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An emerging hybrid mechanism for information disclosure forecasting

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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.

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Acknowledgments

The author thanks the Editor-in-Chief Dr. Xi-Zhao Wang and anonymous reviewers for their valuable suggestions. The author would like to thanks Ministry of Science and Technology of the Republic of China, Taiwan for financially supporting this work under Contract No. 103-2410-H-034-029.

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Authors and Affiliations

  1. Department of Administration, Kaohsiung Municipal Ta-Tung Hospital, 68, Jhonghua 3rd Rd., Cianjin District, Kaohsiung City, 80145, Taiwan, ROC

    Yu-Shan Hsu

  2. Department of Accounting, Chinese Culture University, 55, Hwa-Kang Rd., Yang-Ming-Shan, Taipei, 11114, Taiwan, ROC

    Sin-Jin Lin

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  1. Yu-Shan Hsu
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Correspondence to Sin-Jin Lin.

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Hsu, YS., Lin, SJ. An emerging hybrid mechanism for information disclosure forecasting. Int. J. Mach. Learn. & Cyber. 7, 943–952 (2016). https://doi.org/10.1007/s13042-014-0295-4

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