analysis of the results of a questionnaire which was distributed to owners and managers of intellectual property. Views were canvassed across industries, of both borrowers and lenders, and also of lawyers and other advisers experienced in the transactions involving intellectual property.

The purpose of this paper is to investigate whether International Financial Reporting Standards (IFRS)-based data improve bankruptcy prediction over Australian Generally Accepted Accounting Principles (AGAAP)-based data. In doing so, this paper focuses on intangibles because conservative accounting rules for intangibles under IFRS required managers to write off substantial amounts of intangibles previously capitalized and revalued upwards under AGAAP. The focus on intangibles is also motivated by empirical evidence that financially distressed firms are more likely to voluntarily capitalize and make upward revaluations of intangibles compared with healthy firms.

This paper analyses a sample of 46 bankrupt firms and 46 non-bankrupt (healthy) firms using a matched-pair design over the period 1991 to 2004. The authors match control firms on fiscal year, size (total assets), Global Industry Classification Standard-based industry membership and principal activities. Using Altman’s (1968) model, this paper compares the bankruptcy prediction results between bankrupt and non-bankrupt firms for up to five years before bankruptcy. In the tests, the authors use financial statements as reported under AGAAP and two IFRS-based data sets. The IFRS-based datasets are created by considering the adjustments on the AGAAP data required to implement the requirements of IAS 38, IFRS 3 and IAS 36.

This paper finds that, under IFRS, Altman’s (1968) model consistently predicts bankruptcy for bankrupt firms more accurately than under AGAAP for all of the five years prior to bankruptcy. This greater prediction accuracy emanates from smaller values of the inputs to Altman’s model due to conservative accounting rules for intangibles under IFRS. However, this greater accuracy in bankruptcy prediction comes with larger Type II errors for healthy firms. Overall, the results provide evidence that the switch from AGAAP to IFRS improves the quality of information contained in the financial statements for predicting bankruptcy.

Small sample size and having data available over the required period may limit generalizability of findings.

Although bankruptcy prediction is one of the primary uses of accounting information, the burgeoning literature on the benefits of IFRS adoption has so far neglected the role of IFRS data in bankruptcy prediction. Thus, this paper documents a new benefit of IFRS adoption. In this paper, the authors demonstrate how the restrictions on the ability to capitalize and revalue intangibles enhance the quality of information used to predict bankruptcy. These results provide evidence to international standard setters of what they can expect if their efforts to remove non-restrictive accounting practices for intangibles are abandoned.