Can intangible assets predict future performance? A deep learning approach
DOI | https://doi.org/10.1108/IJAIM-06-2021-0124 |
Published date | 27 October 2021 |
Date | 27 October 2021 |
Pages | 61-72 |
Subject Matter | Accounting & finance,Accounting/accountancy,Accounting methods/systems |
Author | Eleftherios Pechlivanidis,Dimitrios Ginoglou,Panagiotis Barmpoutis |
Can intangible assets predict
future performance? A deep
learning approach
Pechlivanidis Eleftherios and Ginoglou Dimitrios
Department of Accounting and Finance,
University of Macedonia, Thessaloniki, Greece, and
Barmpoutis Panagiotis
Department of Computer Science, University College London, London, UK
Abstract
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 regressionmodel.
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-listedfinancial 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
profitabilityprediction accuracy. Subsequently, this study comparesthe modified long-short-term model with
other machinelearning models such as SVMs and RF as well as the traditionalpanel 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
evidencethat the modified long short-termmemory model outperforms othermachine learning models suchas
SVMs andRF , as well as traditionalstatistical panel regressionmodel, in predictingcorporate profitability.
Research limitations/implications –Limitation of this study includes the relativelysmall amount of
data available. Furthermore, the aim is to challengethe authors’modified long short-termmemory by using
listed corporate data of Greece, a code-law country that suffered severely during the recent fiscal crisis.
However, this studyproposes that future research may apply deep learningcorporate profitability models on
a bigger pool of data such as STOXX Europe600 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’spotential to create future returns. Finally,management may
incorporatethis model in the evaluation processof potential acquisitions of othercompanies.
Originality/value –The contributions of this work can be summarized in the following aspects. This
study providesevidence that by including goodwill and other intangibleassets 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 valueof the profitability (or the profitability ratio) in contrastto
other studieswhich deal with trend predictions, i.e. the binomial outputresult of positive or negative earnings.
Finally, posing an extra challengeto the authors’deep learning model, the authors’usedfinancial 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
characterizedby its lower investors’protection compared to common-lawcountries.
Keywords Corporate profitability, Goodwill, Intangible assets, Deep learning model,
Long short-term memory networks
Paper type Research paper
Intangible
assets
61
Received15 June 2021
Revised7 September 2021
Accepted12 October 2021
InternationalJournal of
Accounting& Information
Management
Vol.30 No. 1, 2022
pp. 61-72
© Emerald Publishing Limited
1834-7649
DOI 10.1108/IJAIM-06-2021-0124
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