An emerging hybrid mechanism for information disclosure forecasting

Abstract

Corporate governance mechanisms ensure that investors get a fair return on their investment. A wellestablished 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' crossvalidation 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.

Hybrid Kernelized Fuzzy Clustering and Multiple Attributes Decision Analysis for Corporate Risk Management

“…The fundamental concept is to generate artificially labeled samples where the target class of training data is replaced by the class predicted by KTE. The manipulated dataset is then fed into another artificial intelligence technique with explanation ability (i.e., RST), which learns what KTE has learned [6,25].…”

Identifying Key Factors for Adopting Artificial Intelligence-Enabled Auditing Techniques by Joint Utilization of Fuzzy-Rough Set Theory and MRDM Technique

“…Furthermore, AI technology can be used to analyze big data, by collecting and summarizing data from multiple sources, providing auditors with sufficient evidence and information that can be included in judgments, supplementing their judgment capability, and making more informed decisions to provide clients with higher levels of assurance. More importantly, AI-enabled auditing techniques are prominent in data analysis, such as data extraction, comparison, and validation (Hsu & Lin, 2016;Lin, 2017), which means that AI-based technology can extract textual information from complex electronic documents (Deloitte, 2015). Thus, auditors can spend more time in areas that require higher levels of judgment and present greater insights to businesses.…”