An emerging hybrid mechanism for information disclosure forecasting

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题名:	An emerging hybrid mechanism for information disclosure forecasting
作者:	Hsu, YS (Hsu, Yu-Shan) Lin, SJ (Lin, Sin-Jin)
贡献者:	會計系
关键词:	Ensemble learning Manifold learning Disclosure Rough set theory Forecasting
日期:	2016-12
上传时间:	2017-04-11 12:39:53 (UTC+8)
摘要:	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 preprocessing 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.
關聯:	INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 卷: 7期: 6 頁碼: 943-952
显示于类别:	[會計學系暨研究所 ] 期刊論文

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