Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

01-12-2016 | Original Article | Issue 6/2016

International Journal of Machine Learning and Cybernetics 6/2016

An emerging hybrid mechanism for information disclosure forecasting

Journal:
International Journal of Machine Learning and Cybernetics > Issue 6/2016

Abstract

Corporate governance mechanisms ensure that investors get a fair return on their investment. A well-established governance mechanism reduces the information asymmetry and agency cost between a firm’s management and stakeholders, but decision makers find it difficult to assess the corporate governance status of publicly-listed firms before the annual official announcement the following year. This study proposes a hybrid ensemble learning forecasting mechanism (HELM), whose single-component candidates from the extreme learning machine (ELM) algorithm with dissimilar ensemble strategies (that is, data diversity, parameter diversity, kernel diversity, and pre-processing diversity) form one initial dataset. We implement locally linear embedding into the proposed mechanism to handle the dimensionality task and then utilize the weighted voting taken from the base components’ cross-validation performance on a training dataset as the integration mechanism. Experimental results show that the proposed HELM significantly outperforms the other classifiers, but its superior performance under many real-life application domains comes with a critical drawback: it is incapable of providing an explanation for the underlying reasoning mechanisms. Thus, this study advances the utilized rough set theory with its explanation capability to extract the inherent knowledge from the ensemble mechanism (HELM). The informative rules can be used as a guideline for decision makers to make a reliable judgment under turbulent financial markets.

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article

Other articles of this Issue 6/2016

International Journal of Machine Learning and Cybernetics 6/2016 Go to the issue

Original Article

Linguistic rough sets