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Purpose: The purpose of this study is to investigate the value relevance of goodwill and its additional aspects during a long-term period in Greece. Furthermore, by implementing two of the most popular value relevance models, the Ohlson's price and Easton and Harris' return model, this study examines the impact of goodwill on Greek stock prices from 2007 to 2018, a period of 12 years in which International Financial Reporting Standards (IFRS) are applied. Furthermore, this study analyzes how goodwill's value relevance changes as it ages and during the Greek debt crisis. Design/methodology/approach: In order to test the value relevance of goodwill we implemented two of the most popular value relevance models, Ohlson's price and Easton and Harris' return model. Our sample consists of non-financial listed Greek companies that reported positive goodwill accounting balances on their financial statements during the financial period from 2007 to 2018. Finally, we applied fixed-effects regression model to all equations. Findings: The results provide evidence that the year-end goodwill accounting balance is value relevant, and that the debt crisis has improved goodwill's information content. Finally, the empirical findings suggest that only current year acquired goodwill is value relevant compared to older goodwill, and therefore, goodwill's impact on stock prices is decreasing as it ages. Research limitations/implications: A noteworthy limitation of this study is that it focuses on a specific code-law country Greece, which is a relatively small economy compared to the whole Eurozone. This research contributes to the research literature as it confirms other research findings in the European context and specifically that goodwill based on IFRS is value relevant to financial statement users. Additionally, it investigates for the first time how goodwill was affected by the Greek debt crisis. Finally, it contributes to other researcher's debate concerning the duration of goodwill's value relevance in a code law environment such as Greece. Practical implications: Financial analysts and institutions are provided with more assurance about goodwill's financial reporting quality to be embedded in the financial evaluation process of corporates. As this research confirms that goodwill should be regarded as an asset, companies should obtain better financial ratings from financial institutions and investors and thus will have better access to equity and debt funding. Originality/value: We investigate the value relevance of goodwill in Greece during a long-term period of 12 years. Additionally, our study examines the impact of the Greek debt crisis on the information content of goodwill accounting balances and the period during which accumulated goodwill balances and within-year acquired goodwill maintain its value relevance. Our research could assist accounting standard setters such as the International Accounting Standard Board to evaluate the quality of specific standards such as IFRS 3 "Business Combination" and IAS 38 "Impairment of Assets." [ABSTRACT FROM AUTHOR]

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: 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. Practical implications: 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. Originality/value: 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. [ABSTRACT FROM AUTHOR]